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Tag: Multi-cloud

What I Learned Building Cloud-Portable Services Across Multiple Providers

Most teams I’ve worked with start their multi-cloud journey with a clear business case and a tidy architecture diagram. Within weeks, the diagram stops matching reality. The development experience across cloud providers is fragmented in ways that no amount of upfront planning fully prepares you for.

After building services across AWS, GCP, and Alibaba Cloud at several companies, I’ve seen a consistent pattern: teams start these journeys aiming for portability and get derailed by semantics. A 2024 Gartner report found that over 92% of large enterprises now operate in multi-cloud environments, which means the scale of this problem is enormous. Yet the real engineering challenge, reconciling the behavioral differences across cloud services, is far more subtle than most architecture discussions acknowledge.

Where Semantic Differences Actually Bite

Each cloud provider builds services with different assumptions about how operations should behave. Consider something as basic as deleting an object that doesn’t exist. One major provider returns a success response; another returns a 404 error. This might seem trivial until you realize every service team writing cloud-portable code must handle this semantic difference, along with dozens of others, independently.

Pagination tells a similar story. Some platforms provide explicit continuation tokens. Others rely on building cursors from the last document returned. These aren’t bugs to fix, they’re fundamental design decisions baked into each platform. Without abstraction, teams independently implement provider-specific code paths, duplicating effort and creating technical debt that compounds over time.

Why Raw REST APIs Fall Short

Previous attempts at multi-cloud abstraction, including projects like Apache jclouds, took a REST-based approach. The logic was straightforward: REST is the shared layer. But in practice, t cloud providers evolve their APIs constantly, adding features, changing authentication flows, and optimizing performance. REST-based abstractions inevitably lag behind, leaving gaps that grow wider with each provider update.

Building on official provider SDKs changes this dynamic. Cloud vendors invest heavily in their SDKs: they handle request signing, header management, retry logic, timeout handling, and endpoint discovery. Leveraging that work means an abstraction layer can focus on normalization rather than infrastructure plumbing. When providers update their SDKs, those improvements flow through automatically rather than requiring manual catch-up work.

Layered Architecture for Provider Abstraction

The architecture pattern that I’ve found works best separates concerns into three layers. A portable client layer gives developers stable, cloud-neutral APIs. A driver layer handles validation and coordination. Provider-specific implementations sit underneath, normalizing behavior by translating status codes, unifying pagination patterns, and standardizing error handling. Application teams interact only with the top layer; provider complexity stays hidden below.

The approach aligns with the broader shift in how organizations manage infrastructure complexity. Internal developer platform adoption rose from 23% in late 2024 to 27% in 2025, according to industry surveys, reflecting growing recognition that developers need insulation from platform-level details. Multi-cloud SDKs serve a parallel function at the provider level: creating consistent interfaces that let engineers focus on business logic rather than cloud-specific integration work.

The tradeoff between portability and provider-specific capabilities ultimately comes down to cost leverage versus business-critical functionality. A useful framework: roughly 90% of cloud services are commodities (compute, object storage, pub/sub messaging) standardized enough across providers that abstraction makes sense. The remaining 10% represent genuine differentiators. When a provider-specific feature offers a significant cost advantage or competitive edge, teams should embrace native integration rather than forcing it through an abstraction layer. The goal isn’t portability for its own sake; it’s making deliberate choices about where portability matters and where specialized capabilities justify the tradeoff

Testing Across Providers

Ensuring uniform behavior across providers requires rigorous conformance testing. The most effective approach involves writing tests against abstract driver classes, then running the same suite against each provider’s implementation. Any behavioral deviation surfaces immediately. The challenge is running these tests in CI environments where managing live credentials for multiple cloud providers introduces security risks.

One solution involves using tools like WireMock as a forward proxy, recording real HTTP transactions on developer machines with credentials, then replaying them in CI. This enables reliable integration testing without exposing production secrets while still validating actual request and response flows.

What the Industry Still Gets Wrong

Surveys consistently show that vendor lock-in ranks among the top concerns driving multi-cloud adoption, with 86% of enterprises citing flexibility as a primary motivation. But that strategic intent rarely translates into developer tooling. The gap between what cloud architects plan and what development teams can actually execute remains a persistent friction point, and most multi-cloud strategies are written at the infrastructure level, where the conversation is about regions, availability zones, and failover policies. The developer experience rarely enters that discussion until teams are already deep into implementation.

Provider-level abstraction layers help close that gap, but they require deliberate investment. The ability to switch clouds by changing configuration rather than rewriting code sounds simple in a design review. Getting there means confronting semantic differences one operation at a time, building conformance tests that run across every provider, and accepting that some provider-specific features are worth keeping outside the abstraction. The work is unglamorous and detail-heavy. It’s also what separates a multi-cloud strategy that exists on paper from one that works in production.

September 24, 2026
Training IT Teams for Multi-Cloud DevOps Environments

Multi-cloud is mainstream. According to recent data, 89% of companies now use a multi-cloud approach for everything from day-to-day workloads to data storage, server backups and in-depth analytics.

While public clouds offer the advantage of on-demand resources and private clouds deliver enhanced control, combining public and private performance — or multiple instances of both — helps businesses maximize their cloud investment.

A multi-cloud approach can also benefit development and operations (DevOps) teams if they have the training and skills necessary to maximize multi-cloud potential.

Starting Strong: Three Tips for Building a Better DevOps Team

By completing development and operations tasks simultaneously, DevOps teams set the stage for faster deployments with fewer problems.

Below mentioned are three tips for starting strong.

Identify Tech Talent

DevOps functions differently than traditional IT teams. Instead of familiar ‘waterfall’ models that take a step-by-step approach to design, development, deployment and assessment, DevOps teams complete these tasks simultaneously.

As a result, it is critical to find staff with tech talents that facilitate this model or upskill existing staff to help enable DevOps. In practice, this means looking for staff who are capable of thinking outside the box by developing creative answers to common problems and those who are willing to experiment to see what works, rather than sticking with what they have always done.

Set Clear Expectations

Clear expectations are also critical. The purpose of DevOps is to streamline the deployment of new applications and services, but the collaborative and continuous nature of this approach makes it easy for teams to get off track, especially as the number of apps used by business teams grows exponentially.

To reduce the risk of projects started and not finished, companies must set clear goals, including key milestones and deliverable dates.

Create a Collaborative Culture

DevOps dies in isolation. Siloed services and challenges in communication frustrate teams’ ability to iterate and improve on deliverables. To ensure DevOps lives up to its potential, companies must prioritize open collaboration that prioritizes communication over being ‘correct’.

In many cases, this requires a more hands-off approach from managers and executives. While it is important to regularly check in with DevOps teams, the nature of this collaborative approach means they are better off experimenting and iterating.

Key Skills for Effective Multi-Cloud Management

Multi-cloud environments can enable DevOps efforts. For example, companies might keep critical data on-site or in private clouds to maximize security but use public cloud resources for large-scale application testing.

The caveat? Managing multi-cloud solutions is naturally more complex than handling their single-cloud counterparts. To ensure DevOps teams are prepared to maximize multi-cloud, training is essential.

The Challenge? Complexity

The Solution: Cloud Platform Specializations

While cloud platforms share core functionality, how users interact with and apply these functions differs significantly across providers. Google Cloud, Microsoft Azure and Amazon Web Services (AWS) all provide powerful, interoperable cloud platforms but these platforms are not identical, creating the potential for increased complexity.

For example, while AWS is known for its broad service offerings, Google Cloud has emerged as a leader in data processing and artificial intelligence (AI). Microsoft Azure, meanwhile, provides enhanced platform-as-a-service (PaaS) offerings and seamless integration with Windows and other Microsoft products.

To effectively leverage multiple clouds, platform specializations are critical. This makes Google Cloud, Azure and AWS training essential for DevOps teams.

The Challenge? Effective Integration

The Solution: Containerization Proficiency

For DevOps teams to see success, applications and services must work across multiple platforms with minimal need for adjustment. Containers make this possible; by keeping application code isolated from the larger cloud environment, apps and services become portable, allowing teams to quickly build, test and deploy new solutions across multiple clouds.

This creates the need for comprehensive container proficiency. Common containerization tools include Kubernetes (K8s), Docker, OpenShift and the Amazon Elastic Compute Cloud. Experience and training with multiple container tools helps empower solution portability.

The Challenge? Limited Observability

The Solution: Monitoring Mastery

While multi-clouds increase functionality, they naturally decrease observability. This can lead to both performance and security issues, for example, applications may be secure in one cloud but exposed in another. In the best-case scenario, the issue is caught early and teams spend time fixing the problem. Worst-case? Attackers exploit the oversight to compromise company data, with teams unaware until performance issues emerge.

To improve observability and reduce risk, teams need training in real-time monitoring tools. Look for solutions that provide real-time data analysis, in-depth dashboards and customizable alerts to help identify small issues before they become big problems.

Making the Most of Multi-Cloud

Much like DevOps, multi-cloud approaches combine the best of both worlds to deliver improved outcomes.

Just like DevOps, the right strategy is key to making the most of multi-cloud. This starts with purpose-built teams given clear expectations and backed by a collaborative culture. It is enhanced by skills and training that help DevOps teams reduce complexity, improve integration and enhance observability across multiple cloud platforms.

February 12, 2025
How to Manage Multi-Cloud Infrastructure and Reduce Costs

It’s no secret that the public cloud has been a game changer for businesses. In today’s world, every organization is looking for ways to store data more efficiently, support agile application development and reduce the burdens associated with on-premises data centers.

The public cloud is essential for meeting those objectives and achieving the higher degree of innovation and digital transformation that many organizations seek. While the shift to the cloud has unlocked unprecedented innovation, it has also created a new layer of complexity and sometimes unexpected costs.

The Nutanix Enterprise Cloud Index found that nearly every organization (99%) has moved one or more applications to a different IT infrastructure in the past year, and the vast majority (86%) agree that moving applications is both complex and costly. Accordingly, 56% plan to focus on gaining more visibility into their cloud consumption and costs this year, while 52% plan to leverage solutions that minimize cloud waste.

As Scott Sinclair, senior analyst for TechTarget’s Enterprise Strategy Group explains, “The typical enterprise now has multiple data centers and public cloud providers, and many are also using lots of different colocation facilities and multiple edge locations.” As such, “It presents a new paradigm with companies having to look at different factors and considerations like cost, portability and security because their data is no longer all locked in one room.”

Organizations must carefully plan their cloud strategy to avoid getting bogged down by complexity and sticker shock. But in most cases, this is easier said than done. The majority of companies are not using a single environment — their infrastructure includes some combination of on-premises and public cloud resources. One of the challenges this creates is additional expenses and complexity associated with data mobility and data protection: Moving data around so that it’s available where and when it’s needed while simultaneously keeping it safeguarded requires careful planning and budgeting. In other words, data has gravity, and unless data movement and orchestration are planned, one can end up stranded when the primary location for a dataset is unavailable.

Even companies that were born in the cloud have their work cut out for them. Managing different cloud environments might be relatively straightforward for a cloud-native organization initially, but if at some point their data needs to be relocated on-premises to address compliance, data sovereignty issues or budget, it can result in soaring costs if not properly executed.

With this being said, here are four considerations CIOs should take into account to keep cloud complexity and costs in check from the start.

1. Have Your Head in the Cloud but Your Feet on the Ground

To reap the benefits of the cloud, organizations need to adjust their expectations and plan realistically. There’s a false perception that companies can run an application on AWS in the morning, Azure in the afternoon, and Google Cloud Platform at night. In actuality, multi-cloud is a medium-to-long-term strategy that occurs over months or years. Building a cloud operating model with this in mind lets organizations benefit from the flexibility of leveraging multiple clouds while avoiding the cost and complexity associated with trying to do too much too fast with the cloud. When appropriate expectations are in place, hybrid multi-cloud is achievable, affordable, and undoubtedly beneficial.

2. Learn on Others’ Dime as Much as Possible

There are several best practices organizations should follow as they begin their hybrid multi-cloud journey. These include things like ensuring key application capabilities have API wrappers, containerizing modern applications as much as possible so they are portable, and automating application deployments through CI/CD pipelines to avoid manual errors and reduce costs. Public clouds have been around for almost 15 years, and designing for the public cloud is now quite well understood across the industry. Still, companies need to be mindful of how they leverage public cloud for certain applications or workloads. For example, modernizing a legacy application portfolio might sound like a good idea in theory, but in reality, it is cost-prohibitive and it will not provide ROI —lift and shift is a much better option for legacy applications.

3. Standardize, Automate and be Paranoid

Public cloud environments are fundamentally different from traditional on-premises infrastructure, so organizations need to think about them differently—especially where security is concerned. While a fortified exterior with unfettered internal freedom might be a workable model for on-premises, the same model could spell disaster in the cloud. Host-based security approaches have become more critical in the public cloud. Automation and standardization ensure that security measures are applied consistently across all cloud resources and services within an organization and shorten the window to detect unauthorized behaviors. Organizations must also consider the non-technology aspect of this: training and education for their teams. All teams and departments must be on the same page when it comes to security so they can collaborate effectively on security-related initiatives.

4. Build in Optionality From Day One

The public cloud is by far the best place to begin innovation. However, as companies experiment in the public cloud and a smaller portfolio of applications gain traction, enterprises need to adjust to the reality that — for the vast majority of applications — the public cloud will always be more expensive than running them on-premises (or perhaps even another cloud). To avoid lock-in, enterprises must build in optionality for migrating their compute when needed, as well as the mechanism to move their data out of public clouds when needed. Organizations that are flush with cloud engineering talent can do this on their own, but the vast majority will require an underlying platform that’s explicitly designed for data and computation mobility.

The perks of one platform to run all workloads across clouds, on-premises, and the edge are undeniable: From increased flexibility to optimized performance, compliance, agility and more. By heeding the advice above, companies can keep multi-cloud challenges like complexity and unexpected costs at a minimum and fuel innovation.

February 1, 2024
Navigating the Multi-Cloud Ecosystem

Multi-cloud infrastructure, an approach where organizations utilize services from multiple cloud providers, is rapidly gaining traction in the tech world.

In this article, we will unpack the concept of multi-cloud infrastructure, examining how it enables businesses to diversify their cloud ecosystems for enhanced performance, greater flexibility and improved risk management.

Understanding the Multi-Cloud Ecosystem

Multi-cloud environments represent a strategic approach in cloud computing where businesses utilize services from multiple cloud providers to meet diverse operational needs.

This concept differs from hybrid cloud, which typically blends both private and public cloud infrastructures, aiming to offer a balance between the two under a single unified environment.

In contrast, a multi-cloud strategy involves using distinct cloud services – for instance, one provider for storage, another for computing power and another for analytics – without necessarily integrating them into one cohesive system. This approach allows organizations to leverage the unique strengths and offerings of various cloud providers, thereby optimizing their IT infrastructure for efficiency, cost-effectiveness and enhanced performance.

The Advantages of a Multi-Cloud Approach

Adopting a multi-cloud approach brings several key benefits, the biggest among them being enhanced resilience, flexibility and the avoidance of vendor lock-in.

By distributing resources and workloads across multiple cloud environments, businesses can mitigate risks associated with relying on a single cloud provider, such as service outages or data loss. This diversity ensures continuous operation and robust disaster recovery capabilities.

Additionally, a multi-cloud strategy empowers organizations with the flexibility to select the best services that each cloud provider offers, tailoring their IT infrastructure to specific needs and operational demands. This tailored approach enables businesses to optimize both performance and cost. Different providers may offer competitive pricing for specific services, allowing companies to choose the most cost-effective solutions for their requirements.

Moreover, by avoiding vendor lock-in, organizations retain the freedom to switch providers as needs evolve or as newer, more advantageous services become available. This strategic utilization of multiple clouds ultimately leads to a more efficient, agile and economically sound IT infrastructure.

Key Challenges in Multi-Cloud

Multi-cloud management comes with its own set of challenges, particularly in terms of complexity, data security and compliance. DevOps teams often grapple with the complexity of managing disparate cloud services, each with unique features and interfaces, making seamless integration and consistent operation a demanding task.

Data security is another critical concern, as safeguarding sensitive information across multiple platforms requires robust and adaptable security measures. Additionally, ensuring compliance with varying regulations across different cloud environments adds another layer of complexity.

These challenges necessitate that DevOps teams are technically proficient and adept at strategic planning and coordination to manage a multi-cloud ecosystem effectively.

Best Practices in Multi-Cloud Deployment and Management

When managing and deploying within a multi-cloud ecosystem, DevOps teams can greatly benefit from the following five best practices:

1. Strategic Cloud Service Selection: Carefully assess and choose cloud services based on specific workload requirements, performance metrics and cost-effectiveness to ensure optimal deployment.
2. Centralized Monitoring and Management: Utilize centralized tools for monitoring and managing different cloud environments to maintain consistency and control and to streamline operations.
3. Consistent Security Posture: Apply uniform security policies and practices across all cloud platforms to protect data integrity and comply with regulatory standards.
4. Automation: Automate repetitive and routine tasks across different clouds for efficient resource utilization, reduced errors, and faster deployment cycles.
5. Cost Management and Optimization: Continuously monitor and manage cloud spending across platforms to avoid cost overruns, ensure efficient use of resources and follow cloud cost optimization best practices.

Building a Multi-Cloud Strategy: A DevOps Perspective

From a DevOps perspective, building a comprehensive multi-cloud strategy involves aligning cloud infrastructure with business objectives and core DevOps principles.

The first step is clearly defining business goals and identifying how different cloud environments can support these objectives. This should be followed by selecting cloud services that best match the specific needs of each workload, considering factors like performance, cost and compliance requirements. Integrating continuous integration and continuous deployment (CI/CD) processes is crucial in a multi-cloud setup.

This integration ensures software can be developed, tested and deployed rapidly and reliably across different cloud platforms. The strategy should also include implementing robust automation and monitoring tools to maintain consistency and efficiency across the cloud.

By focusing on these elements, a DevOps team can develop a multi-cloud strategy that not only aligns with the organization’s goals but also adheres to the agile and efficient ethos of DevOps methodologies, ensuring seamless operation and scalability in the multi-cloud environment.

Wrapping Up

In conclusion, effectively navigating the multi-cloud ecosystem is crucial for DevOps success, as it offers unparalleled flexibility, scalability and resilience in managing diverse cloud resources. DevOps professionals are encouraged to embrace multi-cloud strategies as they enhance operational agility and open avenues for innovation and improved service delivery.

January 16, 2024
Implementing Data Analytics in Multi-Cloud Environments

Multi-cloud data analytics is more intricate, demanding a nuanced approach to ensure seamless operations and accurate insights. Coordinating analytics across multiple cloud providers introduces various issues, requiring careful consideration of implementation strategy. From cost management to performance variability and data orchestration, we will explore each challenge in detail and provide actionable insights on how to effectively navigate these hurdles, ensuring fool-proof data analytics infrastructure in multi-cloud solutions.

Interoperability and Standards

Interoperability challenges arise in multi-cloud data analytics due to varying APIs and standards across cloud providers. The lack of uniformity in data formats and communication protocols hinders seamless collaboration and data exchange. To address this, organizations should prioritize cloud services adhering to open standards, and advocate for industry-wide interoperability initiatives. Additionally, create an abstraction layer or middleware that acts as a bridge between different cloud providers. This abstraction layer helps standardize interactions, facilitating seamless data exchange and reducing dependencies on specific provider APIs. Thus, you enhance the flexibility of your multi-cloud environment, making it easier to integrate and switch between different cloud platforms.

Vendor Lock-In

Organizations risk becoming overly dependent on a single vendor, limiting flexibility and making migration complex. Utilizing specialized features or tools from one provider may face difficulties transitioning to another, which restricts the ability to choose more cost-effective or suitable solutions and may result in increased switching costs. To mitigate this, a multi-cloud strategy should be adopted, favoring standardized technologies and open APIs, allowing organizations to distribute workloads across multiple cloud providers and avoid being tied to a specific vendor’s ecosystem, enhancing flexibility and strategic decision-making. Design architectures that leverage abstraction layers and avoid tightly coupling with provider-specific services. This approach ensures flexibility, allowing your organization to choose and switch between cloud providers based on performance, cost and strategic considerations while minimizing the risk of vendor lock-in.

Cost Management

Each provider has unique pricing models and billing mechanisms, making it difficult for organizations to accurately estimate and compare costs. The dynamic nature of data analytics workloads, coupled with data transfer expenses between clouds, contributes to the complexity of budgeting. To cope with that, implement a centralized cloud cost management tool to monitor expenses across providers. Also, regularly review and optimize resource utilization to identify underutilized or over-provisioned instances. You may leverage reserved instances or spot instances for cost-effective computing and implement automation for scaling based on demand, ensuring resources are allocated efficiently. Lastly, negotiate pricing with cloud providers and explore discount options to secure favorable terms for long-term commitments.

Performance Variability

As providers have unique infrastructure, network configurations and service levels, inconsistent performance levels may occur and impact the reliability and predictability of analytics results. To mitigate these consequences, conduct thorough performance testing across different cloud environments, optimize queries for specific platforms, and consider workload distribution based on provider strengths. Utilizing performance monitoring tools helps track and analyze variations, enabling timely adjustments. Choosing cloud providers based on specific performance requirements and establishing performance service level agreements (SLAs) also mitigates the impact of performance variability in multi-cloud data analytics.

Resource Orchestration

Due to differences in orchestration tools, APIs, and resource management mechanisms cloud providers employ, compatibility issues often take place. The heterogeneity makes it complex to ensure seamless deployment, scaling, and management of analytics workloads. Organizations may consider adopting cloud-agnostic orchestration tools and frameworks to abstract the underlying differences among cloud providers, allowing for consistent resource management. Embracing infrastructure as code (IaC) practices and automation also streamlines resource orchestration, enabling organizations to deploy and manage analytics workloads more efficiently across various cloud environments.

Data Movement and Latency

Varied network architectures, geographical distances, and differing data transfer mechanisms contribute to increased latency and potential performance issues. Transferring significant datasets across clouds incurs additional costs and can lead to operational inefficiencies. To avoid that, organizations should optimize data movement processes by leveraging efficient transfer protocols, considering edge computing for proximity to data sources, and strategically distributing workloads based on data location. Implementing caching strategies and utilizing content delivery networks (CDNs) can help mitigate latency challenges, ensuring a more responsive and cost-effective multi-cloud data analytics environment.

As a Footnote

In conclusion, the journey of implementing data analytics in multi-cloud environments presents its unique set of challenges. Embracing open standards, adopting cloud-agnostic tools and fostering a culture of continuous improvement are essential components of overcoming them. Continuous monitoring, regular updates to governance frameworks, and strategic decision-making aligned with your business specifics and objectives are keys to reaping maximum benefits from multi-cloud data analytics. You may also take advantage of cloud computing consulting to get a tailored strategy from experts.

January 16, 2024
Key Cost Optimization Strategies for Multi-Cloud Environments
In today’s ever-changing technology landscape, businesses are realizing the advantages of multi-cloud environments. These environments allow organizations to tap into various cloud platforms and use their services as required. This change signifies a shift away from relying on just one cloud provider. Businesses now embrace the flexibility and adaptability that come with using multiple clouds simultaneously.

In complex multi-cloud environments, it is crucial to manage costs effectively. It is important to strategically optimize costs while considering your organization’s specific needs. By utilizing tools, implementing processes and continuously monitoring costs, you can save money while still reaping the benefits offered by different cloud providers.

What is Multi-Cloud?

A multi-cloud environment describes a setup in which companies or individuals utilize cloud computing platforms or providers to fulfill their computing requirements. In a multi-cloud setup, organizations have the ability to integrate both public clouds (such as Amazon Web Services, Microsoft Azure or Google Cloud Platform) and private clouds (on-premises or hosted private clouds) to establish a diverse and adaptable computing infrastructure. Rather than relying on one cloud provider, multi-cloud strategies distribute workloads across several cloud environments. Rather than depending on a single cloud provider, a multi-cloud strategy involves distributing workloads across various cloud environments.

Benefits and Challenges

Multi-cloud environments offer organizations numerous benefits, including flexibility, reliability, scalability and cost optimization. When organizations distribute their workloads across various cloud platforms, they reduce the chances of downtime or service disruptions due to a single provider failure. This guarantees uninterrupted availability and minimizes any potential impact on business operations.

It is quite challenging to manage a multi-cloud environment for several reasons. Organizations must tackle challenges related to governance, cost management, integration and compatibility. To overcome these obstacles effectively, it is important to implement a well-defined strategy with efficient management tools.

Since each cloud provider comes with its own set of tools, APIs and policies, ensuring consistent governance across different cloud platforms is challenging. Since different cloud providers have differing pricing structures, it is quite difficult to monitor and optimize costs in a multi-cloud environment.

Analyzing and understanding each provider’s cost model is necessary when tracking usage patterns across different platforms. Integrating services from different cloud providers into a cohesive system architecture can be challenging due to compatibility issues. Careful planning and expertise are needed to achieve seamless integration without conflicts or performance problems.

The Importance of Cost Optimization in Multi-Cloud Environments

In today’s evolving technology landscape, businesses need to make the most of various cloud environments to fully utilize their cloud capabilities. However, great power entails great responsibility and cost optimization is a critical aspect that should not be overlooked.

As businesses expand their cloud presence across multiple providers, optimizing costs becomes necessary to maximize ROI and stay competitive in today’s fast-paced market. In a multi-cloud environment, organizations typically use several cloud platforms simultaneously, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), to reduce expenses and maximize efficiency by comparing the services, features, and pricing models offered by each of the providers.

Multi-cloud environments offer opportunities for cost optimization by allowing organizations to choose the cost solutions tailored to their specific needs. With access to pricing models and options offered by providers businesses can optimize their expenditure based on workload requirements.

Optimizing Costs in Multi-Cloud Environments

To optimize costs in multi-cloud setups, one must carefully analyze and manage expense factors across providers and services. It involves understanding cost structures, pricing models and usage patterns while considering workload requirements to choose cost-effective options for different tasks. Achieving these financial benefits requires efficient resource allocation and minimizing unnecessary expenses.

This section discusses the key strategies for optimizing costs in a multi-cloud environment.

Automating Resource Management

You can optimize costs in a multi-cloud environment by automating the management of resources. Efficiently optimizing costs in cloud environments relies heavily on automating resource management. This enables organizations to monitor usage levels and resources dynamically as required, effectively implement cost-saving policies and ensure optimal utilization of cloud services without the need for human intervention.

Organizations can automate their processes and tasks to streamline their operations, thereby saving time and reducing the risk of overspending on unnecessary resources. To effectively handle resources, it is crucial to have monitoring tools that offer real-time observations on usage patterns and performance metrics. These tools enable businesses to identify underutilized resources, which can then be automatically shut down or adjusted in size as required.

Automation also involves implementing automatic resource allocation capabilities based on demand fluctuations. By scaling up or down depending on workload requirements, organizations can optimize costs by ensuring they only pay for the resources they truly need at any given time. By implementing these automated policies, organizations are able to enforce cost optimization practices. For example, by establishing guidelines for assigning metadata tags to resources, organizations gain visibility and control over expenditure across various cloud platforms.

Additionally, scheduling non-production environments to automatically shut down during off-business hours can significantly reduce costs. By turning off development and testing environments when they’re not in use, companies can avoid paying for resources during periods of low activity.

Analyzing Costs in Multi-Cloud Environments

While analyzing costs in a multi-cloud environment is challenging, it is a crucial strategy for your organization to make the most of its resources and avoid unnecessary expenses. It’s important to understand usage patterns and expenditures to optimize costs effectively. Start by gaining visibility into spending across all clouds. Use cost management tools provided by the cloud platforms or consider third-party solutions that consolidate data. This helps track costs at a detailed level and identify areas of high expenses. Analyze usage patterns to find instances that can be resized or turned off when not needed. By adjusting resources based on demand, overprovisioning can be avoided and unnecessary costs reduced.

Here are the key steps to analyze costs in a multi-cloud environment:
- Identify and categorize workloads
- Track usage patterns and cost
- Analyze the usage patterns of your workloads
- Automate cost optimization
- Incorporate cost analysis into your decision-making process
- Review cost optimization strategy regularly
Use Spot Instances

In multi-cloud environments, using spot instances is crucial for cost optimization. These instances are available from cloud providers at a much lower price than on-demand or reserved instances, leading to substantial cost savings for organizations. With spot instances, you can take advantage of unused or idle capacity on cloud providers’ infrastructure, resulting in substantial cost savings. Being strategic with spot instances provides economical access to compute resources. Cloud providers offer discounted spare resources in the absence of regular customer demand, making it favorable compared to on-demand or reserved instances. Use on-demand or reserved instances for critical tasks while employing spot instances for less critical workloads. To optimize costs, you should leverage on-demand, spot and reserved instances, i.e., a mix of all of them.

Right-Sizing and Efficient Resource Allocation

Rightsizing is a key strategy for cost optimization that reduces costs and enhances performance in multi-cloud environments, leading to substantial savings. One of the most important steps in this regard is capturing comprehensive data on resource utilization (CPU, memory, storage and network utilization). By identifying over-provisioned or underutilized instances, organizations can optimize resource allocation. Automated tools simplify this process by monitoring and recommending adjustments. Consolidating workloads into fewer instances and leveraging virtualization technologies or containerization platforms further improves performance and scalability. Right-sizing and resource allocation are two key techniques that can help optimize costs in a multi-cloud setup.

Here are the key strategies at a glance:
- Analyzing workload requirements
- Selecting the appropriate instance types
- Regularly review and adjust resource allocations based on actual usage patterns
- Implementing auto-scaling and load-balancing mechanisms
- Utilizing resource tagging and monitoring tools for cost visibility
- Right-sizing resources on demand
- Automated scaling strategies
Conclusion

By implementing key strategies such as using reserved instances or savings plans, automating resource management, implementing rightsizing techniques, leveraging spot instances, optimizing data transfer and storage, applying automation and orchestration principles, practicing financial governance and budget management and keeping an eye on future optimizations, businesses can effectively control their cloud spending and ensure they are making the most of their multi-cloud investments while minimizing costs.

However, it’s important to acknowledge that cost optimization in cloud environments comes with its own set of challenges. Managing costs can be quite challenging for cloud environments despite their flexibility and scalability. From dealing with pricing models to facing a lack of visibility across clouds, there are obstacles that need to be overcome.

With careful planning and implementation combined with ongoing monitoring and review processes, organizations can mitigate the challenges and truly harness the power of multi-cloud. Your organization should adhere to the key strategies outlined in this article to take advantage of the benefits of multi-cloud while ensuring optimal costs in their cloud spending.
December 7, 2023
Avoiding Cloud Lock-In Amidst Potential Regulatory Change

At the end of my street, there’s a beautiful and much-admired garden with vibrant flowers and a well-manicured lawn. Next to this splendid garden is an abandoned vacant lot, long-neglected and overgrown with weeds. Every time I walk by, I think about how this neglected space could be transformed into something magnificent (like this garden), but no one wants to take responsibility for it. Similarly, many CIOs and CTOs have “discarded vacant lots” to deal with in their own work lives: The problem of cloud lock-in.

Cloud lock-in can be a significant headache for business leaders. Even the UK’s communications sector regulator, Ofcom, has expressed concerns about the issue. Earlier this year, Ofcom announced that it would refer the UK cloud market for investigation to the Competition and Markets Authority, highlighting barriers to interoperability and concerns about high egress fees charged by the cloud hyperscalers.

But organizations shouldn’t just wait (and hope) for regulators to solve this problem. As a true believer in harnessing the full benefits of the cloud, I propose two practical ways to combat cloud lock-in.

The Upside of a Technical Approach

The first option is straightforward: Have an honest conversation with your cloud service account leader. Express your concerns about lock-in and negotiate more favorable terms and conditions. This approach might yield some results, but it’s not a guaranteed success. So, there’s a more effective strategy: Adopt a technical approach that empowers you to avoid vendor lock-in altogether, without costing you an arm and a leg.

Imagine that, instead of trying to turn your neglected space into a garden alone, you could access a ready-made gardening community, where multiple people work together to create something amazing. The same principle applies to cloud databases. Rather than relying exclusively on one hyperscaler’s technology stack, you can opt for platforms that offer portability and flexibility across different cloud providers.

Let’s consider databases, a critical underlying component of many mission-critical business applications. Each cloud hyperscaler will offer a range of SQL, NoSQL, and graph database options, which cater to various types and scales of technical problems. The challenge is to avoid getting locked into a single cloud stack, which hinders your ability to switch between clouds if and when needed.

One viable solution is to choose “vanilla Postgres” – the basic open source version of PostgreSQL available from postgres.org or any cloud provider. This will certainly provide initial flexibility, but you are likely to encounter limitations as your application grows and requires enterprise-level features.

The real benefit of the cloud lies in modernizing your data layer to fully exploit cloud-native features like scalability and resilience.

To achieve this, you need databases that run across different public clouds and share common APIs. Born-in-the-cloud technologies, designed from the ground up for cloud environments, offer the most flexibility and benefits.

Examining your current cloud vendor’s offerings is natural, as you’re already on their platform. However, keep in mind that these services are customized to their specific cloud environment only, so this could limit your future growth. If you decide to change cloud providers or adopt a multi-cloud strategy, moving across the cloud database “matrix” can be cumbersome due to the inconsistent APIs.

If you were restricted to a single set of gardening tools when trying to transform that discarded space into a beautiful community garden, it would be nearly impossible and would not be fit for purpose.

You need to realize that these services and their functionality will always need to be customized in some way to the specific cloud environment. AWS’s fully managed relational database engine, Aurora, is compatible with MySQL and PostgreSQL, but it was specifically designed to run on Amazon. This means it can’t run on-prem, and it certainly won’t work in GCP (Google’s cloud).

As a consequence, you will have to make time-consuming and costly changes to your application to accommodate a bespoke version of PostgreSQL that will run efficiently in the cloud environment of your hyperscaler partner. You’re going to be locked into that version of the application because it can’t really work anywhere else. The same applies to the Google equivalent of vanilla Postgres (CloudSQL for Postgres), or the Microsoft version (Azure PostgreSQL).

If you truly want a multi-cloud strategy, you need to consider how you go, for example, from mid-range Google to low-end Amazon. Scaling is much discussed, but surely one of the main selling points of cloud is that you can go down as easily as you go up.

The Business Advantages of Multi-Cloud are in Reach

To achieve the advantages of multi-cloud, you need to consider databases that offer common APIs and portability across clouds. There are several open source PostgreSQL-based database options available, which enable you to keep your cloud database options open and avoid the trap of cloud vendor lock-in.

The wait for regulatory intervention might be long and uncertain. Taking proactive steps now to mitigate cloud lock-in is crucial.

Adopt a scalable open source PostgreSQL option to immediately enjoy many of the benefits of multi-cloud. Embracing this strategy now can spare you potential panic moves when and if regulatory changes come into play.

By freeing your business from the shackles of cloud lock-in, you can unleash the true potential of multi-cloud environments. Just like that “discarded vacant lot” waiting to be transformed into a beautiful garden, your applications can blossom in a multi-cloud ecosystem, which allows you to easily scale up or down and enjoy the best that each cloud provider has to offer.

So, don’t wait for someone else to fix the problem. Take action today and start cultivating your multi-cloud future.

October 3, 2023
Dell Taps Microsoft to Simplify Hybrid Cloud Computing

Dell Technologies, in collaboration with Microsoft, today unveiled a platform that tightly integrates an on-premises IT environment with the Azure cloud.

Caitlin Gordon, vice president of multi-cloud product management for Dell Technologies, said the Dell APEX Cloud Platform for Microsoft Azure is the first instance of a hybrid cloud computing platform designed specifically to address that requirement using a Premier Solution of the Microsoft Azure Stack hyperconverged infrastructure (HCI) platform that Microsoft added to its portfolio today.

The Premier Edition of Microsoft Azure Stack HCI makes available an instance of that platform that provides a more turnkey management experience than the existing editions, said Gordon.

In the face of increased economic headwinds, more organizations are looking to lower the total cost of managing IT by reducing the number of control planes that need to be managed. Each platform that an IT team typically supports today has its own control plane that requires IT staff to manage it. Reducing the number of control planes creates an opportunity to reduce the total headcount currently required to manage multiple IT platforms.

It also serves to streamline workflows in a way that enables DevOps teams to reduce the current level of friction encountered when managing multiple IT platforms, noted Gordon.

At the core of Microsoft’s approach to hybrid cloud computing that Dell is embracing is Arc, a management framework that makes it possible to manage Azure cloud instances alongside on-premises IT environments. In terms of the ability to manage hybrid cloud computing environments, Microsoft is significantly more advanced than any other cloud service provider, said Gordon.

It’s not clear whether IT organizations are embracing HCI platforms running an instance of the same core stack of software that Microsoft relies on to provide Azure services. If IT organizations expect to lower the cost of managing IT, there is no doubt they will need to modernize on-premises IT infrastructure. The older the platform, the more expensive it becomes to automate using various third-party frameworks.

Regardless of the approach, IT teams need to reconcile the need to invest in automating workflows that span multiple IT platforms. Over time, those investments will reduce the total cost of IT, but getting the required funding to start that process will require IT teams to make a strong case for increasing the return on investment (ROI).

In the meantime, the number of workloads running both in the cloud and at the network edge is only going to increase. At the same time, the bulk of existing workloads continue to run in local data center environments. Most IT teams will be required to find additional ways to automate the management of all those environments simply because the cost of adding additional IT staff to manage highly distributed computing environments as scale will otherwise be prohibitive.

As such, the issues that IT teams are facing today may be a matter of not if the management of IT will become more automated but how soon.

September 26, 2023
Cisco Aligns With Nutanix on Hybrid Cloud Computing

Cisco and Nutanix this week announced a strategic partnership through which the two companies will collaborate to advance hybrid cloud computing.

As part of that agreement, Cisco is committing to making AHV virtual machine software from Nutanix available from the edge to the cloud, starting with the Cisco Unified Compute Systems (UCS) rack and, soon after, blade servers.

Jeremy Foster, senior vice president and general manager of Cisco Compute, said via a software-as-a-service (SaaS) platform, Cisco will be able to centrally manage virtual machines from Nutanix alongside the other major virtual machines it already supports.

In addition, the partnership will enable Cisco to apply multiple forms of artificial intelligence (AI) across a hybrid cloud computing environment, he added.

Thomas Cornely, senior vice president of product management for Nutanix, said the goal is to provide IT teams with an opportunity to consolidate infrastructure across a hybrid cloud computing environment. In effect, Nutanix and Cisco are working toward converging the control planes across their entire respective portfolios, he added.

The goal is to enable IT teams to streamline the number of platforms that need to be supported as part of an effort to reduce the total cost of IT, said Cornely.

The issue that IT organizations regularly encounter is that each platform requires hiring additional IT personnel to manage. At a time when many organizations are encountering economic headwinds, there is a natural inclination to reduce the number of platforms as the size of the IT staff is reduced. Despite advances in automation, IT labor costs still represent the largest overall segment of any IT budget.

It’s not clear how aggressively IT teams are consolidating IT infrastructure to reduce costs, but after more than a decade of continuous investments in IT, many organizations are discovering they are supporting a wide range of platforms. During a downturn, however, IT teams tend to more aggressively eliminate “snowflake” platforms that require unique skillsets to manage, noted Cornely.

In the meantime, IT environments are also becoming more distributed as more workloads move to the network edge. Those workloads are not replacing applications running in on-premises or cloud computing environments, so the overall IT footprint will continue to increase. Given the chronic shortage of IT skills, the only way to effectively manage that IT estate will be to rely more on automation frameworks increasingly infused with AI.

Each IT organization will need to determine for itself what level of automation to embrace, but Cisco is betting most will opt to rely on a SaaS platform provided by a vendor versus building and maintaining an IT management platform themselves. Regardless, legacy approaches that depend heavily on manual processes will increasingly give way to cloud-based platforms that can more easily manage workloads wherever they happen to be deployed.

The issue now, as always, is funding the transition to new platforms to bring the total cost of IT back under control and eventually pay for themselves.

August 31, 2023
Why Multi-Cloud Cost Optimization is Harder Than it Looks

If your business is like most businesses that use the cloud today, you’ve gone multi-cloud. Nine out of 10 large companies have already adopted multiple clouds, and IT analysts expect that even more businesses will embrace multi-cloud architectures over the next several years. Now, the challenge facing those companies is how to optimize spending across multiple clouds. Many will find that, despite common narratives suggesting that cost savings are an inherent benefit of multi-cloud architectures, reducing spending when using multiple clouds can be a lot harder than it looks.

This is, fortunately, a challenge that can be conquered easily enough. Keep reading for tips on updating your cloud cost optimization strategy to support multiple clouds.

Multiple Clouds Don’t Guarantee Reduced Cloud Cost

Before diving into strategies for reducing spending when using multiple clouds, let me dispel a common myth about the relationship between multi-cloud architectures and spending.

Contrary to popular belief, using multiple clouds doesn’t guarantee that your business will spend less overall in the cloud, or even make it very easy to do so. Although advocates of multi-cloud strategies often promote the idea that using multiple clouds makes it simpler to find the most cost-effective cloud services because you have a larger menu of offerings to choose from, the reality is that cost-saving is not a common outcome of going multi-cloud.

Sure, in theory, having multiple clouds does give you more opportunities to reduce costs. But in actuality, migrating workloads between clouds typically takes significant time and effort. Given that most mainstream cloud services–like VM hosting and object storage–have been commoditized to the point that there are rarely major pricing differences between them, few businesses are actually going to migrate workloads back and forth between clouds on a frequent basis in order to save a few dollars.

To Reduce Multi-Cloud Spending, Focus on Cloud Provider Relationships, not Workloads

That said, adopting a multi-cloud strategy does place you in a position to reduce overall spending–but it’s not because you can migrate workloads between clouds. It’s because you can leverage relationships with multiple cloud providers to negotiate pricing reductions.

If your teams are actively using multiple clouds, you have a strong ability to negotiate enterprise pricing deals with the providers of each of those clouds. Each provider that you work with will be eager to earn as much of your business as possible, and if they know that your company is already also using a competitor’s platform, they’ll probably be more forthcoming with discounts in exchange for a commitment on your part to run more of your workloads on their cloud.

How to Keep Multi-Cloud Cost-Efficient – but Also Flexible

The caveat is that if you want to retain the flexibility that a multi-cloud architecture nominally provides, it’s important not to over-commit to any single cloud provider. If you do, you may find yourself locked into one cloud for certain workloads due to pricing discounts that you’ve negotiated for those workloads with that cloud provider.

An effective approach for finding the right balance between pricing discounts and flexibility is to decide which percentage of your overall cloud spending you want to avoid tying to a particular cloud. For example, you might decide that it makes sense to commit 30% of your spending to one cloud provider and 30% to another in exchange for pricing discounts that negotiate with each provider. But the remaining 40% of your cloud spending won’t be linked to enterprise agreements with cloud providers, so you’ll be free to spend it in whichever cloud you like.

Maintain Workload Portability to Minimize Multi-Cloud Spending

As you work to optimize multi-cloud spending, strive to adopt portable workload technologies, like containers in place of ones that are harder to migrate between clouds, like VMs.

This is important not just because portable technologies help to maximize the flexibility of your multi-cloud strategy, but also because they give you more leverage when negotiating with cloud providers. The providers realize that some types of workloads are harder to migrate than others, and they’re less likely to offer serious pricing discounts if they know it’s going to be very difficult for you to move your workloads to a competitor.

But if you can say, “70% of what we deploy runs in Kubernetes and can migrate from your managed Kubernetes environment to your competitor’s offering next week,” you’re in a much stronger negotiating position.

Conclusion

In short, don’t assume that simply adopting a multi-cloud architecture will yield enormous cost savings just because you can easily move workloads from one cloud to another to take advantage of pricing differences. In most cases, you can’t.

What you can do, however, is leverage your relationships with competing cloud providers to negotiate discounted enterprise pricing agreements. And the more portable your workloads are, the better able you’ll be to obtain serious cost savings while simultaneously enjoying the flexibility and agility that multi-cloud architectures stand to offer.

August 18, 2023