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The Observability Tax: When Monitoring Costs Exceed Downtime Costs

Something strange has happened in the observability market. The tools that were supposed to help teams reduce the cost of downtime have themselves become one of the largest line items in the infrastructure budget. I have spoken to teams paying six and seven figures annually for their observability stack, more than the estimated business impact of the downtime they are trying to prevent. When the medicine costs more than the disease, something has gone fundamentally wrong with the treatment plan.

The path here is easy to trace. It starts with good intentions. A team instruments their services with metrics, traces, and logs. The data is useful. They instrument more. They add custom dashboards. They enable distributed tracing across every service boundary. They configure high-resolution metrics because “what if we need the granularity during an incident?” Each decision is individually reasonable. Collectively, they produce a telemetry firehose that costs a fortune to ingest, store, and query, and most of which is never looked at by anyone.

The observability vendors are not incentivised to help you solve this. Their pricing models are based on data volume. More data means more revenue. So the product encourages you to instrument everything, retain everything, and correlate everything. The marketing says “full observability.” The invoice says you are paying per gigabyte for logs that nobody reads, traces that nobody queries, and metrics that feed dashboards nobody watches.

The fix is to treat observability like any other engineering investment: with a cost-benefit analysis. Start by identifying what you actually look at during incidents. Not what you could theoretically look at. What you actually look at. For most teams, that is a surprisingly small subset of the data they collect. Then work backwards from there. What telemetry directly supports incident detection, diagnosis, and resolution? Keep that at full resolution. Everything else can be sampled, aggregated, or retained at lower resolution with shorter TTLs.

OpenTelemetry helps here by decoupling instrumentation from vendors, but it does not solve the underlying problem of over-collection. That requires engineering discipline and, frankly, a willingness to accept that “less data, better curated” is a more mature observability strategy than “collect everything and hope someone finds it useful.” Your observability stack should make incidents cheaper to resolve, not more expensive to prevent than they are to endure.

September 24, 2026
Why Your Observability Stack Is Costing You More Than Your Cloud Bill

There’s a pattern playing out across engineering teams right now that nobody talks about openly: the tool meant to reduce operational complexity has quietly become one of the biggest line items on the infrastructure budget.

Observability spending is out of control, and for most teams, it’s not because they’re monitoring too much. It’s because they’re paying for platforms designed for enterprises ten times their size, ingesting data they’ll never query, and running three or four disconnected tools that still don’t give them a single coherent picture of what’s happening in production.

This isn’t a niche problem. It’s one of the defining operational challenges of 2026.

The Hidden Cost of “Industry Standard” Tooling

For the better part of a decade, the default answer to observability was to pick whichever platform the largest companies were using and figure out the budget later. That logic made some sense when infrastructure was simpler, and telemetry volumes were lower.

It doesn’t hold up today.

The average engineering team running Kubernetes across even a modest multi-cloud setup can generate millions of log lines and spans per hour. At per-GB or per-host pricing models used by most legacy observability vendors, that volume compounds fast. Teams report seeing observability bills balloon by 3–4x within 18 months of scaling, often without a corresponding increase in actual insight.

Worse, the cost isn’t just financial. Sprawling observability stacks, where metrics live in one tool, logs in another, and traces in a third, create a cognitive tax on every engineer doing incident response. When an alert fires at 2 a.m., the last thing you want is to pivot between four dashboards trying to correlate what went wrong.

Fragmentation Is the Real Bottleneck

The conversation about observability in 2026 has shifted from “are you monitoring?” to “can your team actually use what you’re monitoring?”

That distinction matters. Most teams have plenty of data. What they lack is context, the ability to move from a spike in error rates to the relevant trace, to the exact log lines, to the infrastructure event that caused it, all within a single workflow and without switching tools.

This is where the architecture of your observability platform becomes a first-class engineering decision, not just a vendor procurement choice.

Platforms built on a unified data model, where logs, metrics, and traces share the same underlying pipeline and correlation layer, fundamentally change what on-call looks like. Engineers stop triaging across disconnected dashboards and start investigating. Mean time to resolution drops not because the engineers got faster, but because the tool stopped slowing them down.

OpenTelemetry Changed the Calculus

One shift that’s genuinely reshaping the observability market is the maturation of OpenTelemetry as a standard. In 2026, OTel is no longer an aspirational project; it’s production-grade, widely supported, and increasingly the default instrumentation choice for teams building on Kubernetes, serverless, and distributed microservice architectures.

What OpenTelemetry means in practice: your telemetry data is no longer hostage to a single vendor’s SDK. You can instrument once and route data wherever you want. This is eroding one of the biggest switching-cost advantages legacy observability platforms relied on.

For engineering teams, the practical implication is significant. You can now evaluate observability platforms on the merits of their analysis, visualization, and alerting capabilities, not on how deeply their agents are embedded in your codebase.

What Modern Teams Are Actually Looking For

Conversations with SRE and platform engineering teams consistently surface the same set of priorities when they’re evaluating or re-evaluating their observability stack:

Unified telemetry correlation. Not just ingesting logs, metrics, and traces separately, but correlating them automatically so that moving from a failed deployment to its root cause takes minutes, not an hour of cross-tool investigation.

Predictable pricing. Per-host or flat-rate models that don’t punish growth. Teams that are scaling fast can’t afford a billing model where every new service or container spikes the observability bill unpredictably.

Fast time-to-value. Setup overhead is a real concern, especially for teams without dedicated platform engineers. Platforms that can be fully instrumented in under an hour, with out-of-the-box dashboards for Kubernetes, Docker, and common cloud services, reduce the operational friction of getting observability right from the start.

Infrastructure-aware alerting. Alerts that understand topology, that a pod restart in a specific namespace is related to a database timeout, which connects to a configuration change pushed 20 minutes ago, dramatically reduce alert noise and the cognitive load on whoever is on call.

A Practical Framework for Evaluating Your Stack

If you’re reviewing your observability setup right now, here are the questions worth asking before renewing a contract or adopting something new:

Does your current platform consolidate logs, metrics, and traces into a single interface, or are your engineers still switching between tools during incidents?

How does your observability bill scale with infrastructure growth? If doubling your container count doubles your bill, that’s worth modeling out.

What does your average setup time look like for a new service? If it takes more than a day to get meaningful visibility into a new microservice, the instrumentation burden is too high.

Are you paying for features your team doesn’t use? Enterprise observability platforms are often bought for capabilities that never get deployed: SIEM integrations, compliance dashboards and ML anomaly detection that require weeks of baseline tuning before they’re useful.

The Emergence of Developer-First Observability

One of the more interesting market developments of the past 18 months is the rise of platforms built specifically for the engineering teams doing the work, not for the security operations center or the compliance team.

Middleware is a good example of this shift. It’s built around the idea that observability should be fast to deploy, unified across signals by default, and priced in a way that doesn’t create a finance conversation every time a team decides to instrument something new. For teams running Kubernetes and distributed services that need production-grade visibility without the onboarding overhead or unpredictable billing of legacy platforms, it offers a meaningfully different value proposition.

The broader trend is real regardless of which specific platform a team chooses: the “enterprise observability suite” model is losing ground to platforms that prioritize developer experience, OpenTelemetry-native pipelines, and operational simplicity.

Getting the Architecture Right

The teams getting observability right in 2026 share a few common traits. They’ve standardized on OpenTelemetry for instrumentation. They’ve consolidated signal types onto a single platform rather than running parallel stacks. And they’ve tied their observability investment directly to SLOs that matter to the business — not just system metrics that look good on a dashboard.

Well-done observability isn’t about having the most comprehensive monitoring. It’s about having the right signals, in the right place, fast enough to be useful when something breaks.

That framing shift, from “collect everything” to “correlate what matters,” is the difference between a team that spends incident response time fighting their tools and one that spends it fixing the actual problem.

June 11, 2026
Futurum Research Report Surfaces Increasing DevOps Adoption of AI

A survey of 855 IT decision-makers conducted by The Futurum Group finds artificial intelligence (AI) is increasingly being viewed as playing a critical role in the development and deployment of software.

In total, the survey finds that 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code, while 39% plan to make use of AI models based on machine learning algorithms.

More than a third plan (35%) also plan to apply AI and other forms of automation to IT operations, the survey finds.

Other priorities include software supply chain security (32%), cloud-native microservices and containers (26%) and modernizing their data infrastructure (24%).

Over the next 12-18 months, organizations specifically plan to significantly increase software security spend on application programming interfaces (42%), DevOps toolchains (35%) incident response (34%) open source software (32%), software bill of materials (30%) and software composition analysis (27%) tools.

Overall, the survey finds that 71% of organizations in the advanced stages of DevOps are either standardizing its rollout across the organization (44%) or have a high competence or mastery of DevOps (26%). A full 93% claim to have adopted platform engineering as a methodology for managing DevOps workflows at scale.

Additionally, 61% of respondents report they are using Kubernetes clusters to run some (41%) or most (19%) of their production workloads. The top workloads deployed on Kubernetes are AI/ML/Generative AI (56%) and data-intensive workloads such as analytics, tied at 56% each, closely followed by databases (54%), modernized legacy applications (48%), and microservices-based applications (45%).

This report comes on the heels of a separate Futurum market research report that projects the overall size of the DevOps and application development market will reach $268.73 billion by 2028, representing a 9.76% compound annual growth rate.

That report also suggests the rate at which DevOps teams are shifting to cloud platforms is only going to accelerate, with DevOps and software development tools and platforms projected to reach $218.21 billion by 2028.

Mitch Ashley, vice president and practice lead for DevOps and application development for The Futurum Group, said the four main drivers of those investments are the need to increase the velocity at which software is developed and deployed, the rise of AI, an ongoing transition to cloud-native applications and a need to better secure software supply chains.

In general, it’s apparent that DevOps as a practice for building and deploying software continues to gain momentum even in uncertain economic times. As the race to build and deploy AI applications continues the need to build and deploy applications faster has arguably never been more crucial. Most of those AI applications will eventually be deployed on Kubernetes clusters that most organizations already rely on to deploy their most modern applications.

The challenge, as always, is finding the best way to achieve that goal in a way that application developers will embrace rather than resist. After all, if application developers don’t appreciate the experience, the best and brightest will inevitably start looking for alternative employment that better suits the way they, rather than the organization, prefer to work. At the same time, however, there is always going to be a need to achieve some level of standardization that keeps application development costs from spiraling out of control so ultimately DevOps success requires the striking of the most delicate of balances.

March 19, 2025
Exploring Cloud Costs and Cost-Saving Strategies
We are witnessing cloud transformation moving beyond technology to change the way businesses operate and innovate. As the cloud becomes the operating model for a company, effective cloud cost management becomes critical for the organization’s health.

However, before organizations can begin cloud transformation, they should develop and align the technical and financial strategies required for the journey. This will allow them to set measurable goals for their cloud shift, optimize resource utilization and ensure smooth cloud transformation.

In this article, Itransition’s cloud consultants, skilled in developing cost-effective cloud transformation strategies, will guide you through common cloud migration expenses, potential financial pitfalls on your cloud journey and cost optimization strategies.

Cloud Adoption Costs

While cloud transformation can lead to long-term savings, the initial stages of the cloud journey and ongoing cloud usage can be costly if budgeted irresponsibly. Here are the main costs associated with each consecutive stage of cloud migration.

Cloud Readiness Assessment

Any cloud journey requires a strategy. To develop the optimal cloud adoption plan for your company, you must do a cloud migration assessment. This is done by evaluating your existing IT infrastructure, the business software and applications you use, and the data gathered and processed to determine your cloud infrastructure needs, such as computing, storage and networking resources. For example, assessing your apps enables you to find the best method to migrate or modernize them for the cloud.

Cloud migration assessment is a project on its own, requiring time and effort. Sometimes, you need to hire cloud consultants to perform a thorough cloud readiness assessment and buy specialized assessment tools, such as application portfolio management or performance and dependency mapping tools.

Cloud Strategy Development

After assessing your cloud readiness, you must develop an effective cloud strategy that outlines a clear path to maximize the benefits of the cloud, while avoiding costly mistakes and pitfalls along the way.

The cost of designing a cloud adoption strategy depends on factors like organization size and complexity, the scope of cloud adoption plans, the amount of customization required and the expertise of the cloud consultants involved.

Migration and Deployment

Once the assessment and planning are completed, organizations move on to the migration and deployment stage, which involves moving applications, data and workloads to the cloud environment by the established plan or strategy.

When deciding on a cloud deployment strategy (single cloud, multi-cloud or hybrid cloud), keep in mind that multi-cloud and hybrid cloud deployments are more expensive.

Depending on the scope, the migration process may involve rehosting (lift and shift), refactoring, re-platforming, rearchitecting or redesigning your existing solutions for the cloud.

The lift and shift method is the fastest and least expensive way of cloud migration, as applications are moved as-is from your on-premises infrastructure to cloud infrastructure, which doesn’t require a large team.

However, rehosting is suitable for moving workloads that are cloud-ready to some extent (for example, apps built on microservices architecture) or as a first step to rearchitecting a monolithic application for the cloud. In other cases, preparing your applications for the cloud requires refactoring, re-platforming, rearchitecting or redesigning, which are more expensive, labor-intensive and time-consuming.

At the end of the cloud migration process, there are costs associated with testing the performance of the migrated applications, penetration testing of the new environment and regulatory compliance assessment.

To sum up, estimating the cost of the actual migration is a challenging task since it greatly depends on the expertise of the team performing the migration. Hiring a consulting partner with a proven track record of managing cloud migrations can accelerate the project and reduce the risk of downtime.

Change Management and Training

Moving to the cloud will change how your teams work, requiring new skill development and technical training. The expected skill gaps arise in areas such as security management, cloud app development and cloud infrastructure management and administration.

Ongoing Management

Cloud operational expenses consist mostly of cloud infrastructure costs for the three core areas: Compute, storage and network.

Computing is the processing power, memory and temporary storage needed to run cloud workloads. You pay for computing capacity depending on how many and what types of instances you use during a given period.

Network connectivity refers to the network capacity you use for your applications. This means that you are charged for the volume of data you transfer out of a cloud service (egress), into a cloud service (ingress) or both, in gigabytes (GB), terabytes (TB) or petabytes (PB). Low-latency, high-bandwidth network connectivity can incur extra costs, as well as static IP addresses, gateways and load balancers.

Storage capacity costs will depend on the type of storage you choose. For instance, the elastic storage service is priced on a per GB per month basis. Alternatively, if you choose a managed storage service you must pay for the whole storage volume, regardless of how much capacity you use.

To set up a comprehensive cloud environment, you would want to leverage cloud-based application services, integration connectors and API management, data services, monitoring and analytics and security solutions. These could incur costs related to, for example, API calls per month, users per month requiring access governance and resources and events per hour which require monitoring.

Cloud operational expenses are also affected by software licensing and different licensing models. Some vendors can charge based on the number of users, while others are based on the computing resources used.

Hidden Cloud Costs

Cloud offerings are diverse and appealing, with cost-effectiveness being the most anticipated promise of the cloud. But what about the traps of cloud migration? Here are the most common factors that can lead to unexpected costs:

Improper Cloud Storage Management

Mismanaging your cloud storage can lead to multiple copies of the same data, accumulation of stale data, losing track of how much storage is being used and what by and overuse of storage. Inefficient cloud storage management leads to additional costs, such as using high-performance (and more expensive) storage for archived or backup data or paying for unused storage capacity.

Choosing the Wrong Data Center Location

When you decide to move to a cloud-based virtual infrastructure, you might think it is enough to choose a cloud data center location in the same area as your company or the majority of your users. However, the fact that the data is easily accessible through the cloud doesn’t mean the location of the data center doesn’t matter.

Choosing the wrong data center location option can lead to higher data transfer costs, as transferring data between different geographical regions (for example, from a data center in Europe to one in Asia) is often more expensive than within the same area.

Inefficient Planning of Data Transfers

Data transfer occurs when you are moving data across cloud regions and zones, to and from the cloud and to and from on-premises networks. Cloud providers typically allow free data ingress. However, when data is transferred out of the cloud, data egress fees can vary significantly depending on the provider and the amount of data being transferred. So, if you do not plan your data transfers carefully, the costs can rise quickly.

Strategies for Optimizing Cloud Costs

You can’t think about optimizing cloud costs only in terms of simply reducing them. Rather, consider it as a strategic technique to ensure your cloud costs are in line with the goals and objectives of your business. It would be reasonable to pay more for certain cloud services if you believe that they can considerably enhance revenue.

Here are some practical tips for optimizing your cloud expenses and avoiding hidden charges:
- Review pricing plans offered by several cloud providers and chose a simplified pricing model with no hidden fees in order to make informed decisions about how to use these resources and control costs.
- Always employ specialized tools to examine and monitor costs linked to the cloud. These tools detect anomalous cost spikes and dips in real-time so that you can further investigate and minimize unexpected variations in your cloud spending.
- Conduct continuous monitoring of your cloud resources utilization and set key performance indicators (KPIs). With usage metrics in hand you can identify any unused resources or redundant instances, scale up during peak demand times or scale down when the workload decreases and thus avoid unnecessary costs.
- Modify computing resources dynamically based on actual needs. Here, you can benefit from autoscaling as it helps reduce the number of active servers during minimal activity and launches many new ones when activity is high. Most major public cloud providers have tools and services to ensure cloud scalability for their customers.
- Use discounts and savings programs from cloud providers. For instance, if you have predictable workloads, consider using reserved instances instead of on-demand ones.
- Optimize your data routing by determining the most efficient path for data transfer. For example, use the least expensive regions for data transfer — when it comes to AWS, the U.S. and Canada are usually the cheapest, while Singapore, India and South America are usually more expensive.
- Consider opting for a multi-cloud or hybrid cloud infrastructure. With a multi-cloud approach, you will be able to compare solutions from different providers and choose the most cost-effective combination of cloud services that align with your budget. Hybrid solutions, in turn, provide a means for integrating on-premises storage with cloud resources as necessary.
Conclusion

Cloud technology keeps advancing with new services, pricing models and capabilities that are introduced regularly. This changing environment calls for a holistic approach to how companies use cloud resources.

There are various costs to consider when adopting the cloud, including initial setup, ongoing operations and resource expansion, as needed. Businesses must be aware of these expenses to budget and prepare for cloud expenditures appropriately. Additionally, they should use best practices for cloud cost optimization to fully realize the benefits of the cloud.
December 9, 2024
How Cloud Costs can be Lowered and How Cloud Tooling can be Delivered at a Lower Cost
Several factors, including price increases, a rush to adopt AI, inflation and ongoing digital modernization efforts, are driving IT leaders to revisit cloud costs. According to a recent survey of IT professionals, three out of every five organizations have seen an increase in cloud spending in the past year, with nearly four in 10, who experienced price hikes, saying their costs jumped by more than 25%.

Edward Viaene, Co-Founder of IN4IT, looks at how cloud costs can be lowered and cloud tooling be delivered at a lower cost.

For businesses running on Amazon Web Services (AWS), it is easy to get caught off guard by the escalating cloud costs. One minute you are taking advantage of AWS’s incredible scalability and diverse service offerings, and the next you are facing an unexpectedly high bill.

Be it a startup, a small business or an enterprise, controlling AWS costs is critical to maintaining the bottom line. In this blog, we will explore a few practical strategies that help you reduce AWS expenses while keeping your infrastructure efficient.

When you first sign up for AWS, you might receive credits through a startup accelerator or during a proof-of-concept (POC) phase. During this time, you are probably not paying close attention to the costs as those credits are covering everything. But as soon as the credits run out, reality hits hard — suddenly, what seemed like ‘free’ services have ballooned into a significant monthly expense.

It is common for businesses to overlook monitoring their AWS usage early on, only to find out later that they have been running services that are more expensive than anticipated. This is particularly true for services like Amazon Aurora, Redshift or serverless offerings that may be overkill for smaller companies or startups.

Monitor Your Cloud Costs

The first step in reducing your AWS bill is to monitor your costs from the beginning. Even if you are using credits, AWS provides cost dashboards that allow you to see how much each service is costing you in real-time. This visibility is crucial for identifying potential overspending during the early stages.

For example, some businesses opt for Amazon Aurora because it is often recommended as a top-tier relational database service (RDS). However, Aurora is designed for large-scale, high-performance needs, which may not be necessary for a smaller operation. Instead, Amazon RDS with PostgreSQL could be a much more cost-effective option, especially for those who do not need the full capabilities of Aurora.

Another common pitfall is choosing AWS services that are more suited for enterprise-level operations when a smaller, more affordable option would suffice.

One area where this frequently happens is with serverless technologies. While serverless databases like Aurora Serverless are powerful and scalable, they can be more expensive than traditional instance-based databases for most workloads.

If you are running a database with consistent demand, it might be more cost-effective to use a small instance rather than relying on serverless architecture, which charges per request.

Additionally, Redshift is a popular data warehousing service, but it is not always the best fit for startups. Unless your company is heavily focused on data analytics and can afford the high costs associated with Redshift, it may be wise to explore more economical data storage options like Amazon S3 or RDS.

Once you have a better understanding of which services you need, consider leveraging AWS Reserved Instances (RIs) or Savings Plans to lock in lower prices.
- RIs allow you to reserve EC2 instances for a one- or three-year period, providing significant discounts compared to on-demand pricing. These are especially useful if you know exactly what instance types you would be using for a long period.
- Savings Plans, on the other hand, offer more flexibility by allowing you to commit to a minimum amount of compute spend per hour without being tied to a specific instance type or region. This is a great option if you are still scaling or testing different services but want to reduce costs in the meantime.
The key is to review your current usage and forecast your future needs. If you are not ready to commit to a specific instance family, a Savings Plan gives you more wiggle room while still saving money.

Databases are often one of the biggest contributors to AWS bills, but there are ways to optimize them. For example, Amazon DynamoDB offers two pricing models: On-demand and provisioned capacity. On-demand pricing is convenient for workloads with unpredictable traffic, but it is far more expensive than provisioned capacity if your workload has consistent demand. By switching to provisioned capacity and reserving capacity for one or more years, you can significantly reduce costs.

Additionally, RDS instances running on ARM64 architecture can save you money. ARM-based instances are cheaper to run as compared to x86-based instances and they often provide better performance too. If your application is already running on a MacBook or other ARM-based machine during development, switching to ARM instances in production should be seamless.

One of the most common mistakes businesses make is duplicating services, which unnecessarily drives up costs. For instance, many organizations use both AWS CloudWatch and third-party tools like Datadog for logging and monitoring. While Datadog offers a user-friendly interface, it comes at a higher cost, especially when you are already paying for a similar functionality in CloudWatch. If you need Datadog or another third-party tool, consider sending logs and metrics directly to that service instead of duplicating them in CloudWatch. This approach can help reduce costs while still giving you access to the features you need.

AWS offers a wide range of services designed for different scales of operation. As a startup or smaller company, you do not need to immediately jump to enterprise-level solutions like Kubernetes or Amazon SageMaker. Kubernetes offers flexibility and control, but it can quickly become expensive when you factor in the costs of managing multiple nodes, load balancers and data storage.

Instead, services like AWS Fargate or elastic container service (ECS) provide managed container solutions that are easier to maintain and cheaper to run at smaller scales. Similarly, while SageMaker is great for large-scale machine learning (ML) models, you can often build and train models locally using a smaller instance and move to a managed service when necessary.

Review Your Architecture

Finally, one of the most important ways to keep your AWS costs down is to regularly review your architecture. As your business grows, your AWS infrastructure will evolve and once essential services may no longer be necessary. It is easy to let unused resources pile up, leading to higher bills. Consider having an external architect review your infrastructure periodically. AWS offers free architectural reviews through programs like the Well-Architected Framework, which can help identify areas where you can reduce costs or improve performance.

Reducing AWS costs does not require a complete overhaul of your infrastructure. Many cost-saving strategies are about simplifying what you already have. By choosing the right services, leveraging savings plans and RIs, avoiding service duplication and regularly reviewing your architecture, you can significantly lower AWS expenses while maintaining the performance and reliability of the business.

For DevOps teams, keeping AWS costs under control is about being mindful of the services you use and making sure you only pay for what you need. By making a few simple adjustments, you can reduce cloud expenses and free up resources to focus on business growth.
December 6, 2024
Survey Credits Engineering Teams With Keeping Lid on Cloud Costs

A CloudZero survey credits engineering teams with keeping cloud costs under control, but there’s still some room for improvement.

A survey of 1,000 U.S. engineering and finance professionals working in organizations with 100 to 9,999 employees, that spent at least $500,000 annually on cloud services from Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, finds just over half (51%) believe costs are higher than they should be, with 14% of those respondents claiming their cloud costs were way too high.

Conducted by CloudZero, a provider of a platform for managing cloud computing costs, the survey further finds that 81% of respondents said their cloud costs are “about where they should be” when engineering has some level of ownership. The survey also noted that more than half (59%) said their organization now has at least one person dedicated to implementing best FinOp practices to control cloud costs.

In total, 87% said they are able to investigate cost discrepancies in a matter of hours, the survey finds.

Despite those efforts, however, a full 88% said they see significant variance (5–30%) in actual cloud spend versus forecast, but only 43% said cloud costs are a higher priority in 2024 than in the previous year. In fact, well over a third (37%) said cloud costs are less of a concern.

Lack of Cloud Cost Visibility

Nevertheless, a full 89% of respondents said a lack of cloud cost visibility has an impact on their ability to carry out their role, with 46% describing that impact as significant. Among the 500 engineers that participated in the survey, 66% report a lack of visibility into cloud costs causing some level of disruption to their work, with 22% reporting high levels of disruption.

Three-quarters (75%) of all respondents noted they fear losing their jobs if cloud costs abruptly surge by 50% or more, the survey finds.

Only approximately a third of respondents said they could accurately allocate cloud costs, with 42% only being able to estimate. More than three quarters (78%) have less than 75% of their cloud spend allocated.

The profit margins enjoyed by cloud service providers suggest there is plenty of room for improvement when it comes to optimizing consumption of cloud services.

Costs Can be Highly Variable

Bill Buckley, senior vice president of engineering, said the challenge is that in addition to all the types of cloud services being consumed, costs can be highly variable from one month to the next. Developers also have a natural tendency to over provision cloud resources to ensure applications are always available without appreciating total costs, he added.

The best way to control those costs is to ensure engineering teams armed with the right tools are managing the consumption of those resources, said Buckley.

There may, of course, come a day soon when artificial intelligence (AI) is more broadly applied to rein in cloud costs by, for example, taking more advantage of spot instances to run workloads.

In the meantime, DevOps teams would be well-advised to revisit application workloads running on various clouds to make sure resources are optimally being employed in an era where many organizations as much as possible are trying to maximize existing investments in IT. After all, even in the best of times, it’s only a matter of time before someone from the finance teams starts asking questions that DevOps teams should be able to answer in a matter of minutes.

May 10, 2024
Controlling Cloud Costs With Efficient Ramp Plans

A mix of rapid migrations to the cloud and complexity over usage and costs has companies desperately trying to reduce waste and gain control over their spending. Making matters worse is a volatile economy that has decision-makers on edge, considering cost-cutting measures and placing a priority on locking down revenue.

Without a formal FinOps or cost optimization strategy in place, it’s easy for companies to see cloud expenses snowball out of control. In fact, analysts from Gartner report that organizations that do not employ cloud cost optimization can overspend by up to 70%. That said, ramp plans are becoming a vital tool for containing cloud resources.

Still, while this strategy can show how to effectively grow your environment and increase workloads, the way a ramp plan is carried out is key to its success. The following looks at what companies should do to succeed with ramp planning accompanied by some strategic advice for handling unique needs that may arise.

Ramping Up

A ramp plan, at its most rudimentary level, is data tracking toward how your organization consumes cloud resources versus what it commits to with a cloud service provider (CSP) or reseller. By committing to spend a set dollar amount, you can get healthy discounts or other incentives. As an example, a 50% increase in consumption by the end of the year might earn a 10% discount from a CSP on compute workloads. But there is risk: A company is still obligated to pay that figure, even if it uses far fewer resources than it committed to.

Still, just as you don’t want to spend money needlessly, CSPs—even high-volume hyperscalers like Amazon AWS and Google GCP—would rather see customers get what they need and grow their footprint. For some organizations, the answer is bringing in a partner to help forecast spend. Questions regarding what is needed, past usage, anticipated growth, potential for acquisitions and more need to be answered. You also have to examine such things as product and service offerings. For instance, a subscription app you offer could catch fire and bring in customers—but you’ll need to plan accordingly so the resources will be in place to meet this demand.

Compiling and projecting this all isn’t easy, but this type of data is needed so you can develop a ramp plan and budget effectively. In the past, this process required time-consuming manual research, a host of spreadsheets and sophisticated algorithms. However, technology has been developed to automate, drive and scale this process. For some businesses, the ideal is to find a partner with this capability in-house and a depth of expertise to use it correctly.

Processes and Partners

Ideally, a ramp plan should be developed before a commitment is made. It should be reviewed on at least a quarterly basis with the C-suite and appropriate technical executives and then revised as necessary. On the whole, CSPs like to sign customers to contracts extending more than a year, with three-year terms being typical.

Working with a third-party partner, reseller or directly with a hyperscaler comes down to personal preference. Big companies, much like hyperscalers, tend to have a lot of bureaucratic processes and requirements. So, if you want a tactical agreement with AWS, you may have to buy tactical support. You may get a good financial incentive in exchange, but the cost of enterprise-level support could offset that.

Partners and resellers of cloud services, on the other hand, help hyperscalers scale their businesses, so they often have special arrangements that lower support costs. The development of ramp plans is usually less when going through a third party versus registering with a CSP. These services are often designed for smaller companies that larger CSPs don’t have time to handle. Third parties tend to provide tailored services more in tune with customer needs, including more flexible usage, optimization and security. These partners tend to negotiate more effectively than customers themselves because they better understand the ecosystem and which levers to pull.

Measuring Success

It’s not easy to measure and track the success of a ramp plan. Beyond understanding and meeting your commitment—and getting the most from cloud spend—the best measure is profitability. Are you just eating costs or acquiring new customers? Is your efficiency boosting or lowering your margins? It’s even possible to apply unit economics to assess if you’re making money on the resources being used.

Making a commitment and ramp planning should involve more than financial incentives. Maybe there’s some other strategic aspect you want to achieve? From service-level agreements to support, a lot of things can be woven into a commercial contract. Benefits can come in various forms, but when they’re added together, you can take success in the cloud to a whole new level.

March 14, 2024
AWS Cost Management in 2024

AWS cost management involves strategies and practices aimed at controlling and optimizing the expenses associated with using Amazon Web Services (AWS). As businesses scale and diversify their use of AWS services, it becomes crucial to manage costs to ensure cloud expenditures remain aligned with business objectives and budgets. Effective cost management in AWS encompasses understanding billing and account structures, monitoring usage and costs in real-time and making informed decisions about resource allocation and deployment.

At the core of AWS cost management is the ability to visualize and analyze cloud spending. AWS provides detailed billing reports and cost management tools that enable users to track their usage patterns, identify trends and pinpoint areas of inefficiency. Many organizations use third-party tools to get more detailed insights and savings recommendations. This data-driven approach allows organizations to adjust their cloud usage to avoid unnecessary costs, such as by shutting down idle resources or choosing more cost-effective service options.

Furthermore, cost optimization techniques, such as selecting the appropriate pricing models (e.g., Reserved Instances, Savings Plans), optimizing resource utilization (e.g., scaling resources to match demand) and applying cost allocation tags for granular tracking, are critical components of AWS cost management. These practices help businesses allocate cloud costs accurately across departments or projects, enabling better planning, budgeting and accountability.

The Importance of Cloud Cost Optimization for DevOps Teams

Cloud cost optimization is crucial for DevOps teams to ensure efficient resource utilization and cost-effectiveness in cloud environments. As cloud infrastructure becomes more complex and dynamic, DevOps teams face the challenge of balancing performance, scalability and cost. By adopting cost optimization strategies, teams can identify wasteful spending, optimize resource allocation and ensure that cloud investments align with business goals and operational requirements.

For DevOps teams, cloud cost optimization involves continuous monitoring, analysis and adjustment of cloud resources. This iterative process allows for the fine-tuning of systems to achieve optimal performance without overspending. Techniques such as auto-scaling, choosing the right instance types and employing cost-effective cloud services are part of this optimization.

Additionally, implementing budgeting controls and cost alerts helps prevent unexpected expenses, enabling teams to allocate resources more efficiently and respond quickly to changes in demand. Through effective cost optimization, DevOps teams can contribute to the overall financial health of an organization by maximizing the value of cloud investments while supporting rapid development and deployment cycles.

AWS Cost Management Best Practices in 2024

Cloud cost management is evolving. Here are some of the best practices that will become more important in 2024 and beyond.

Adopt Serverless Architectures Where Appropriate

Serverless architectures, also known as function-as-a-service (FaaS), allow developers to build and run applications without having to manage any servers. This means you only pay for what you use, which can significantly decrease costs when compared to traditional server-based architectures.

In addition, serverless architectures can scale automatically to meet demand, eliminating the need for manual intervention. This can free up valuable time for your IT team to focus on other important tasks. Furthermore, serverless architectures are highly flexible, allowing for rapid development and deployment of new features and applications. This can significantly reduce time-to-market, which can provide a competitive advantage in today’s fast-paced digital economy.

Utilize Spot Instances for Flexible Workloads

Another effective strategy for AWS cost management is to utilize Spot Instances for flexible workloads. Spot Instances are spare AWS EC2 instances that are available at up to 90% off the On-Demand price. This means you can significantly reduce your AWS costs by strategically bidding on and using Spot Instances when demand is low.

However, it’s important to note that Spot Instances are not suitable for all workloads. They are best suited for flexible, interruptible workloads that can withstand occasional downtime. This includes tasks such as batch processing, data analysis and non-critical background tasks.

In addition, it’s crucial to have a robust strategy in place to handle Spot Instance interruptions. This can include setting up Auto Scaling Groups, using Spot Fleet or leveraging third-party tools. By effectively managing Spot Instances, you can significantly reduce your AWS costs while maintaining operational efficiency.

Implement FinOps Principles

FinOps, or cloud financial management, is a set of principles and best practices designed to help organizations manage and optimize their cloud costs. Implementing FinOps principles is a critical component of AWS cost management in 2024.

FinOps involves three key principles: Visibility, optimization and governance. Visibility means having a clear understanding of your cloud usage and costs. This can be achieved through comprehensive tagging, detailed cost reporting and regular cost reviews.

Optimization involves ensuring that you’re using your cloud resources efficiently. This can involve right-sizing your instances, leveraging savings plans and reserved instances and automating your resource management.

Governance involves setting up policies and procedures to manage your cloud costs. This can include setting up budget alerts, implementing cost approval workflows and regularly auditing your cloud usage and costs.

Leverage AI for Cost Optimization

Leveraging artificial intelligence (AI) for cost optimization is a growing trend in AWS cost management. AI can analyze vast amounts of data quickly and accurately, providing valuable insights into your cloud usage and costs.

For example, AI can identify inefficiencies in your cloud infrastructure, such as underutilized instances, unnecessary data storage, or expensive data transfer. By addressing these inefficiencies, you can significantly reduce your AWS costs.

In addition, AI can provide predictive analytics, allowing you to forecast future cloud usage and costs. This can help you plan your budget more accurately and make informed decisions about resource allocation.

Use Sustainable Computing to Reduce Costs

Sustainable computing, or green computing, is the practice of using computing resources efficiently while minimizing environmental impact. By adopting sustainable computing practices, you can not only reduce your environmental footprint, but also reduce your AWS costs.

One strategy is to use energy-efficient instances, such as those powered by the AWS Graviton2 processor. These instances deliver superior performance while consuming less power, resulting in lower costs.

Another strategy is to utilize AWS’s carbon footprint tool, which can help you measure the carbon emissions associated with your AWS usage. By reducing your carbon emissions, you can reduce your environmental impact and potentially qualify for carbon credits, which can offset your AWS costs.

In conclusion, AWS cost management in 2024 requires a comprehensive approach that includes adopting serverless architectures, utilizing Spot Instances, implementing FinOps principles, leveraging AI and using sustainable computing. By following these best practices, you can effectively manage your AWS costs, optimize your cloud infrastructure and ensure the maximum return on your cloud investments.

February 23, 2024
Massdriver Adds Cloud Cost Controls to Internal Developer Platform

Massdriver has made available tools for tracking cloud costs and generating a cloud infrastructure bill of materials (Cloud IBOM) to internal developer platforms (IDPs) as part of an ongoing effort to streamline platform engineering workflows.

The Cloud IBOM makes it possible, with a few clicks, to map hybrid cloud resources such as databases, machine learning pipelines and serverless computing frameworks.

Massdriver CEO Cory O’Daniel said collectively, these tools reduce expenses by preventing misconfigurations that result in unexpected costs. Rather than solely providing a summary of costs incurred, the tools provided by Massdriver surface how much cloud services are going to cost at the time the developer is invoking them, he noted.

In the absence of that context, developers will nearly always overprovision cloud resources to ensure maximum availability regardless of cost, says O’Daniel. The overall goal is to help organizations reduce the total cost of IT by providing developers with insights into costs in real-time, he added.

Earlier this year, the company launched an IDP that comes with platform engineering tools that, for example, make it possible for DevOps teams to build a catalog of infrastructure components that developers can access within the context of guardrails they define. The platform also includes observability and visualization tools that make it simpler to track metrics and monitor the usage of cloud resources.

Massdriver is making a case for an IDP that provides built-in tools and workflows that enable organizations to embrace platform engineering as a methodology for managing DevOps workflows at scale. The goal is to reduce the level of expertise required to build and deploy applications in cloud computing environments, said O’Daniel. One of the primary reasons there are not more applications running in the cloud today is that these environments are still too complex to successfully manage, he added. An IDP abstracts away that complexity within a set of workflows that have been vetted by a DevOps team, he added.

There are, of course, multiple approaches to building an IDP, but rival approaches require too much effort to set up and maintain, said O’Daniel. A proprietary platform provides a more turnkey approach that doesn’t require DevOps teams to either build their own or customize an open source platform, he added.

It’s not clear at what pace organizations are embracing platform engineering, but there is a clear need to strike a balance between delivering IT services managed by a centralized team and the desire a developer might have to embrace new tooling whenever they see fit. Today, too much responsibility for provisioning IT environments has been pushed left toward developers who now have less time than ever to write code.

There is, naturally, no guarantee that reducing the cognitive load for developers will increase productivity. Building applications requires streaks of inspiration, but, in theory, an IDP should provide more time for inspiration to strike. DevOps teams, as part of that effort, should be continuously working toward eliminating any bottlenecks that have emerged in their DevOps workflows.

December 18, 2023
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