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Senser Extends AIOps Reach to Manage SLOs and SLAs

Senser is extending the reach of its artificial intelligence for IT operations (AIOps) platform to now include an ability to define and maintain service level agreements (SLAs) and service level objectives (SLOs).

SLOs are a set of internal performance goals that require access to telemetry data from service level indicators (SLIs), while an SLA is a formal commitment to maintaining specific levels of service.

Senser CEO Amir Krayden said the company’s AIOps platform collects data from SLIs and then applies predictive AI models to enable IT teams to achieve SLOs and SLAs. The Senser AIOps platform leverages extended Berkeley Packet Filter (eBPF) and graph technology to gain visibility into the entire IT environment versus requiring IT teams to deploy agent software. Machine learning algorithms are then used to aggregate and analyze that data to define thresholds for predicting performance in addition to recommending benchmarks for tracking SLOs and SLAs.

That approach provides a single source of truth for identifying the actual level of service being provided based on a topology of the infrastructure, network, applications and application programming interfaces (APIs) that makes it possible to identify the root cause of issues and the potential impact of an outage for degradation of performance.

IT teams have been attempting to achieve and maintain SLAs and SLOs for decades, but given all the dependencies that exist in a distributed computing environment, it’s difficult to achieve that goal. Senser is making a case for applying AI within the context of a platform for automating the management of IT to define and maintain SLOs and SLAs to make it possible to consistently manage SLAs and SLOs in a way that reduces the level of cognitive load that would otherwise be required. Senser is also working toward adding generative AI capabilities to provide summaries that explain what IT events have occurred.

Collectively, the goal is to provide IT teams with a more efficient holistic approach to monitoring and observability that legacy platforms are not going to be able to achieve and maintain, said Krayden.

At the core of that capability is eBPF, a technology that allows software to run within a sandbox in the Linux microkernel. That capability enables networking, storage and observability software to scale at much higher levels of throughput because they are no longer running in user space. That’s especially critical for any application that needs to dynamically process massive amounts of data in near-real-time.

As the number of organizations running the latest versions of Linux continues to increase, more hands-on experience with eBPF will be gained. IT teams may not need to concern themselves with what is occurring in the microkernel of the operating systems, but they do need to understand how eBPF ultimately reduces the total cost of running IT at scale.

Ultimately, the goal is to reduce the current level of complexity that today makes effectively managing highly distributed computing environments all but impossible for IT teams to manually maintain in an era where the pace at which applications are being built and deployed only continues to accelerate.

December 21, 2023
Digibee Adds AI Tool to Accelerate Migration to Integration Platform

Digibee is leveraging artificial intelligence (AI) to make it simpler to migrate to its integrated platform-as-a-service (iPaaS) environment by converting code into a JavaScript Object Notation (JSON) format.

A translator tool created by Digibee invokes large language models (LLM) to convert that code into a JSON file that can then run on the Digibee Integration Platform.

Initially, that capability is being made available to facilitate migrations from the Mulesoft integration platform made available by Salesforce, with support for other legacy integration platforms planned for 2024.

Digibee CTO Peter Kreslins Junior said this approach reduced the level of time and effort required to switch platforms by using generative AI to map and refactor legacy integration code. That refactoring process doesn’t completely automate the reverse engineering of workflows, but it does substantially reduce the total cost of switching platforms by, for example, automating data migration and simplifying the transfer of configurations, he noted.

Generative AI also eliminates the need to rely on documentation or maps that either may be limited or non-existent, added Kreslins. Instead, all the integration patterns used can now be automatically discovered by an LLM, he added.

IT teams will still need to vet and test the code created by the translator tool, but overall, the speed at which any migration effort is made should be accelerated, thanks to the rise of generative AI, in 2024. Many organizations are locked into various platforms today simply because the effort required to rework the code that has been created to extend them has required either too much effort on their part or the contracting of expensive consultants. A migration project that might have taken a year or more can now be accomplished in a few days, said Kreslins. The overall goal is to reduce the current level of pain IT teams experience when migrating between platforms, he added.

It’s not clear if the rise of generative AI platforms that make converting code into other formats and languages simpler will drive a wave of migrations, but there are many organizations relying on legacy platforms that are not nearly as easy to use as more modern alternatives. As the time and effort required to make those transitions is reduced, more IT teams will be, at the very least, more open to considering their platform options. The providers of those legacy platforms will naturally have to invest in new capabilities much faster to discourage customers from migrating to another platform.

In the meantime, the amount of code that will need to be integrated is about to exponentially increase as developers rely more on LLMs to generate code. The downstream implications of that increased pace of application development for DevOps teams are profound. It’s not clear to what degree those teams can increase the level of scale at which they today manage application deployments.

The one thing that is clear is there is no going back because, so far as application development and deployment are concerned, the proverbial AI genie is out of the bottle.

December 20, 2023
Hybrid Cloud in 2024: Trends and Predictions

Hybrid cloud environments are becoming increasingly pivotal for DevOps teams, primarily due to their ability to bridge the gap between the rapid deployment of public cloud resources and the security and control of private cloud infrastructure. This blending of environments provides DevOps teams with flexibility and scalability, which is crucial for operating continuous integration and continuous deployment (CI/CD) in large enterprises.

In a hybrid cloud setup, DevOps teams can swiftly deploy and test new applications in a cost-effective and scalable public cloud environment. Once the applications are ready for production, they can be seamlessly transitioned to a private cloud or on-premises environment, where they benefit from enhanced security and control. This allows DevOps teams to optimize their workflows, reduce time to market, and respond more effectively to business needs.

The hybrid cloud also enables a more collaborative and agile working environment. DevOps teams can leverage various tools and platforms available in the public cloud while ensuring that sensitive data and critical applications are securely managed in the private cloud. This both enhances operational efficiency and improves compliance with data governance and regulatory requirements. These benefits will make hybrid cloud an indispensable asset for DevOps teams in 2024 and beyond.

What is a Hybrid Cloud?

Let’s start with a brief definition. A hybrid cloud is a computing environment that combines a public cloud and a private cloud by allowing data and applications to be shared between them. This combination allows businesses to enjoy the best of both worlds, leveraging the scalability and cost efficiencies of public cloud platforms while enjoying the security and control offered by private cloud infrastructure.

This hybrid cloud model provides businesses with more deployment options and greater flexibility to scale computing resources. It also helps in reducing capital expenditure and improving security by allowing sensitive data to be stored on a private cloud. A hybrid cloud strategy enables businesses to allocate resources efficiently, keeping less critical applications on the public cloud, while mission-critical applications run on the private cloud.

Current State of Hybrid Cloud Computing

The hybrid cloud market is in a state of rapid growth and evolution. As businesses increasingly recognize the benefits of a hybrid approach, the adoption of hybrid cloud strategies is on the rise.

In a world where digital transformation is the norm, hybrid cloud computing has emerged as a key enabler. It offers the flexibility needed to adapt quickly to changing business conditions, the scalability to handle sudden increases in workload, and the security to protect sensitive data. These benefits have made hybrid cloud a cornerstone of digital transformation strategies.

However, the journey to hybrid cloud is not without its challenges. Businesses are grappling with issues related to data security, compliance, integration, and management. There are also complexities related to workload portability, interoperability, and the need for skilled resources. Despite these challenges, the adoption of hybrid cloud continues to grow as businesses seek to leverage its benefits to drive innovation and competitive advantage.

Emerging Trends in Hybrid Cloud for 2024

Increased Adoption Across Industries

Many industries, including the banking and finance sector, healthcare, retail, manufacturing, and even the government sector, are seeing increased adoption of hybrid cloud strategies. This trend is driven by the need for greater agility and flexibility, better cost management, and improved security and compliance.

In the healthcare sector, for example, hybrid cloud is enabling the integration of disparate systems, facilitating the sharing of patient data, and supporting telehealth initiatives. In the banking sector, it is helping to manage risk, improve customer experiences, and drive innovation. In retail, hybrid cloud is supporting omnichannel strategies, personalizing customer experiences, and improving supply chain efficiency.

Advancements in Cloud Technology

The hybrid cloud landscape in 2024 will be shaped by advancements in cloud technologies. Technologies like containers and Kubernetes are simplifying the deployment and management of applications, making it easier for businesses to adopt a hybrid cloud strategy. The rise of serverless computing is also contributing to the growth of hybrid cloud by eliminating the need to manage servers and allowing developers to focus on writing code.

Furthermore, advancements in networking technologies are making it easier to connect and integrate public and private clouds. The emergence of 5G is expected to further accelerate the adoption of hybrid cloud by providing the high-speed connectivity needed to support real-time applications.

Integration of AI and Machine Learning

The integration of artificial intelligence (AI) and machine learning (ML) into hybrid cloud environments is a trend that is rapidly gaining momentum. One key aspect of this integration is the ability to use AI for better resource management within the hybrid cloud. AI algorithms can analyze patterns in data usage and application performance, allowing for more efficient allocation of resources across public and private cloud environments.

Furthermore, AI and ML are also facilitating better data management and analytics. Cloud-based services are making AI/ML more accessible to businesses of all sizes. By integrating these technologies, businesses can unlock insights from vast amounts of data stored across different cloud environments.

Predictions for Hybrid Cloud in 2024

Growth in Edge Computing Integration

By 2024, the integration of edge computing with hybrid cloud architectures is expected to grow significantly. Edge computing brings data processing closer to the location where it is needed, optimizing response times and saving bandwidth.

In a hybrid cloud context, this means that organizations can process critical data at the edge, in real-time, while still leveraging the cloud for less time-sensitive tasks. This integration is particularly beneficial for industries like manufacturing and autonomous vehicles, where real-time data processing is crucial. The combination of edge and hybrid cloud computing enables businesses to harness the power of cloud computing while maintaining the speed and efficiency of edge processing.

Improved Security and Compliance Measures

In 2024, we anticipate significant advancements in the security and compliance aspects of hybrid cloud environments. As these environments become more complex, the need for robust security measures that span both public and private clouds is crucial. Enhanced encryption techniques, more sophisticated identity and access management systems, and advanced threat detection tools will become standard.

Additionally, we expect to see the development of more streamlined compliance benchmarks and frameworks designed specifically for hybrid cloud architectures, simplifying the process of adhering to various regulatory standards and ensuring data privacy and protection across multiple platforms.

Innovations in Cloud Management Platforms

The year 2024 is likely to witness substantial innovations in cloud management platforms, driven by the need to manage complex hybrid cloud environments more efficiently. These platforms will offer enhanced capabilities in terms of automation, orchestration, and unified management across multiple cloud services.

The use of AI and machine learning for predictive analysis, resource optimization, and proactive problem resolution will become more prevalent. These platforms will also provide better tools for cost management and analytics, giving businesses a more granular understanding of their cloud usage and enabling more cost-effective decision-making.

Customizable and Flexible Cloud Solutions

As hybrid cloud environments continue to evolve, 2024 will likely see a rise in more customizable and flexible cloud solutions tailored to specific business needs. Providers will offer a range of options that allow businesses to mix and match services from both public and private clouds, creating highly personalized cloud environments.

This customization will extend to aspects like security, performance, and compliance, allowing businesses to fine-tune their cloud infrastructure to their specific requirements. Such flexibility will be particularly beneficial for businesses in niche markets or those with unique operational needs, enabling them to leverage the power of cloud computing without compromising on their specific requirements.

Conclusion

By 2024, hybrid cloud computing will have significantly evolved, becoming a critical component in the digital transformation strategies of various industries. This technology offers a blend of public and private cloud benefits, crucial for DevOps teams in terms of flexibility, scalability, and security. The integration of advanced technologies like AI, machine learning, and edge computing will enhance resource management, data analytics, and real-time data processing. Additionally, the hybrid cloud’s role in ensuring agility, better cost management, and compliance is expected to drive its wider adoption across sectors like healthcare, banking, retail, and government.

The landscape will also see advancements in security and compliance, with sophisticated measures spanning both cloud types and cloud management platforms will evolve to include AI and machine learning for more efficient operations. Customizable and flexible cloud solutions will become prevalent, allowing businesses to tailor their cloud environments according to specific needs. Overall, hybrid cloud in 2024 will be a strategic enabler, equipping businesses with the necessary tools and capabilities to thrive in an increasingly digital world.

December 20, 2023
Shared Responsibility? Yeah.

One of the things that the last decade or two of enhancements has brought about is an increasing reliance on everyone across the organization to be more secure. While users have long since thrown up their hands and looked for ways to minimize the fallout when a vendor they frequent is compromised, enterprises have to lock down their own things. And given the ever-increasing volume and sophistication of attacks, the demands are growing.

Cloud brought us the concept of a ‘shared responsibility‘ model. It is necessary in a world where an org does not control the physical servers and supporting infrastructure that applications ultimately run on. The fact is, the shared responsibility model was really cloud vendors reminding us that we still have to protect our apps, no matter where they’re deployed.

This model and DevOps together have driven us to demand more of developers and DevOps teams with regard to security. It has also driven ops to have to care more, as more is exposed and security demands are up while security staff isn’t.

And I’ll just remind you of the obvious. If you are responsible for security at the organization, you can get help from everyone, but those people’s job and direct responsibility is not security. They’re lending a hand because everyone cares, and that is the current state of most enterprises – DevOps made everyone involved in the SDLC a bit more responsible for all the things that aren’t their jobs (often a very good thing, often … not), but they’re not specially trained for it. That’s what security teams are for. And high-quality development security/policy management tools offer Dev and Ops and everyone in between a bit of guidance to good security practices along the way. I’ve praised putting security feedback right in the IDE half a dozen times here and elsewhere, and as a security-conscious developer, will continue to do so until it is so pervasive that people start to worry I’m losing it by going on about it. That’s a great example of helping everyone be more secure without requiring a ton of security staff hours in training. Developers don’t purposely write insecure code any more than they set out to make buggy spaghetti code. Giving tools to help them make the code more secure is a huge bonus – to them and to the corporate security team. So, if you’re not using them, start. And look into all of the other options to help non-security staff lock down their bit. The number of available security people on the market is perhaps the largest it has ever been (It’s so bad I actually know a security researcher who took months to find a new gig – that is pretty much unheard of or a unique case before recent days), but budgets to grow the security team aren’t there – any more than they are currently there to grow most teams. So tools to help are still imperative because you’re not going to get double the security staff in 2024 unless there’s only one of you … and you threatened to quit if they don’t get you help, which no one recommends as a negotiation tactic, so even if you’re solo, no doubling for you.

Just remember who is responsible for overall infosec. Make use of the specialist and their knowledge in their space by finding tools to help them secure in a manner that matches workflow. Then verify because the organization’s security posture is security’s job.

And keep rocking it. We’ve got so much cool stuff going on at the moment in IT in general that there is plenty of opportunity to improve architectures that are even relatively recent. Check what you have, consider it from a security perspective, improve what you have and keep rolling along.

But first, relax, take a breather, and enjoy the holiday period.

December 20, 2023
KubeCon: DevSecOps and Generative AI

Speaker 1: This is Techstrong TV.

Alan Shimel: Hi, everyone. Alan Shimel back here at Chicago KubeCon, North America 2023. If you’ve watched our coverage of past KubeCons or RSA or even sometimes on Techstrong TV, you know this fellow right here, he’s my friend, David DeSanto. David, is the… Is it Chief Product Officer?

David DeSanto: Chief Product Officer.

Alan Shimel: Yep, at GitLab. And GitLab always has something doing, especially if it’s the 22nd of the month, right?

David DeSanto: Correct.

Alan Shimel: How many consecutive 22nd of the month releases are we up to now? Do you know?

David DeSanto: Over 150. I know that.

Alan Shimel: Over 150.

David DeSanto: Yeah.

Alan Shimel: But GitLab is also a big community member of CNCF, as well as a bunch of the other Linux Foundation-

David DeSanto: Absolutely.

Alan Shimel: Foundations as well. And David’s always a wealth of information. So David, I hope I didn’t put too much pressure on you, but welcome.

David DeSanto: It’s fine. Thank you for having me.

Alan Shimel: Always a pleasure. So let’s first talk a little bit about the show.

David DeSanto: Yeah, absolutely.

Alan Shimel: GitLab is here. What are you seeing, hearing? What are you thinking about?

David DeSanto: Yeah, so I would say this probably is no surprise to everyone who’s watching, but this year it’s all about security and AI. And sure enough, at GitLab’s booth here, we’re booth M1, it’s also about…

Alan Shimel: Security and AI.

David DeSanto: Security, AI, and visibility.

Alan Shimel: So we’ve been talking a little bit about security and AI too. Surprise. And one of the… We actually, just a quick plug. We actually have a great virtual event coming up in December on AI, and it’s sort of where does AI meet, rubber meet the road. And I want to turn that around to you, David. Look, AI has tremendous potential to help, I think, with security.

David DeSanto: It does.

Alan Shimel: I don’t know how much of it is real today. I think a lot of it will be real, certainly. But what’s real today? No holds barred. What’s your take?

David DeSanto: Yeah, so maybe we’ll start with the AI part of that and then tie it into security. So I always look at new technology through the lens of my parents. They’re both much older than I am. And to them, when I showed them AI, it was magic because to them-

Alan Shimel: It really was.

David DeSanto: How is that happening, right? But then I also had to help ground that in reality for them to say, “Well, this is generative AI. It works by predicting the next word based off the series, right?”

Alan Shimel: Putting one foot in front of the other, right. It’s not sentient.

David DeSanto: When you look at it though, some people have thought it’s sentient.

Alan Shimel: No, your parents, I go by my wife’s sister, she’s 74, and yeah, she thought there was a little person in there, but yeah.

David DeSanto: Yeah, and actually just as a quick note, and the viewers will love this, but I introduced them to it at my sister’s wedding earlier this year. I needed to do a toast. So I had Bard generate the toast for me. And they were like, “How is this happening?” Right?

Alan Shimel: They’re beautiful, I hear you.

David DeSanto: But to kind of ground in reality and you realize how generative AI works, you have to then ask yourself what are the best use cases to apply it to? It’s not going to be able to tell you how many pages there are in a book, how many words are in a book. Those aren’t things that are questions it can build, right? But it can help you with tasks that can sometimes be mundane. It can help you with understanding something better. And that’s how I kind of tie it back to AI. So when GitLab started applying AI to our platform, we started in 2021 with code review because that was a huge pain point helping customers get through code review. The next area we looked at was actually security.

Alan Shimel: Really?

David DeSanto: And the reason why we did that is that GitLab is, in essence was a developer platform when we started, we’re an enterprise DevSecOps platform today.

But it was about helping developers be more effective. And developers don’t start their day off by saying, “You know what? I want to write a zero day vulnerability in the application today. I want to make it super vulnerable.” Right? They want to write really good code. And so we said, “We’ve helped shift security left. How do we help now those developers understand that security they’re seeing?” And so we launched at RSA this year, explained this vulnerability where it actually explains the vulnerability, natural language using AI gives them an example of how that vulnerability is exploitable. And it gives them an example of how to resolve it. And those code snippets it shows are in the language the developer’s writing it. And so when I think about AI and security, I think about it making security truly approachable. And if you can do that, then everyone starts to win.

Alan Shimel: So I would give you guys the Oscar for best use of AI, right? Because I think that’s where it shines. I had a very similar experience with me. So I’m not a coder.

David DeSanto: And I claim to be one, but it’s been a decade plus since I’ve run any code.

Alan Shimel: But you know what? Try putting a little code into your generative AI chatbot and say, “Explain this,” or, “What does this mean?” And it goes through line by line and tells you, “That’s what this is for. This is what it’s calling, this is what it’s doing.” And you can be a complete neophyte around let’s say Python or name your language, wherever you want to do it in. And it really teaches you how to code.

David DeSanto: It does. It does.

Alan Shimel: Line by line, syntax by syntax. So to me, that’s a great use of AI. That’s what we should be using it for. It’s not Google search. A lot of people I know, they think it’s Google. It’s not Google search.

David DeSanto: No.

Alan Shimel: But those kind, and it’s good for writing toasts for a best man or whatever, or an anniversary as someone in my office I know used it and his wife called him on it. But anyway, it’s really great for learning tool. Now, when we talk about AI and security, though, there’s an evil dark side to it.

David DeSanto: There is.

Alan Shimel: Which is how do you secure AI?

David DeSanto: Yeah. And so I love that question. So the first time we spoke, which now is four or five years ago, the question you asked me is, “GitLab and security, explain that.” And we talked about why we’re taking that journey. And today, GitLab is recognized as a security player in the space.

Alan Shimel: Absolutely.

David DeSanto: And it’s one of those things that we are very proud about because we helped define what shift left actually meant. And so if you bring this back to AI, the one thing I always tell people is that, yeah, code generation can be scary. You’re not sure, is it writing good code? Is it writing functional code? But if you’re using GitLab, that code gets committed back into your CICD pipeline and the GitLab security scanners run. And now you know that vulnerability has been resolved and nothing new has been introduced. And so if you pair AI with the right things around it, it can be truly powerful. But you’re right, if you’re not doing that and you’re thinking it is the person living inside the machine, you give a little too much trust to it today. AI is not a hundred percent there yet.

Alan Shimel: Absolutely. Well, so that’s the next segue. Don’t give up any secrets. Let’s not get in trouble. You’re a public company now, but where do you see AI going with GitLab specifically over the next, let’s say between now and RSA in May or something like that?

David DeSanto: Yeah, great question. So fortunately, we’re a very transparent company, so I can talk about roadmap and not get in trouble, but thank you for making sure that I don’t get myself in trouble with legal.

Alan Shimel: Or me.

David DeSanto: So where we see AI going with GitLab is there’s really two paths that we’re exploring. The first is GitLab Duo Chat. We released it back in June, it continues to mature. We’re seeing people gravitate towards wanting to talk to chat and not necessarily click a button in the UI or engage with AI that way. And it makes sense. If you think about it, we’re humans, you and I are talking, we’re having a conversation. That’s how we’d want to engage with it. And that’s why the chatbots are so popular.

And so what we’re looking to do is continue to take what we’ve released. We have 14 AI features today.

Alan Shimel: Really?

David DeSanto: Yeah. Everything from, GitLab is for the entire SDLC. So we have AI assistance in planning, coding, securing, deploying, the whole way across. And so we’re slowly moving those features into chat. An example that explained this vulnerability is a great example. So that’ll be available in chat here shortly. And the reason why we’re doing that is the natural thing is, “Well, help me resolve it. Here’s my code.” And so we want to help AI be not just a pair programmer for the developer, but be that trusted partner that can help write more secure codes. So I would say over the coming several months going into RSA, you’ll see a lot of AI and security and our Duo Chat feature coming together.

Alan Shimel: I love it.

David DeSanto: The other area is our-

Alan Shimel: Wait, hold on one second.

David DeSanto: Oh, sorry. Yeah, go ahead.

Alan Shimel: I want to make sure Duo Chat, not everyone out here is going to know Duo Chat.

David DeSanto: Yeah, so perfect. Yeah. So GitLab Duo is the name of our AI workflows within the GitLab platform.

Alan Shimel: Got it.

David DeSanto: The name is based off the fact that it’s you as a user, plus AI makes the AI dynamic duo.

Alan Shimel: A duo. Okay.

David DeSanto: It also helps that I’m a huge Batman fan and dynamic duo has that in it.

Alan Shimel: All right. I love it. I love it.

David DeSanto: I didn’t tell marketing that though.

Alan Shimel: No, no, no.

David DeSanto: No, no. No. Hopefully they don’t watch this. They’ll now know my secret.

Alan Shimel: What does marketing ever? I’m only kidding. I’m only kidding. I love my marketing people.

David DeSanto: So that’s the name of the suite of those AI workflows. And so there are things like explain this vulnerability. You’re explaining of codes for, we actually haven’t explained this code.

Alan Shimel: I love it.

David DeSanto: We’ll explain the code. We have our code suggestions, which is going to generate either code completion, your cursors in the file, it’s reading what’s before and after the file’s open and recommending code.

Alan Shimel: I love that.

David DeSanto: Or code generation where you can actually do comment decode, that’s actually the most popular for our users. As well as things in helping you do your planning, summarizing conversations, helping with CI configurations and beyond. So that’s what GitLab Duo is, and Duo Chat is part of that. And so that’s the chatbot you can launch within GitLab’s UI.

Alan Shimel: Now, is there a way to kind of implant this into your IDE, or?

David DeSanto: And that’s the last thing I was going to mention that we’re working on. Yeah. So the other thing-

Alan Shimel: You didn’t pay me to say that.

David DeSanto: I did not.

Alan Shimel: Okay.

David DeSanto: I did not.

Alan Shimel: Go ahead.

David DeSanto: It’s almost like you read our GitLab issues and read our roadmap.

Alan Shimel: We may not coordinate our wardrobes. But we do-

David DeSanto: We didn’t. I do wish I would’ve worn the fall shirt today.

Alan Shimel: Next time.

David DeSanto: We will. Next time we’ll do that.

Alan Shimel: All right.

David DeSanto: But anyway, we’re going to be bringing Duo Chat to the IDEs. So today we have it as an experimental feature and we’re trying it at GitLab today to make sure it works. But it’ll be available in all the IDEs that we support today, including the JetBrains Suite, Visual Studio, VS Code, Neovim, and the GitLab IDE.

Alan Shimel: I love it. I love that. Fantastic.

David DeSanto: But by the way, that’ll also then bring those same actions that are in the GitLab UI into chat in your IDE, so you can actually explain the vulnerability, explain the code, all those things directly where you’re working. You don’t have to leave your IDE to get that.

Alan Shimel: While we’re on this subject. A lot of people out here, look, there’s so much, there’s so much controversy around AI. Is it going to kill off humanity? Is it going to take everyone’s jobs away? Is it going… My feeling is, it’s not… Will it do some jobs? Yeah, but for the most part, it’s going to make a lot of people who do these jobs 10x more effective. How do you view it, and how does GitLab view this, in a world where chat and Duo Chat is built in and AI is built into the entire GitLab SDLC pipeline.

David DeSanto: Yeah.

Alan Shimel: Does it really 10x these people?

David DeSanto: So that’s our target. And to give a little context for everyone, GitLab Ultimate, which is our top tier, includes every future GitLab has including AI. We did a survey of our customers. We had an independent company do that. They found that people using GitLab Ultimate get a 7x boost in efficiency for their organization.

Alan Shimel: So you’re over halfway there.

David DeSanto: Yeah. And an ROI within six months of what they paid for it. And so when we asked ourselves, “How does AI help?” We set ourselves a 10x goal. So continuing to accelerate that efficiency. And our customers using GitLab today and GitLab Duo are talking about that. They’re talking about getting through code review faster. They’re talking about resolving vulnerabilities faster. And to the first part of your comment, we don’t see AI as a replacement for your team members. We see it as helping them be more effective, removing some of the mundane tasks that can slow them down and unlocking their true potential.

Imagine a world where your developers don’t have to worry about doing things like creating the template for the code and now they can just go in and write the actual strategic parts. And same thing across the entire software development lifecycle. I personally benefit from Duo almost on a daily basis, in that as the CPO of the company, I get pinged on our GitLab issues or GitLab Epic or whatever, and someone will say, “What are your thoughts on this?” And I’ll look at it and I go, “How long is this?” And I’ll go up and say, “PDF it.” And it’s like it’s a hundred pages, the issue’s been open eight months.

Alan Shimel: Summarize.

David DeSanto: Customers are coming, yeah. So I say-

Alan Shimel: I do the same thing.

David DeSanto: “Summarize the discussion,” right?

Alan Shimel: We’re connected. I do the same thing.

David DeSanto: And so that’s where I see it. That made me more efficient. I now didn’t spend two hours doing that.

Alan Shimel: I’m doing the same exact… As CEO at Techstrong, I’m inundated. Inundated with content. “Should we publish this? Is this the right tone? Here’s the abstracts.” And a lot of times, and I don’t mean to sound callous, but I just don’t have the time to go through it all. But a summary, man, just give it to me in five bullets.

David DeSanto: Right. And by the way, the other part of that that I use is that it could also help generate descriptions and tasks. And so-

Alan Shimel: A bot.

David DeSanto: Right? So I’ll just say, “I read all that. I have an action, remind me to do this. Help me create the task for it,” and so forth. But you are the great example, right? Same thing with the developer, a security person. There are other things that your brainpower should be used for and why spend the time doing those things that-

Alan Shimel: I agree.

David DeSanto: Truly are not moving Techstrong forward or that company’s application for it.

Alan Shimel: We’ll come up with a, we have a theme for given event, a virtual or in-person event. We need the abstract. I can have other people write the abstract, but it doesn’t really capture my vision for it. I find just putting that into bot, into a chat, it nails it. But here’s another thing that I think is, and again, for those of you out there who don’t use generative AI yet, you should. Don’t be afraid to give it a nice long prompt or give it a lot of back. Because the more info you give it, the better it kicks back to you.

David DeSanto: It does. It actually goes off feedback and you can like something or thumbs it down.

Alan Shimel: That was another kind of revelation. And I think it’d probably work the same way with code.

David DeSanto: It actually does. So the backend of GitLab Duo is monitoring for the amount of code you keep. So do you accept the recommendation? Do you not? It also measures retention. So is that code still there after you move on? And same thing with the chat. So I’ve actually had to say to it, and there’s a joke on the internet about you have to be polite back to the chatbot. I actually just recommend it because I’ve seen that documentary Terminator with Skynet. I’m just afraid-

Alan Shimel: One day they might come for you.

David DeSanto: You want to suck up to them now, so when they take over, they remember you.

Alan Shimel: They’ll remember it.

David DeSanto: But no, I’ll actually say back, “That’s interesting, but have you thought about this? And here’s why.” And then I see that it actually learns from it because we constantly fine tune models based off that feedback. Right?

Alan Shimel: I agree.

David DeSanto: So, absolutely. Please be kind to the chatbots. GitLab Duo will always be chat to you.

Alan Shimel: Be kind to your chatbots. And be kind to everyone else, right?

David DeSanto: If it is-

Alan Shimel: Especially in this day with everything in our world going on.

David DeSanto: If it’s anything, you should be polite. There you go.

Alan Shimel: If you can be anything, be polite. Non-AI stuff, what’s your impressions of the show this year?

David DeSanto: I’ve enjoyed it so far. RSA conference used to be my favorite conference to go to. I think KubeCon is now tied for first and might sometimes edge it out. And the main reason why is that here is one of the few times I feel like it reminds me of the Black Hat Conference back in the day.

Alan Shimel: Back in the day.

David DeSanto: You’ve got all the, it is not meant to sound negative, but the doers here, right?

Alan Shimel: Yeah.

David DeSanto: You’re having real conversations with developers who are trying to understand operations better, security professionals who are here trying to make sure they’re supporting developers’, operations. Same thing with operations. So you get this really good mix of people and it just reminds me of almost like a large startup, if that’s a good way to look at it.

Alan Shimel: Yeah, no, there’s an energy that’s palpable, that I think works.

David DeSanto: But I do want to just make sure RSA, I still love you.

Alan Shimel: We’ll be there, May 6th-

David DeSanto: I’ll be there as well.

Alan Shimel: Is our DevSecOps. It’s going to be all about AI in the future of security, our event as well as the larger AI event all week. Or the larger RSA event all week. What about re:Invent? You guys are going to be at re:Invent?

David DeSanto: We will be at re:Invent. I will personally not be there.

Alan Shimel: No?

David DeSanto: No. I actually, I hit the point where this is my last whole week of the year. I’ve been gone three weeks a month for this entire year. I want to stay married.

Alan Shimel: I hear you.

David DeSanto: I want to spend some time with my dogs. And so I’m taking December and most of January off from travel.

Alan Shimel: Good for you.

David DeSanto: Yeah.

Alan Shimel: I’m getting a new dog Friday.

David DeSanto: What kind of dog are you getting?

Alan Shimel: I guess I could share it. So we’ve had bulldogs for about almost 30 years.

David DeSanto: Okay.

Alan Shimel: Our last one passed away in May. She was 14 and a half.

David DeSanto: Actually, I think you and I talked about that.

Alan Shimel: Yeah. So it took a little while for us to get back in the, I need a dog, but I need a dog. So it’s a new bulldog puppy. Her name is Kaia, K-A-I-A, which means sea in Hawaiian. Her father’s name is Ocean.

David DeSanto: Oh, wow.

Alan Shimel: And yeah, I’ll post pictures.

David DeSanto: Okay. I think I’ve shared this to you before, but we have two rescues. They’re both Chihuahua mixes.

Alan Shimel: Oh, yeah. Yeah.

David DeSanto: One’s really dumb and the other one’s really smart, so [inaudible 00:18:47].

Alan Shimel: It’s good mix. Yeah.

David DeSanto: I will tell you, when I get home, they’re so excited to see me and they get really sad when I’m packing, so.

Alan Shimel: There’s nothing like the love of a dog.

David DeSanto: I completely agree.

Alan Shimel: No doubt.

David DeSanto: But about the conference.

Alan Shimel: Yeah, we went off there, but go ahead.

David DeSanto: Completely derailed us.

Alan Shimel: Yes.

David DeSanto: So for GitLab, a couple things. So if people are watching and they’re here, they want to see it. If not, check it out after the conference on our website. We brought our product analytics feature to market. We already have some customers adopting it and we’re demoing it. And I wanted to share it here because KubeCon’s also about visibility and you can’t close the SDLC without user feedback. And so similar to how we have observability, just about the application running in production, the value stream analytics and value stream management. This is now user feedback. So as part of GitLab CICD, we can embed telemetry into the application. So when it’s deployed, it can connect back to GitLab and start giving you data about your monthly active, daily active usage, the return rate of those users.

Alan Shimel: This is available now?

David DeSanto: It is available now.

Alan Shimel: Or you’re just demoing?

David DeSanto: No, it’s available. It is available.

Alan Shimel: What do you call that?

David DeSanto: Well, we love our boring names at GitLab, it’s one of our values. So it’s just called GitLab Analytics.

Alan Shimel: GitLab Analytics. Okay.

David DeSanto: Yeah, GitLab Product Analytics to be specific.

Alan Shimel: All right.

David DeSanto: But yeah, we started using it earlier this year for our handbook and we’re able to learn about how our users use the handbook, our employees and team members. And so I’m excited how make this available because other people want to understand how their apps are being used. Long term, we’ll be able to pull in things like retention rate, user adoption journey metrics. But today it’s just those raw metrics like the active usage, return rate, the graphs of how the usage is going. But we’re very excited to be demoing it here. It’s really cool.

Alan Shimel: Absolutely. Very cool. GitLab user analytics.

David DeSanto: Yep.

Alan Shimel: David, it’s always a pleasure, my friend.

David DeSanto: Love our chats.

Alan Shimel: You know what? I spoke to you. We’re going to do another chat with David before you leave for two months with the dogs.

David DeSanto: Yeah. I will be working, for everyone. I’m not taking time, I’m just staying home. I’ll be-

Alan Shimel: Okay. You’re just not on the road.

David DeSanto: Yeah, I’ll be working from home that entire time.

Alan Shimel: I was going to say that’s a lot of vacation time.

David DeSanto: No, no, no. I did have my first big vacation since the pandemic though.

Alan Shimel: Really?

David DeSanto: Yeah, we went to Bar Harbor for two weeks and hiked Acadia National Park.

Alan Shimel: Oh, that’s beautiful.

David DeSanto: Yeah.

Alan Shimel: And when was this?

David DeSanto: End of August. Last two weeks of August.

Alan Shimel: Oh, so it’s still nice there.

David DeSanto: It was very beautiful, but-

Alan Shimel: Not really peak color season there yet. But beautiful time, summer.

David DeSanto: But we’ll definitely can connect because I’m working. I’m just not on the road.

Alan Shimel: But we’re going to have you on Techstrong TV because you guys have more news.

David DeSanto: We do.

Alan Shimel: That we can’t talk about yet.

David DeSanto: Yeah, that’s a good teaser. Everyone tune in.

Alan Shimel: That was my, that’s what we call it.

David DeSanto: For the big news. It’s the hook.

Alan Shimel: That’s what we call it in the industry. My hook.

David DeSanto: Going back to our marketing friends. That’s the hook.

Alan Shimel: All right. Hey, we better take a break here. We’re getting a little loony. We’ll be back. We’re live in Chicago at KubeCon with our friends at GitLab. We’ll be right back.

December 19, 2023
From Machine Learning to DevSecOps: Six DevOps Trends for 2024

The DevOps market is set to grow by 25% annually between 2024 and 2032, fueled by the growing demand for faster software delivery and higher-quality output. Emerging technologies like AI-driven development and serverless architectures have introduced new products in the space, while traditional processes like version control and CI/CD will be further refined. But amongst this innovation, security and quality should remain as organizations’ highest concerns. Looking into the coming year, it’s hard not to see where the DevOps industry is going based on the past year. As such, we have endeavored to provide six distinct DevOps trends we think will be particularly influential in the new year.

The Integration of AI and Machine Learning

One of the most notable trends in the future of DevOps is the increasing incorporation of artificial intelligence (AI) and machine learning (ML) into the software development life cycle. The explosion of AI technology in 2023 has set the stage for more predictive analytics in code development, automated testing and intelligent monitoring in the operations phase.

As Google’s 2023 State of DevOps Report highlighted, DevOps teams feel that AI will contribute most to analyzing data and security, alongside identifying bugs. However, a solid use case has yet to materialize for most teams, and the report shows that early adopters of AI have experienced a minor decrease in their software delivery performance and a significant decrease in their operational performance. So, it’s possible that at this stage teams are implementing AI without considering what problems they’re trying to solve, or without paying full attention to the security and quality implications.

Emphasis on Increased Productivity

While AI adoption will be driven by the underlying push for increased productivity in DevOps tools and practices, DevOps teams should focus on drawing maximum value from the existing tools that automate mundane and repetitive tasks, allowing them to focus on more critical aspects of development and operations. This will be made possible by DevOps vendors that provide enhancements to collaborative tools, simplified interfaces and more intuitive workflows, reducing the cognitive load on developers and operations professionals.

Culturally, the continuing trend towards remote and hybrid work environments is influencing productivity strategies in DevOps, with tools and practices that support remote collaboration and communication becoming vital. Cloud-based DevOps tools are facilitating seamless collaboration, allowing teams to work efficiently irrespective of their physical locations.

Alignment to Existing Cloud and Microservices Architecture

We can expect the new alignment of DevOps with cloud and microservices architectures to continue into 2024. This integration is about more than just adopting new technologies — it’s about creating efficiencies that enhance agility and revenue growth.

The combination of DevOps practices with cloud and microservices enables organizations to leverage the inherent benefits of these platforms. Cloud infrastructure is scalable and flexible, reducing overhead costs associated with physical servers. Microservices allow for rapid development, testing and deployment of independent components without disrupting the whole system. Together with DevOps automation and continuous practices, these architectures accelerate innovation and time-to-market.

This alignment of DevOps with cloud and microservices will lead to enhanced scalability, resilience, collaboration and customer experiences. By being able to quickly scale, update, and respond to demands, organizations can better satisfy customers and meet market needs, and this, in turn, will drive revenue growth and productivity.

The Drive for Higher Quality

While increased productivity and revenue generation will be a key priority in 2024, it’ll be matched by an unwavering commitment to quality. With the rising complexity of software and the increasing dependency of businesses on digital platforms, the margin for error is slimming. Quality assurance practices will become more integrated into the DevOps workflow, ensuring that each release meets the highest standards of performance, security, and user experience.

Continuous integration/continuous deployment (CI/CD) practices are evolving to include more rigorous and automated testing protocols. These protocols are designed not just to catch bugs but to assess performance under diverse and real-world scenarios. Real-time monitoring tools are being integrated more deeply into DevOps workflows, providing ongoing insights into application performance and user experience. This continuous feedback loop ensures that any issues are identified and addressed promptly, thereby maintaining high standards of quality post-deployment.

DevSecOps Maturity

In the same vein, cybersecurity will continue to be a primary concern in 2024, with core DevOps tooling and processes stepping up by incorporating advanced security features. Version control systems will take the lead here by introducing robust access controls, encryption of code repositories and integration with security testing tools to ensure that the code is not only efficient but also secure from vulnerabilities.

Security integrations in version control will become more advanced, possibly incorporating real-time scanning for security vulnerabilities, tighter integrations with identity management platforms and pattern analysis to predict and prevent potential security breaches in code.

And as AI is pioneered throughout DevOps tooling, organizations will need to consider the security implications. The Open Worldwide Application Security Project (OWASP) has predicted that data leaks, permission issues and insecure plugins could pose serious security vulnerabilities for software. Teams that can harness growth with AI in 2024 while building sufficient security through DevSecOps practices like backup and monitoring will future-proof themselves.

Speed and Security

The speed of DevOps growth, innovation and adoption is only set to increase in 2024. We’ll see teams pioneer new technologies to meet business priorities and revenue targets. In doing so, it’s very easy to jump on the AI or serverless bandwagon and assume that sprinkling it on every technical discipline will make for a bright and shiny future. While some key opportunities in the DevOps space will benefit from the acceleration and tighter integration that new technology brings, organizations looking for lasting growth will need to prioritize quality and security practices.

The key trend we’ll see in 2024 is teams getting smarter and more focused on how they apply new technology to their DevOps trajectory, delivering faster, more accurate releases that free up time to deliver ever greater value to their customers and business. So, the DevOps winners of 2024 will be the organizations that manage to balance the speed of new technology adoption with robust security.

December 19, 2023
Opsera Leverages AI to Simplify DevOps Platform Migrations

Opsera is leveraging a generative artificial intelligence (AI) model it built to enable DevOps teams to migrate from one platform to another.

Gregg Holtzrichter, chief marketing officer for Opsera, said the Hummingbird AI model makes it simpler to migrate source code from, for example, a Bitbucket on-premises continuous integration/continuous development (CI/CD) platform to GitHub.

Opsera originally developed Hummingbird as an orchestration engine that integrates DevOps workflows spanning multiple tools and platforms. The primary goal is to make it possible for DevOps teams to unify their workflows, said Holtzrichter.

At the same time, Opsera is now making it possible to leverage a generative AI model to reverse engineer the source code a DevOps team may have developed to extend the capabilities of a specific platform. That new code then makes it possible to significantly reduce the total cost of switching DevOps platforms, said Holtzrichter. As part of such an effort, Opsera is also now providing support for organizations looking to specifically migrate to GitHub Copilot and GitHub Actions.

It’s not clear how many DevOps teams might be looking to switch platforms, but with the rise of generative AI capabilities for writing code, interest in platforms such as GitHub has increased, noted Holtzrichter. In addition, support for platforms such as the on-premises edition of Bitbucket is being sunsetted by Atlassian as part of an effort to drive more customers toward the cloud edition of the CI/CD platform.

Regardless of the underlying platforms involved, generative AI promises to reduce the ability of any vendor to lock customers into a specific platform. As DevOps teams extended DevOps platforms over the past decade, many of them have discovered that the integrations they created and maintained made it all but impossible to switch platforms. With the ability to refactor much of that code based on the insights surfaced by the Opsera platform using generative AI, the time and effort required to switch platforms has been substantially reduced, noted Holtzrichter.

At its core, Opsera makes it possible to apply analytics to DevOps workflows as they become more complex. The number of dependencies that exist between various application modules, for example, makes it exceedingly difficult for DevOps teams to manually identify the impact a delay in one project might have on any number of other downstream projects.

The Opsera platform surfaces that intelligence by scanning all repositories and branches, including commit history and generating alerts and notifications whenever KPI thresholds or compliance requirements are exceeded. DevOps teams can also use the platform to assess their overall DevOps maturity using, for example, the DevOps Research and Assessment (DORA) metrics defined by Google. In addition, many organizations are keen to discover what best practices should be adopted by other application development teams.

Now that AI is being applied to that analytics capability, it’s become possible to extend the reach of the Ospera platform beyond analytics to include source code migration. Each organization will still need to determine whether such an effort is worth the remaining time and effort still required, but at the very least, most DevOps teams can take comfort in the fact they can now keep their options a lot more open.

December 19, 2023
Seasons Greetings 2023

December 19, 2023
What Will DevOps Look Like in 2024?

The last two years have been marked by significant downsizing in technology companies. As large layoffs at companies like Amazon, Meta and Google make major headlines, there is a general shift away from the “spending cash and growing fast” mindset that free capital and strong growth during the pandemic launched in the tech industry. The name of the game is to be more conservative with spending, and that mindset will drive a lot of investments in the next year. While these cost pressures may feel challenging to technology organizations, there are a few key opportunities as well. Many trends are starting, sustaining and ending, so let’s focus on the top six likely to drive changes in the industry in 2024 and beyond.

1. AI—Specifically Generative AI—Move Right on the Hype Cycle

While AI is not new, OpenAI and ChatGPT have created an amazingly accessible and powerful tool that is positioned to drive a lot of value. The hype cycle is still in full swing as dozens of new uses of the technology hit the market daily, and if you’re not trying to build a generative AI-enabled product, you’re getting asked when you will start. In 2024, we will likely see LLMs move into the trough of disillusionment as inflated expectations and projects don’t all play out. However, those organizations with generative AI products that truly deliver value to users should expect to see massive growth. Technology organizations will need to make hard decisions about AI products. You will either find traction in the next year and see huge growth or quickly find it doesn’t work and shift focus back to non-AI products.

2. Developer Experience Gains Momentum

While DX has been talked about for years, we’re seeing new investments in tools that empower developers. This trend is partially born out of a need to reduce tool and system sprawl for engineering teams, to empower teams and allow them to move faster and to improve efficiency. While you may not build or buy a DX platform, you will likely start a DX team or committee that better represents the folks building your software. This will lead to some great internal changes and key efficiencies getting attention that may have been previously overlooked. The wonderful thing about this trend is that it both brings efficiencies to the organization and makes developers’ lives easier. It’s a win-win!

3. Increased Pressure to Reduce Costs

Even for organizations that have avoided layoffs, most are seeing pressure to shift into a leaner existence. This often means critically looking at all sources of spending and taking on new projects to optimize them. Maybe a cloud re-platform can reduce hosting spend by 20%, or maybe you start looking to remove or combine SaaS tools to reduce seats. You may even see hiring freezes for critical teams. Organizations will have to embrace other trends to increase the efficiencies of their teams to meet business demands here. Don’t be surprised to hear “do more with less” become a mantra around the office.

4. Increased Accountability Through Data

While data platforms for technology organizations continue to grow, teams are finding that it’s not enough to just measure a few metrics. Organizations will be required increasingly to use the data they have to show they are delivering value. Partially due to increased pressures mentioned above, teams will need to become more predictable, and better at identifying and handling risk at every stage of development. It’s no longer enough to just build a great development engine, teams must ensure that the engine can power them to their destination.

5. ESG in Technology

Focus on DEI in technology continues to require significant investment, however, organizations are starting to consider a broader umbrella of social responsibility. This can range from considering the environmental impacts or carbon production of your servers to changing your software supply chain to avoid supporting particular organizations. These types of requirements are appearing sooner and more overtly in the software development process, and may significantly impact how teams choose to build or implement software.

6. DevSecOps Continues to Grow

There has been a renewed focus on security in recent years after some critical attacks exposed vulnerabilities in the software supply chain. It’s not surprising that security is now written into the fabric of how we build and deliver software. Teams are now expected to consider security at all stages of development from product planning to operation in production, and require additional experts, training and resources to both monitor and ensure compliance. Requirements for SaaS products continue to evolve as the last remnants of on-premises solutions evaporate, and teams should be prepared to move quickly as new threats are discovered and need to be patched.

As the technology industry prepares for 2024, it’s clear that innovation and adaptation will be the keys to success. From the continued evolution of AI and the growing focus on the developer experience to the imperative to reduce costs, increase accountability through data, embrace ESG practices and fortify security through DevSecOps, technology organizations face an exciting future.

December 19, 2023
KubeCon 2023: Elevating Observability

Speaker 1: This is Techstrong TV.

Alan Shimel: Hey everyone. We’re live back here at KubeCon. I hope you’ve been enjoying our coverage today, Mitchell Ashley, our CTO and a GM of Techstrong Research. And I have been kind of splitting the duties. Just we’re that booked with people. I don’t know, there’s over 10,000 people here. I’m going to guess there’s 12 to 15,000 people.

Alex Burkhardt: Yeah, there’s a lot of people.

Alan Shimel: … At the event. Maybe more I’ll know more. I’ll find out tonight. But anyway, let me introduce you to our next guest. His name is Alex Burkart. Alex is with Observe Inc. Alex, welcome to Techstrong TV. Thanks for being here with us at KubeCon.

Alex Burkhardt: Yeah, thanks Alan. I appreciate you guys taking some time to talk to me today.

Alan Shimel: Absolutely. So let’s start with this. Let’s start with Alex.

Alex Burkhardt: Yeah.

Alan Shimel: What do you do at Observe?

Alex Burkhardt: Yeah, so at Observe I run our technical customer facing team. We call it field engineering. And we work with customers from the first interaction when they’re in the sales cycle to figure out if observes a great solution for them, all the way to success and renewal and helping them realize value from their investment in our partnership.

Alan Shimel: Okay. I mean, so you’re at that role, but let’s tell a little bit about more your personal journey.

Alex Burkhardt: Oh yeah. So I started as a computer scientist actually in the Navy.

Alan Shimel: Really?

Alex Burkhardt: Yeah. I went to the Naval Academy, got a computer science degree, and they said, that’s great, go drive some ships.

Alan Shimel: Such is life in the Navy, what do you expect?

Alex Burkhardt: And then my journey brought me to Silicon Valley after I did some cybersecurity work for the Navy. And what I love more importantly than anything else is helping people find a way to use technology to solve a problem that makes their life better.

Alan Shimel: Excellent. Now, when did you come to Observe?

Alex Burkhardt: I’ve been at Observe for more than three years now. Yeah, I was employee 28. I think if my numbers are right, pre customers, in fact.

Alan Shimel: Really early.

Alex Burkhardt: I think when I started, we signed our first customer.

Alan Shimel: Good for, good for you. Good for them. So we don’t have to assume, I know for a fact there are people out here who don’t know, Observe from, have never observed Observe. Tell them. How would you describe what Observe is and does to our audience?

Alex Burkhardt: That’s a great question, Alan. So at Observe, what we realized is observability is a data problem. So we started by building a data company. What we wanted to figure out is can we bring a solution to market that allows customers to send any data they’d like to correlate it and interact with it in a way that allows them to get more value out of it more quickly. And what we’ve done is we’ve brought a solution to market that unifies the observability tooling into one platform that allows you to send logs, metrics, traces, and any other type of data structure or unstructured that you’d like to keep in your observable universe into one tool, one data store.

Alan Shimel: Love it. And before we jump into news and stuff, for people who want to just say, hey, I’d like to just see what their website is, what’s the URL?

Alex Burkhardt: Yeah, our website is www.observeinc.com.

Alan Shimel: So it’s observeinc.com.

Alex Burkhardt: Observeinc.com. We are Observe Inc technically. So observeinc.com.

Alan Shimel: Makes sense to me. All right, we’ve got all that out of the way, Alex, let’s jump in. Now, you guys recently announced some new product feature releases. Why don’t we jump in right there and see what’s that about?

Alex Burkhardt: Our last release was called Hubble, and Hubble is significant for a couple of reasons. One, naming after the Telescope, one of the things that we want to do is help people elevate their observability. And that came in two specific forms.

Alan Shimel: I get it. I just got to ask, is the next release called Web?

Alex Burkhardt: We have considered other satellites, and in fact, we had Erica on our launch. She was actually a NASA scientist.

Alan Shimel: Very cool. All right.

Alex Burkhardt: One of the things that we know is very important to our customers is if we bring a solution to market and it’s very, very powerful, is it easy to use and are we taking advantage of the latest and greatest technology that’s emerging in the market? On the ease of use side, one of the things that this Hubble launch and we did, we call it a launch included, was the recognition that when we introduced our logs in our metrics and our tracing explorer, we’ve seen an enormous increase in users engaging in the platform. And we supercharge that by adding one additional layer, which is how we leverage GPT or AI in our platform. And that happens in four specific locations. We have a chat bot that allows you to get guidance on how to use the tool, will actually allow the AI to write any of our query language for you if you’d like to do that. We call that copilot.

We also have two cool functionalities in terms of making your data more actionable. You can click on a field and observe and ask Ollie or Ollie GPT specifically to explain what an error means and it will actually return an answer that helps you stop having to copy and paste into Google and do outside investigations. The last thing that’s most interesting is we leverage GPT to allow users to separate out their data and do those hard parsing and Reg X challenges that no one likes writing Reg Xs and anyone that has written them knows that regx101.com is their friend, and we wanted to kind of bring that into the product and make that easy for customers to use.

Alan Shimel: Very cool. Very cool. Now this was released not here at the show, it was released earlier in the month.

Alex Burkhardt: October 4th.

Alan Shimel: What’s been the feedback?

Alex Burkhardt: Well, we’ve more than doubled our monthly active users since we’ve released that.

Alan Shimel: Really?

Alex Burkhardt: Yeah.

Alan Shimel: Since October 4th?

Alex Burkhardt: Technically since late September. But yes.

Alan Shimel: Wow.

Alex Burkhardt: It’s really simple. We listen to our customers and we really care about what they say and how they experience ourselves on our platform. And when we say we’re focusing on ease of use, we mean it.

Alan Shimel: So the entrepreneur in me just has to ask you.

Alex Burkhardt: Okay.

Alan Shimel: What were you waiting for?

Alex Burkhardt: It’s very easy to make very quick, bad decisions. It’s a lot harder to take a data-driven approach to how to build a product better. And when we approach any problem at Observe, and it’s built into our culture as we first of all figure out this is an important problem, how can I measure the impact I can make on this problem? And that’s how we measured usability. Now for AI, we kind of knew that it was going to be something special and it could help. We’re still figuring out exactly how to measure the impact there, but every decision we make at Observe is grounded in what’s best for our customers and how do we help make the market better.

Alan Shimel: So how much of this phenomenal growth in such a short period of time do you think is just attributed to riding the AI wave, if you will?

Alex Burkhardt: That increase in active user growth actually wasn’t tied to the AI release. It was more to the ease of use functionality that we brought. And I think that’s important for any product, but more specifically, one as powerful as ours. When you’re solving big data problems, people get exposed to big data. And one of the things we thought very deeply about is how do we take the experience that a user has to have to get to the outcome they want and narrow that down and give them an obvious path to go through so that they don’t have to deal with all of the problems if they just want to come in and check their logs and see what’s not working. Sometimes it’s the simplest question of, hey, is it easy to do this in the tool? And when we decided ourselves that it wasn’t easy enough, then we started making some really serious changes and that was backed by every single one of our customers agreeing with us back in the active user growth.

Alan Shimel: Sure. That’s fine. That’s phenomenal, man. Alex, I can’t look for those out here. This is impressive. Let’s talk a little bit about KubeCon here now. I mean, it’s only day one of the expo floor open and everything. What are you hearing from the crowd, from the attendees?

Alex Burkhardt: Yeah, that’s a great question. This is my second KubeCon. I was new to it last year when we were in Detroit, and I really love this conference, Alan.

Alan Shimel: Yeah, it’s great. It’s a great community.

Alex Burkhardt: It’s a great community. It’s a great conference. And frankly, when people come up to our booth and want to talk to us, they want to talk about what we do. There’s not a lot of tire kickers. Everyone that’s here seems to be operators, people that are experienced and steepen the problem. And I love whenever someone comes up and talks to us. I think the other thing I’ve seen in this particular venue specifically, the room is packed. It’s crowded, so crowded that I think that we’re going to have to have a bigger footprint next year.

Alan Shimel: Observers or the whole?

Alex Burkhardt: Both.

Alan Shimel: Yeah. Well, the problem is there’s a pretty big convention center. I mean, short of breaking into sort of a two different exhibit halls, which RSA used to do that. Now it’s one big continuous one. It’s hard. The other thing I will tell you, so we do the North American and the European. So in Amsterdam in the spring, they actually said, look, I think it was 12,000 was the number. At 12,000 they stopped selling tickets. They did a waiting list and a lot of people didn’t get to the show. They were on the waiting list. As anti-business as that sounds, I wouldn’t be surprised if they did something similar.

Alex Burkhardt: Yeah, that makes sense. I mean, the quality of conversations and the quality of attendee keep the quality super high. And you asked about some of the conversations we’re having when people come by. It’s a variety of things and a couple of themes that I see. I’m curious if other people that have come on the show have said the same, but people continue to talk to us about saving money. They want to do more with less.

Alan Shimel: That’s the big thing today.

Alex Burkhardt: [inaudible 00:09:23].

And I think they also want to figure out, do we need all of the tools? Is there a way we can both get some hard dollar savings and some soft dollar savings. By compressing some tooling and aligning teams we’re also seeing a lot of push and the DevOps teams are getting more involved in security conversations as well. Which I guess-

Alan Shimel: No, no, that there’s no doubt.

Alex Burkhardt: As a former security practitioner, if you had asked me five years ago if I thought these two markets would converge, I would not have said yes. Now I think it’s inevitable.

Alan Shimel: I think it’s already happened, right? So there’s a lot of convergence going on. There’s security with DevOps into what we call DevSecOps. But there’s also, there was a time, and I’ve been going to these conferences now before Covid, so I don’t know, four or five years. There was a time where cloud native was very developer-centric, not as much centric, but it was very distinct from DevOps. There was DevOps and there was cloud native and then there was security. So you look at a company like ours, right? We have DevOps.com, cloud native now, security boulevard.

And I always knew there was an inherent overlap between them, but we’ve seen much more than an overlap now. We’ve seen a conversion around this with security being a primary concern of people. And you don’t do cloud native today without doing DevOps.

Alex Burkhardt: Agreed.

Alan Shimel: And everything getting pushed out today, especially Greenfield, not Brownfield stuff. It’s predominantly probably 85% or better cloud native.

Alex Burkhardt: Yeah, I think that’s another thing I’ve seen too as well. Many, many customers are adopting the cloud native terminology, setting goals in those languages and words.

Alan Shimel: Now, multi-threaded, no doubt. Container based, the whole cloud native thing. And now what I think we’re starting to see and we see, look, you’re naming your company’s Observe. It’s not just the architecture and development being cloud native, but the cloud native aspect of the operations of ops. Forget dev, forget dev ops, ops.

Alex Burkhardt: It even touches revenue. So one of the things that we see very often is do the nature of how our platform can process data. We take technical telemetry from a customer’s application. We also take business data. Sometimes customers ask us, how can you Observe, help us give our customers a better experience? Well, one of the ways is we can tell you when a particular customer is hurting, when a customer has a challenge or help you reframe all these very deep technical jargon and things that are happening into the context of how does this impact the customer’s experience? How am I able to get proactive? Is what I’m doing in my infrastructure plan having a net positive impact on my customers, on my users?

And those are questions that previously companies I don’t think they thought they could answer. And when they start working with us, they start seeing a path to that. And I love that. We have some customers even completely away from the DevOps side, that send us shipping data just because they want to know, can I make a support dashboard for my low level support employees so that they can be more efficient with their time and our data? It’s such a cool convergence. I agree with you. I love that word convergence.

Alan Shimel: It really is. Let’s talk a little bit about observability though too. So over the last two years, so there was a KubeCon in LA during around Covid time that was a little dicey. Then there was the one in Europe, then got canceled. Then there was Detroit, then Amsterdam here. We’ve really seen the observability market just explode, right?

Alex Burkhardt: Yeah.

Alan Shimel: Prometheus. What’s the other, I’m drawing a blank. The other big observability open source. OpenTelemetry.

Alex Burkhardt: OpenTelemetry. Yes. Everyone-

Alan Shimel: I think of the second and third biggest projects of the gazillion projects in CNCF now. Right?

Alex Burkhardt: That’s such a good point. I remember when OpenTelemetry was OpenTracing and it was still kind of a fractured project. If you asked us three years ago if we thought that open telemetry would be the way, we weren’t super sure a year into that. So two years ago we were like, yeah, this is the direction. It’s solid.

Alan Shimel: No doubt about it. You look and you can’t see this at home guys because all you’re seeing is here, but there’s a-

Alex Burkhardt: All our beautiful faces.

Alan Shimel: … Big floor out there, but so much of it is based now on this. Really good stuff, really good stuff.

Alex Burkhardt: I think the other thing, observability as a vernacular is kind of emerging even at the higher level in the business. By definition observability is, can I tell something about a system by looking at its outputs? And one of the things as we’re moving closer to cloud native is more and more systems have outputs and that kind of builds on that data problem I mentioned and why we were founded. So I look forward to seeing how this problem space changes in the next year.

Alan Shimel: Oh, I think we’re just, look, when you start, you guys are already doing it with gen AI and stuff. You start adding gen AI to this stuff, we start putting more focus on it. We’re in for an interesting times. What’s the Irish proverb? May you live in interesting times.

Alex Burkhardt: Yes.

Alan Shimel: Definitely interesting times. Anyway, Alex, thanks for joining us here live at KubeCon. This is Observe Inc. So let me again observeinc.com. Check it out. Hey, they’re doing amazing things. This new release, something that gets twice as many new visitors right? In a couple of weeks, a month. It’s got to doing something right.

Alex Burkhardt: Yeah.

Alan Shimel: All right, we’re going to take a break. We’re live at KubeCon in Chicago. I think we have one or two more videos before we wrap for today. We’ll be right back.

December 18, 2023