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Tag: Dynatrace

Dynatrace Acquires Arize as AI Agents Deepen the Observability Challenge

Dynatrace announced Thursday it has agreed to acquire AI observability company Arize in a $915 million cash and stock transaction.

Rick McConnell, CEO of Dynatrace, said the company expects demand for AI observability will keep growing as more AI systems move into production. He said acquiring Arize would speed up Dynatrace’s roadmap, expand its developer reach and bring Arize’s AI team into the company.

Dynatrace said the deal would combine Arize’s AI evaluation and observability software with its own platform for correlating telemetry and tracing dependencies across applications, services and infrastructure. The transaction includes about $815 million in cash and replacement equity awards for Arize employees and is expected to close later this quarter or early in Dynatrace’s fiscal third quarter.

DevOps teams are already used to piecing together telemetry across distributed systems when something breaks. AI adds another source of context to correlate when information about model and agent behavior and output quality lives in separate systems from the application and infrastructure data used to investigate failures. That can leave AI engineering, SRE and platform teams trying to reconstruct the full story across the production environment.

That is where Arize fits. Its tools track what happens during an AI or agent run, including model calls, retrieval and tool use, and use those traces to evaluate both the final output and the path the agent took to reach it. Arize co-founder Jason Lopatecki said the company collects billions of agent trajectory events, while Dynatrace tracks what the surrounding software does. Combining those views could give teams an easier trail between an agent’s actions and the transaction, service or infrastructure involved when something goes wrong.

Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, sees that connection as the deal’s key value.

“Dynatrace is paying $915 million for the observability-native link between AI observation and agent behavior,” Ashley told devopsy.kinsta.cloud. “An AI engineer measures output quality in one tool. Without it, the current state-of-the-art is that the on-call SRE sees a failed transaction and has no path back to the prompt.”

The issue becomes more than a debugging problem once agents start taking actions in production. Teams also need a record of what the agent did, what systems it touched and what happened afterward. When that trail is incomplete, verifying agent behavior becomes another manual task for platform and operations teams.

“Enterprises ship agents only as fast as they can prove that those agents’ behavior is accountable,” Ashley said. “Verification debt lands on platform teams as a review backlog.” Because agents often run across more than one vendor’s stack, Ashley said enterprises should make that accountability trail a contract requirement that carries across those different systems. That trail has to remain intact as agents cross vendor boundaries.

That need for continuity helps explain the fit between Arize and Dynatrace. Lopatecki said Arize contributes a deep record of agent trajectories, while Dynatrace provides the traces and logs needed to connect those actions with the environment. He sees the acquisition as a way to join those two views as agents become more embedded in production software.

“These two areas just belong together if we are going to build the future systems that continuously improve,” he said.

August 17, 2026
Dynatrace to Acquire Bindplane to Process and Route Telemetry Data

Dynatrace this week revealed it has agreed to acquire Bindplane, a provider of a platform for pre-processing and routing telemetry data.

Bob Wambach, vice president of market and customer insights for Dynatrace, said the addition of the Bindplane platform will further accelerate a rapid expansion of the log management capability that Dynatrace already provides. However, instead of requiring all telemetry data to be processed in the backend on its observability platform, IT teams will have the option to pre-process that data to reduce costs and streamline workflows, he added.

Additionally, Dynatrace is committed to ensuring the Bindplane platform remains agnostic after the acquisition closes this month, which means IT teams will be able to route telemetry data to multiple backend systems, noted Wambach.

While there has been some debate over the merits of using, for example, an OpenTelemetry agent to pre-process telemetry data, the massive expansion of the amount of telemetry data being generated, especially in the age of artificial intelligence (AI), is likely to force the issue. Good data, after all, is now a prerequisite for good AI, said Wambach. Arguably, one of the reasons that so few organizations have operationalized AI agents is that they are unable to ensure the data they access is pristine enough to reliably act on, he added.

One of the primary benefits of the Dynatrace platform is that it includes multiple types of predictive and causal deterministic AI models that can be used to help ensure AI agents are performing tasks as expected, said Wambach.

Bindplane has, in part, been addressing that issue by first making it possible to orchestrate the deployment of fleets of open source OpenTelemetry collectors. The company also provides a Blueprints capability that makes it simpler to reuse instances of validated telemetry pipelines for specific use cases. Additionally, IT teams can also use AI to automatically identify log types, apply parsers, and optimize configurations to streamline the management of OpenTelemetry data.

Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said as deployments of cloud‑native and AI applications continue to increase, IT teams are seeing rapid growth in telemetry. In fact, telemetry pipelines that capture, optimize, and deliver this data in an actionable form have become foundational to modern software ecosystems, he added.

The Bindplane platform optimizes and governs telemetry data at the edge to improve quality, reduce ingest costs, and enhance compliance, noted Ashley.

That’s crucial because IT teams are increasingly investing in observability platforms to operationalize and analyze all that telemetry data. A recent Futurum Group survey finds well over a third (36%) plan on spending more than $1 million on observability in 2026, with 7% planning to spend in excess of $5 million.

Each IT organization will need to determine to what degree to reduce their reliance on legacy monitoring tools to embrace observability, but as IT environments become more challenging to manage there will be a clear need to rely more on AI. The only issue that remains to be resolved is what type of AI to use when depending on the task at hand.

April 10, 2026
JFrog and Dynatrace Extend DevOps Reach to NVIDIA Enterprise AI Factory

NVIDIA in partnership with JFrog and Dynatrace are working toward integrating DevOps tools with a platform for building artificial intelligence (AI) agents.

At the COMPUTEX conference in Taiwan this week, NVIDIA added a validated design for building AI agents to the NVIDIA Enterprise AI Factory, an application development platform based on the NVIDIA Inference Microservices (NIM) framework that leverages containers to provide access to reusable services.

Additionally, NVIDIA also added NVIDIA AI Blueprints to make it simpler for developers to build avatars, otherwise known as smart AI teammates.

At the same time, JFrog revealed that its platform for managing software artifacts will be included in the NVIDIA Enterprise AI Factory platform.

JFrog CTO Yoav Landman said the Software Supply Chain Platform developed by JFrog will now serve as a single source of record for keeping track of the provenance of the software artifacts and AI models used to build AI applications. The JFrog Software Supply Chain Platform provides the ability to track the pulling, uploading and hosting of AI models and datasets, AI containers, Docker containers, and dependencies across NVIDIA’s AI Factory platform. All told now, the JFrog artifact repository can now support more than 40 different types of software packages, including NVIDIA NIM.

Additionally, DevSecOps teams building these applications will be able to use JFrog tools to scan for vulnerabilities and other security-related issues, he added.

JFrog also revealed the core JFrog Platform can now also run natively on NVIDIA’s Grace Blackwell graphical processor units (GPUs) to improve overall performance.

Dynatrace, meanwhile, announced it is integrating its observability platform with NVIDIA Enterprise AI Factory to enable DevOps teams to troubleshoot the NVIDIA application development platform.

It’s not clear how widely adopted the NVIDIA Enterprise AI Factory has been adopted, but as more AI applications are built, the need to integrate these types of platforms into DevOps workflows is becoming more pressing. On one level, AI models are just another type of artifact being used to build an application. However, the way existing artifacts are managed using version control systems doesn’t as easily apply to AI models. Each new version of an AI model, for instance, isn’t backwards compatible with the previous instance of the model, noted Landman.

Regardless of how AI application development becomes integrated with DevOps tooling and platforms, the pace at which these applications and associated AI agents are being built will increase dramatically in the age of AI.

The challenge and the opportunity is integrating a set of application development platforms that, in many instances, are adopted by data science teams rather than traditional application developers. In effect, data scientists are emerging as a new type of end user that DevOps teams will need to support.

Undoubtedly there will be culture clashes in the months ahead as organizations look to extend their existing DevOps workflows to govern the development of AI applications. In the meantime, however, DevOps teams would be well-advised to investigate how their organizations may be building AI applications today in a way that probably isn’t going to scale as easily as it should.

May 20, 2025
Dynatrace Extends Data Reach and Scope of Observability Platform

At its Perform 2024 event today, Dynatrace unfurled a Dynatrace OpenPipeline that makes it possible to apply analytics to multiple types of data sources in real time.

The company also unveiled a Data Observability offering that can be used to vet the quality and lineage of data being exposed to the Davis artificial intelligence (AI) engine at the core of the Dynatrace observability platform to reduce false positives that otherwise might generate an alert in addition to helping reduce the volume of data that might need to be stored.

Finally, Dynatrace announced it is starting today extending the reach of its observability platform to the large language models (LLMs) that are used to create generative AI platforms. That capability will, for example, make it simpler to monitor the consumption of tokens that are used to provide access to these models.

Steve Tack, senior vice president for product for Dynatrace, said Dynatrace OpenPipeline will enable organizations to streamline the collection of data in a way that will enable them to apply observability more broadly by applying stream processing algorithms to petabytes of data.

Scheduled to be available in 90 days, the Dynatrace OpenPipeline capability enables IT teams to ingest and route observability, security, and business events data from any source and in any format at the point of ingestion, including unstructured data that is automatically converted into a usable format. That data can then be enriched to enable deeper analytics.

It also provides IT teams with more control over which data they analyze, store or exclude from analytics, which in turn helps reduce the total cost of observability, noted Tack.

Finally, it presents IT teams with the ability to apply more customizable security and privacy controls to how that data is employed, he added.

Dynatrace is pursuing a multimodal approach to AI that spans predictive, causal and generative models. Collectively, that approach makes it simpler to identify the root cause of issues, identify anomalies that might disrupt services and streamline incident management by, for example, providing summarizations of events in a natural language format.

It’s not clear how broadly observability will be applied beyond the data collected to manage DevOps workflows and other IT processes, but it’s clear there is a high correlation between IT events and business outcomes as organizations become more dependent on software. As AI becomes more pervasively employed, it should become simpler to apply analytics to a much wider range of types of data to surface the relationship between IT events and business processes. In effect, Dynatrace is making it simpler to apply best data engineering practices to collect, manage and analyze that data, noted Tack.

In the meantime, DevOps teams should revisit how their pipelines are currently constructed to make it simpler to capture all relevant data. As critical as telemetry data collected from the DevOps platform is to ensure application development and delivery occurs as fast as possible, that data is only one factor in a larger equation. The challenge and the opportunity now is determining how best to apply AI to correlate it all.

January 31, 2024
Dynatrace Survey Surfaces DevOps Automation Gains and Challenges

A global survey of 450 DevOps and security practitioners published this week by Dynatrace found investments in automation have improved software quality (61%), reduced deployment failures (57%) and decreased IT costs (55%).

However, only 38% of respondents said their organization had a clearly defined DevOps automation strategy. On average, organizations have succeeded in automating just over half (56%) of their end-to-end DevOps life cycle, the survey found.

Saif Gunja, director of product marketing for Dynatrace, said the survey makes it clear that automation is still being applied to isolated workflows rather than centrally managed. That is changing, however, as more organizations embrace platform engineering to manage DevOps workflows at scale, he added.

Overall, organizations are investing in DevOps automation over the next 12 months to improve security and compliance management (55%), infrastructure provisioning and management (52%) and performance optimization (51%).

The biggest barriers preventing organizations from automating new DevOps use cases are security concerns (54%), difficulty operationalizing data (54%) and toolchain complexity (53%). The average organization relies on more than seven different tools for DevOps automation, the survey finds.

As a result, the automation that has been put in place is often too brittle to effectively scale beyond a small DevOps team, noted Gunja.

A full 71% of respondents also used observability data and insights to drive automation decisions and improvements in DevOps workflows. However, 85% also admitted they faced challenges using observability and security data to drive DevOps automation. The top three challenges facing organizations included inaccessible data (51%), siloed data (43%) and the need for data to flow through many systems (41%).

Well over half (54%) said their organization is investing in platforms to enable easier integration of tools and collaboration between teams involved in automation projects.

Finally, 59% also anticipated large language models (LLMs) such as ChatGPT and Bard would have a significant impact on DevOps, with the top three benefits expected including increased productivity and reduced manual effort (57%), improved development, security and operations collaboration (56%) and enabling teams to generate code automatically (48%).

Many organizations that have embraced DevOps are now looking to strike a balance between the concept of developers owning every application or service they developed and the need to reduce the amount of toil developers now encounter, noted Gunja. Developers typically only spend about a quarter of their time writing code so there is a clear need to reduce the current level of cognitive load developers encounter as they maintain application development environments, he added.

The challenge is finding a way to reduce that load in a way that doesn’t overly restrict the ability of developers to embrace new tools and platforms that will enable them to build applications in a more innovative fashion. One of the primary reasons many development teams embraced DevOps in the first place was to not have the way they build applications dictated to them by a centralized IT team.

One way or another, DevOps workflows will continue to be increasingly automated. Less clear at the moment, however, is exactly who within an organization will be driving those initiatives.

September 28, 2023
Dynatrace Adds Generative AI Capabilities to Davis Engine

Dynatrace today revealed it has added generative artificial intelligence (AI) capabilities to its Davis engine it uses to apply machine learning algorithms to its observability platform.

Steve Tack, senior vice president for product management at Dynatrace, said that hypermodal approach to AI will allow DevOps teams to take advantage of probabilistic AI capabilities provided by Davis CoPilot that are enabled by large language models (LLMs) originally developed by OpenAI.

The generative AI capabilities effectively provide DevOps teams with a tool that surfaces workflow recommendations via a natural language interface. The recommendations are based on historical data such as sales and customer experience trends, seasonality and cloud application health. It is also able to analyze dependencies across large sets of data while retaining context accessed via the Dynatrace Grail data lakehouse and the Dynatrace Smartscape application visualization and mapping framework.

Dynatrace is now making a case for a multimodal AI platform that uses a common set of metrics, traces, logs, user behavior and topology to enable machine learning algorithms to predict anomalies and causal-AI dependencies to determine the root cause of an IT issue and then leverage generative AI to provide recommendations to resolve it. The company is forecasting IT teams will achieve 10% to 20% productivity improvements year-over-year as a result.

It’s too early to determine the full extent to which a multimodal approach to AI will transform IT management, but it’s clear that as IT operation teams become more productive, more time will be freed up for innovation, noted Tack. Many of the manual tasks that IT teams still need to perform divert time away from building and deploying additional applications, he added.

Naturally, there’s a lot of speculation concerning the impact AI will have on DevOps. Some contend it’s only a matter of time before the entire software engineering process is automated. As those advances are made, the cost of building applications will effectively drop to zero. Others argue that AI will augment DevOps teams in a way that makes it simpler to manage IT at scale using either the same number or possibly even fewer engineers.

The one thing that is certain is that AI will soon be pervasive, which will drive the adoption of automation frameworks as organizations look to operationalize the insights surfaced by AI platforms. As rich as the insights any AI model surfaces might be, they only provide real value when integrated with an automation framework, noted Tack.

Ultimately, most DevOps professionals will not want to work for organizations that don’t provide them with AI tools that make it more likely for them to be successful. Organizations that embrace AI may not have a sustainable competitive advantage when every other organization is doing the same, but those that don’t will soon be left behind. Those that succeed will benefit from managing application environments at a previously unprecedented level of scale at minimal additional cost. The challenge is determining which tasks will be automated with an eye toward planning how DevOps roles will need to evolve.

July 25, 2023
Dynatrace Survey Surfaces State of DevOps in the Enterprise

A global survey of 1,300 CIOs and DevOps managers working for organizations with more than 1,000 employees published today finds more than three-quarters of respondents work for organizations (78%) that deploy software updates into production every 12 hours or less, with more than half (54%) say they do so at least once every two hours. A total of 20% report they are deploying updates every minute or less.

Conducted by the market research firm Coleman Parkes on behalf of Dynatrace, the survey also finds 90% of respondents reporting that the pace of digital transformation within their organization has accelerated in the past 12 months. More than a quarter (26%) expect the pace of digital transformation to continue to accelerate, the survey found.

However, 55% of respondents work for organizations that make tradeoffs between quality, security and user experience to meet the need for rapid transformation. A total of 41% of respondents said they must sacrifice code quality and user experience due to meet demands for faster innovation, while more third (34%) also need to sacrifice code security.

Overall, DevOps teams, on average, spend nearly a third (31%) of their time on manual tasks involving detecting code quality issues and vulnerabilities that conspire to slow the pace of innovation, the survey found.

To address these issues, organizations plan to increase their spending on automation across development, security and operations by 35% by 2024, as they invest more in continuously testing software quality (54%) and securing production environments (49%), automatic vulnerability detection and blocking (41%) and automating release validation (35%). The average budget allocated to these activities in these large organizations is $9.1 million, the survey finds.

A full 88% of CIOs said the convergence of observability and security practices will be critical to building a DevSecOps culture, with 94% noting that extending a DevSecOps culture to more teams is key to accelerating digital transformation. Only 27% of respondents claimed to fully adhere to a DevSecOps culture today. The biggest obstacles to achieving that goal are that security teams don’t trust developers (55%), developers perceive security teams to be blockers of innovation (49%) and the silos that exist between teams (36%).

Organizations expect to increase their annual investment in DevSecOps automation by 2024 an average of 35%, but only a quarter (25%) said applying artificial intelligence to security was a priority. At the same time, only 28% of respondents said they are fully confident applications are fully tested for vulnerabilities before they are deployed.

A total of 90% said increasing the use of AI for IT operations (AIOps) will be key to scaling up these practices. However, 70% of respondents said they need to improve their trust in the accuracy of AI’s decisions before they can automate more of their continuous integration/continuous delivery (CI/CD) pipeline.

Bob Wambach, vice president of product marketing for Dynatrace, said there is a clear correlation between that accelerated pace of digital transformation and a need to deliver more frequent software updates that is driving those investments. Most organizations, however, are going to find it difficult to achieve all their goals unless they break down the silos that prevent them from automating and observing modern DevSevOps workflows, he added. Achieving that goal will also require DevOps teams to reevaluate which platforms they are employing as they move to reduce the current level of friction encountered when building and deploying applications, he added.

It’s too early to say just how soon DevSevOps workflows will define the next era of software development. One way or another, it’s certain that every organization that embraces DevOps will need to address lingering application security issues.

January 24, 2023
Dynatrace Adds Grail Data Lakehouse to Observability Platform

Dynatrace today announced it has added a data lakehouse, dubbed Grail, to the Dynatrace Software Intelligence Platform to make it simpler to aggregate all the observability data an IT team collects.

Bob Wambach, vice president of product marketing for Dynatrace, said Grail will initially drive analytics of log data but in time will also be applied to additional types of application development, security and business intelligence data.

The goal is to streamline processes such as data indexing and rehydration across a unified observability platform that eliminates the need to maintain multiple disparate repositories, he added.

Grail, at its core, is a causational data lakehouse based on a massively parallel processing (MPP) analytics engine that will initially be made available on the Amazon Web Services (AWS) cloud. IT teams can use the Dynatrace Query Language (DQL) to query data stored in the data lakehouse. Additionally, the Davis artificial intelligence (AI) engine that is already embedded within the Dynatrace Software Intelligence Platform will be able to apply machine learning algorithms to the data stored in Grail.

Data lakehouses have recently emerged as an approach to enable organizations to apply the structure of a data warehouse platform to massive amounts of data that previously would have been stored in a data lake. In effect, a data lakehouse combines the attributes of both types of platforms for centralizing the management of data. One of the biggest issues with any approach to observability is the massive amount of data that winds up being stored to allow DevOps teams to track issues over time.

Of course, it’s up to each individual IT team to determine how much data they want to save to drive analytics, but the more data they save the more cloud storage costs will naturally increase. Grail provides a purpose-built means of consolidating data silos in a way that reduces the total cost of observability, noted Wambach.

In general, because of the need to embed a data lakehouse capability, most observability platforms will be consumed as a software-as-a-service (SaaS) application. That means the number of IT teams that will likely build and maintain their own observability platform will be limited. IT teams are also now struggling to manage increasingly complex IT environments made up of both cloud-native applications built using microservices and legacy monolithic applications. In fact, a global survey of 1,303 CIOs and senior IT practitioners commissioned by Dynatrace found 71% of respondents believed the explosion of data produced by cloud-native technology stacks is beyond the ability of IT staffs to manage without help from an AI platform.

It’s not clear how quickly IT teams will be migrating from legacy monitoring tools that are designed to track specific metrics to observability platforms that promise to make it easier to identify the root cause of an issue by simplifying the launch of queries against a massive pool of data. However, as organizations continue to become more dependent on software, it’s apparent that the level of tolerance for any kind of disruption is steadily declining; it’s now more a question of when rather than if a different approach to managing IT will be required.

October 4, 2022
Dynatrace Extends Reach of Application Security Module

Dynatrace has extended the Application Security Module it provides for its observability platform to protect against vulnerabilities in runtime environments, including the Java Virtual Machine (JVM), Node.js runtime and .NET CLR.

In addition, Dynatrace has extended its support to applications built using the Go programming language.

The Dynatrace Application Security Module leverages existing Dynatrace tracing and artificial intelligence (AI) technologies to detect threats in real-time and then apply appropriate policies to automatically secure applications. At the core of the Dynatrace approach to application security is its OneAgent software for application instrumentation, which collects data and feeds it back to an observability platform that includes an AI engine dubbed Davis.

Amit Shah, director of product marketing for application security at Dynatrace, said by extending the Application Security Module to support runtimes in the most widely adopted programming languages Dynatrace is now providing real-time visibility and vulnerability analytics across the entire application stack, which includes custom code, open-source and third-party libraries, language runtimes, container runtimes, and container orchestrators.

The Dynatrace platform not only identifies vulnerabilities across each of these layers automatically but also analyzes them to reduce false positives by only surfacing alerts concerning code that is actually running, noted Shah. Without those capabilities, it becomes impossible for development teams to prioritize their application remediation efforts in the absence of any context, he added.

In general, achieving and maintaining application security will require more responsibility to shift both left and right, said Shah. While it’s critical to discover as many vulnerabilities as possible before applications are deployed it’s also crucial to make it simpler to discover vulnerabilities in runtimes after an application is deployed, he noted. The goal is to enable DevOps teams to employ the same observability platform they are using to manage and optimize applications to also ensure application security, Shah added.

That Dynatrace approach provides the added benefit of also avoiding the need to convene a “war room” to discover where all the vulnerabilities in those application environments might reside once they are discovered, Shah noted.

The primary application security challenge organizations face is the rate at which applications are now being deployed. Cybersecurity teams, because of a chronic shortage of talent and expertise, can’t keep pace. As a result, more responsibility for application security is being shifted left toward developers and DevOps teams as well as right toward IT and security operations teams. In effect, application security is finally becoming a team sport within IT organization as cybersecurity teams focus more of their efforts on securing runtime environments, noted Shah.

It may be a while before that approach has a major impact in terms of reducing the number of vulnerabilities being regularly introduced into application environments. However, as more tools and platforms that specifically address application security are deployed, the overall state of application security will steadily improve.

In the meantime, however, the volume of attacks aimed at the application layer of the IT stack will, unfortunately, continue to increase as cybercriminals focus more of their efforts on exploiting both now and zero-day application vulnerabilities that are increasing in volume with each passing day.

August 15, 2022
Dynatrace Embeds DX Monitoring in Observability Platform

Dynatrace has enhanced its analytics capabilities to enable digital experience monitoring, including session replays, based on the logs, metrics and traces it collects via its observability platform.

Steve Tack, senior vice president of product management at Dynatrace, said this update makes it simpler for DevOps teams to track user journeys across the multiple components and microservices that make up a modern application environment. Rather than trying to, for example, determine the impact of an issue by parsing through log data without any context, Tack said Dynatrace’s analytics capabilities will automatically fire up a session replay to enable DevOps teams to view the exact nature of the application issue being experienced.

Previously, DevOps teams would have needed to deploy the Dynatrace session replay offering separately. The overall goal is to remove much of the friction DevOps teams encounter when trying to fix an application issue by providing context in a way that is more accessible, he added.

In general, as observability continues to advance, it’s now become much less about the amount and types of data that can be collected, said Tack. The data is table stakes for achieving observability, he added. The focus now needs to be on the richness of the actionable analytics being surfaced once that data is collected, he said.

In fact, as more organizations start to build and deploy applications based on highly interdependent microservices, the richness of the analytics is now more critical than ever, noted Tack. While microservices-based applications are more resilient than monolithic applications, troubleshooting them is more challenging whenever application performance degrades, he added.

It’s not clear at what rate DevOps teams are embracing observability. The concept has always been a core DevOps principle, but, for the most part, more mature DevOps teams have only been able to achieve what amounts to continuous monitoring of pre-defined metrics. Observability platforms promise to surface anomalies indicative of IT issues before they escalate; DevOps teams can then launch queries to better determine their root cause and potential severity.

Dynatrace is making a case for an observability platform, accessed as a cloud service, that employs the company’s Davis AI engine and machine learning algorithms to surface the root cause of application issues.

As observability platforms continue to evolve, the days when IT teams determined the root cause of an IT issue by process of elimination are finally coming to an end. DevOps teams will be gaining more visibility into their application environments in ways that surface more meaningful actionable intelligence. That intelligence is especially critical for externally-facing applications driving digital business transformation initiatives.

There is, of course, no shortage of options when it comes to observability platforms. In some cases, IT organizations are opting to rely on cloud services while others are extending DevOps platforms they built themselves and continue to maintain. Regardless of the approach, the issue will no longer be how to collect data as much as what to do with it once it’s collected.

July 21, 2022