69% K8s Spend Cut [2026]

Elias Virtanen
September 12, 2026
21 min read

Kubernetes cost management sits at the center of the broader cloud computing conversation in 2026, and clusters are running hotter on cost than almost anything else in the infrastructure budget. The numbers back that up. Cast AI’s 2026 State of Kubernetes Optimization Report, based on telemetry from tens of thousands of production clusters across AWS, Azure, and Google Cloud, found average CPU utilization sitting at just 8%, down from 10% a year earlier. Memory utilization dropped to 20% from 23%. GPU utilization, the metric that matters most as AI workloads eat cluster budgets, averaged just 5%. That means most teams provision roughly 20 times more GPU capacity than their workloads actually use.

That waste is why three tools keep coming up in the same breath: DevZero, Kubecost, and Cast AI. All three target Kubernetes cost specifically, rather than general multi-cloud FinOps reporting (that’s a different fight, one we already covered in our CloudZero vs Vantage vs Kubecost breakdown). This comparison narrows the lens to the Kubernetes-native layer: rightsizing, bin-packing, spot automation, and increasingly, GPU allocation. The three tools take genuinely different approaches to solving the same 69%-CPU-overprovisioning problem, and picking the wrong one can mean paying for a dashboard when you needed an autopilot, or the reverse.

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DevZero, Kubecost, and Cast AI at a Glance

DevZero positions itself as an autonomous optimizer. Its homepage leads with a live rightsizing pitch: match node and pod requests to actual usage, blend on-demand pricing with spot pricing, and shop for the cheapest equivalent instance across AWS, Azure, GCP, and Oracle Cloud. Founded in 2021, the company has raised roughly $26 million total, including a $21 million Series A in January 2023. DevZero explicitly supports Amazon EKS, Azure AKS, and Google GKE, and its own pricing blog shows node SKU comparison tables spanning all four major clouds.

Kubecost takes the opposite architectural bet: deep, trustworthy cost allocation and reporting, built on top of OpenCost, the open-source cost allocation engine it originally created and donated to the Cloud Native Computing Foundation. OpenCost remains a CNCF Sandbox project as of 2026, with its GitHub repository sitting around 6,600 stars. Kubecost itself is now sold under IBM’s Apptio portfolio following IBM’s earlier acquisition of Apptio, and it keeps a genuinely permanent free tier rather than a time-limited trial, a detail we walked through in our Kubecost setup guide.

Cast AI sits between the two, leaning toward automation but monetizing it differently. Founded in 2019, Cast AI has raised a reported $272 million total, including a $108 million Series C in April 2025, with a valuation reported around $850 million in the most recent disclosed figures. Rather than charging a flat per-core rate, multiple third-party pricing trackers describe Cast AI’s paid tier as tied to a percentage of realized savings, commonly cited in the 15% to 20% range, alongside a free monitoring-only tier. Cast AI counts BMW and Cisco among its publicly named customers, according to business press coverage of its 2026 optimization report, though neither company has disclosed a specific savings percentage tied to the tool.

Kubernetes Cost Optimization Tools Compared: Full Specs

The feature gap between these three tools is wider than a typical three-way vendor comparison because they aren’t really competing on the same axis. DevZero automates first and reports second. Kubecost reports first and recommends second. Cast AI tries to do both while pricing itself on outcomes rather than usage. Here’s how the specs stack up as of September 2026.

Notice the split in row four. Kubecost’s free Foundations tier is unusually generous, unlimited clusters up to a combined 250 cores, forever, with no credit card required. DevZero’s free tier is monitoring-only and capped at two clusters, nudging teams toward the paid automation tier quickly. Cast AI’s free tier also withholds the automation, the actual product differentiator, behind a paywall.

Pricing Compared: Free Tiers to Percentage-of-Savings Fees

Pricing across these three tools follows three different logics, and that alone should shape which one fits a given team. DevZero charges by CPU core consumed, roughly $5 per core/month through its direct plans and $7 per core/month via the AWS Marketplace consumption listing. Kubecost prices by vCPU monitored once you outgrow the free 250-core ceiling, with Business tier pricing reported around $3.42 per vCPU/month through AWS Marketplace, or a flat $449/month minimum, whichever is higher, and custom Enterprise contracts sold through IBM’s store. Cast AI declines to publish a rate card at all; third-party trackers describe a Growth plan near $1,000/month plus roughly $5 per vCPU/month, but the more commonly cited model ties paid tiers to 15% to 20% of the savings Cast AI’s automation actually delivers.

The percentage-of-savings model is the one worth sitting with. On paper, it aligns Cast AI’s incentives with the customer’s, since the vendor only gets paid if it actually cuts your bill. In practice, it makes budgeting harder to forecast than a flat per-core rate, especially for finance teams trying to lock in a FinOps line item a quarter in advance. A CFO comparing quotes should ask each vendor to model a worst-case, best-case, and typical-case monthly bill rather than accept a single blended average.

Autonomous Remediation vs Reporting-Only: The Core Architecture Divide

This is the single biggest fork in the road. Kubecost, at its core, answers the question “where is the money going?” It allocates cost by namespace, deployment, label, and team, and it will recommend a rightsizing change, but it stops short of pushing that change live. Someone still has to review the recommendation and apply it, whether that’s a pull request against a Helm values file or a manual kubectl patch.

DevZero and Cast AI both push further into the “just fix it” category. DevZero’s own marketing shows a before-and-after dashboard: current spend of $576,542 per month against an optimized spend of $176,542 per month, an annualized savings claim of $4.8 million. That figure comes from DevZero’s homepage and isn’t tied to a named customer, so treat it as a vendor-disclosed example rather than an independently audited case study, but the math checks out: a 69% cut if the optimized number holds. Cast AI’s automation works similarly, adjusting pod requests, node counts, and instance types without a human clicking approve on each change.

The tradeoff is trust. Autonomous remediation is only comfortable once a team has watched it run long enough to believe it won’t evict a production pod at 2 a.m. because a metric spiked. Several 2026 tool roundups, including guides from DevZero’s own comparison of the category, suggest a common pattern where larger organizations run Kubecost for financial reporting and chargeback while layering DevZero or Cast AI on top for the actual remediation, rather than picking one tool to do both jobs.

Rightsizing, Bin-Packing, and Node SKU Selection

Rightsizing sounds simple until you look at how differently each tool executes it. Kubernetes’ own documentation on resource bin-packing describes the scheduler-level mechanics: packing pods tightly onto fewer nodes based on requested CPU and memory, versus spreading them out for resilience. None of these three tools change the underlying Kubernetes scheduler. Instead, they change the inputs the scheduler works with, by adjusting pod resource requests and by picking which node types exist in the cluster to begin with.

DevZero’s SKU shopping approach

DevZero’s pricing blog lays out a concrete example: comparing an m7i.xlarge on Amazon EKS against a Standard_D4s_v5 on Azure AKS and an n2-standard-4 on Google GKE, all roughly equivalent in vCPU and RAM, then picking whichever region and provider combination is cheapest at that moment, including spot pricing. Its homepage shows a live “best price found” ticker citing figures like $0.038/hr on GCP versus $0.045/hr on Oracle Cloud for a comparable instance. That’s bin-packing at the fleet level, not just the pod level, choosing the cheapest hardware before choosing how tightly to pack it.

Kubecost and Cast AI’s narrower scope

Kubecost doesn’t attempt cross-cloud SKU shopping; it assumes you’ve already chosen your infrastructure, whether that’s a managed control plane or a lighter-weight distribution like the ones we compared in K3s vs Kubernetes vs MicroK8s, and focuses on right-fitting requests within it. Cast AI’s automation is closer to DevZero’s model in spirit, adjusting node pools and instance types automatically, though the specific SKU-comparison tooling isn’t documented in as much public detail as DevZero’s. For teams already committed to a single cloud and mostly interested in not overpaying for compute they don’t use, that narrower scope from Kubecost is arguably a feature, not a gap, since it reduces the number of moving parts a platform team has to trust.

Spot Instances and Multi-Cloud Support Across EKS, AKS, and GKE

Spot and preemptible instances remain one of the cheapest levers in Kubernetes cost management, often 60% to 90% off on-demand pricing, and all three tools engage with them differently. DevZero explicitly blends standard and spot pricing, but its own documentation admits a limitation worth flagging: spot automation “works perfectly on AWS, but support for Google Cloud” is more limited, according to DevZero’s 2026 tool comparison post. That’s a meaningful caveat for any team running a majority-GKE footprint expecting the same level of spot automation they’d get on EKS.

Cast AI markets spot automation as a core capability across its supported clouds (AWS, Azure, GCP) without the same publicly documented asymmetry, though independent, apples-to-apples benchmarking of spot interruption handling across all three tools isn’t publicly available as of this writing. Kubecost, true to its reporting-first design, will show you exactly how much of your spend rode on spot instances and how much you saved versus on-demand, but it won’t provision or manage the spot fleet itself; that’s left to the cluster autoscaler or a dedicated tool like Karpenter.

On raw multi-cloud breadth, DevZero currently documents the widest reach, with explicit support for EKS, AKS, GKE, and node pricing examples on Oracle Cloud Infrastructure. Cast AI documents AWS, Azure, and GCP. Kubecost, because it measures cost rather than provisioning infrastructure, is effectively cloud-agnostic by design, tracking spend wherever Kubernetes happens to be running, including on-prem and bare-metal clusters that the other two tools don’t emphasize.

GPU Cost Optimization for AI and ML Workloads

GPU waste is the fastest-growing line item in Kubernetes cost management, and it’s also where these three tools diverge most sharply in how seriously they’ve built for it. Cast AI’s 2026 State of Kubernetes Optimization Report is the most GPU-forward document among the three vendors, reporting an industry-average GPU utilization of just 5%, which the report’s own framing translates to roughly 20 times more provisioned GPU capacity than workloads actually consume. With H100-class instances running $100 to $200 per day on-demand across AWS p5, GCP A3, and Azure ND H100 v5 families, that 95% idle rate turns into real money fast on any cluster running large training or inference jobs.

DevZero markets itself with the tagline “autonomously optimize Kubernetes and AI,” which signals GPU intent, but the publicly available documentation doesn’t itemize specific GPU tactics like MIG (Multi-Instance GPU) partitioning, time-slicing, or idle-GPU scale-to-zero the way the broader GPU cost optimization literature discusses those techniques. Kubecost, similarly, tracks GPU spend by reading Kubernetes labels and cloud billing data, which gives visibility into what a GPU node costs, but it doesn’t natively perform GPU-specific bin-packing or time-slicing.

For a team whose primary Kubernetes cost driver is CPU-bound microservices, this gap matters less. For anyone running GPU-backed training or inference clusters, where a single idle H100 node can burn through a five-figure monthly bill doing nothing, Cast AI’s explicit GPU utilization focus, backed by its own published telemetry across tens of thousands of clusters, is the strongest public signal of the three that the vendor has actually built for this problem rather than bolted it on.

The timing makes the gap sting more. AWS raised H200 GPU instance pricing by 15% in January 2026, the first such increase in roughly two decades of EC2 pricing history, according to reporting on 2026 GPU cloud economics. That means the cost of leaving a GPU node idle didn’t just stay flat while utilization sat at 5%, it went up. A platform team that hasn’t audited GPU scheduling since before that price change is very likely paying more for the same waste than it was a year ago, which is exactly the scenario Cast AI’s report is designed to surface with hard numbers rather than a general warning.

Real-World Cost Scenarios: What Each Tool Actually Costs at Scale

Vendor case studies with named companies and audited savings percentages are rare in this category; none of the three publishes a full roster of named customers with verified savings figures. What is publicly available is enough to build honest, math-based scenarios using each vendor’s own disclosed pricing and telemetry.

• DevZero’s own disclosed example. The vendor’s homepage shows an unnamed account moving from $576,542/month to $176,542/month in optimized spend, a 69% reduction that annualizes to the $4.8 million figure DevZero advertises. It’s a vendor-provided figure, not third-party audited, but the arithmetic is internally consistent.
• Cast AI’s aggregate telemetry. Rather than one company’s story, Cast AI’s 2026 report draws on tens of thousands of real clusters spanning AWS, Azure, and GCP from January through December 2025, plus GPU data through April 2026, to show the industry-wide 8% CPU / 20% memory / 5% GPU utilization baseline every team is quietly living with.
• Named enterprise customers without disclosed savings. Business press coverage of Cast AI’s report names BMW and Cisco as customers of the platform. Neither company has published a specific percentage or dollar savings figure tied to Cast AI, which is worth noting explicitly rather than inferring a number that doesn’t exist in the source material.
• A 200-vCPU mid-market cluster, priced three ways. Using each vendor’s own published or reported rate: Kubecost’s Business tier lands near $680/month at roughly $3.42/vCPU. DevZero’s Pro tier at $5–$7/core lands between $1,000 and $1,400/month for the same footprint. Cast AI’s cost is genuinely variable since it scales with realized savings rather than raw vCPU count, which could make it cheaper on a lightly wasteful cluster and more expensive on a badly overprovisioned one where the automation earns its percentage.
• The 250-core free-tier ceiling. Any team running fewer than 250 combined cores across all clusters can use Kubecost’s Foundations tier at zero cost indefinitely, a real and immediately actionable option for startups and small platform teams that a $5/core DevZero bill or a percentage-of-savings Cast AI contract wouldn’t make sense for yet.

Company Backgrounds: Funding, Founding Dates, and Market Position

Waste at this scale isn’t limited to Kubernetes specifically; it echoes the broader 29% cloud waste figure FinOps teams have reported industry-wide. Company maturity and balance sheet matter more in infrastructure tooling than in most software categories, since a cost-optimization agent needs write access to production clusters, and a vendor that folds mid-contract leaves a gap in a system nobody wants unmonitored.

Kubecost has the most institutional backing by a wide margin, having been absorbed into IBM’s Apptio portfolio after IBM’s acquisition of Apptio. That gives it enterprise sales muscle, a permanent free tier subsidized by a much larger parent company, and long-term stability that a standalone startup can’t easily match. Cast AI, founded in 2019, has raised the most independent venture capital of the three at a reported $272 million total, with its most recent disclosed round a $108 million Series C in April 2025 and a valuation reported near $850 million. DevZero, the youngest of the three at a 2021 founding, has raised a comparatively modest $26 million, most recently a $21 million Series A in January 2023, putting it in a different funding tier than either of its two competitors here.

None of that funding gap directly predicts product quality, but it does shape roadmap pace and enterprise sales support, two things that matter when evaluating a tool that will eventually touch autoscaling decisions on production infrastructure.

The IBM acquisition is the most consequential of the three events for buyers to understand, because it changes who a procurement team is actually negotiating with. A Kubecost Enterprise deal now runs through IBM’s own store rather than a small, independent vendor’s sales team, which typically means longer negotiation cycles but also more standardized contract terms, security review paperwork, and legal language that large enterprise buyers already have templates for. Cast AI and DevZero, as independent venture-backed companies, can move faster on custom terms but carry more single-vendor risk if either company’s funding runway tightens before its next round.

Integration Ecosystem and Open

Kubecost is the only one of the three with a genuine open-source lineage. OpenCost, the engine Kubecost built and donated to the CNCF, remains a Sandbox-stage project as of 2026, meaning it hasn’t yet advanced to Incubating or Graduated status, the CNCF’s markers of broader production maturity and multi-vendor governance. Its GitHub repository sits around 6,600 stars, respectable for a focused infrastructure tool but a fraction of larger CNCF projects. That open lineage matters for teams with a strict no-proprietary-agent policy in the cluster: they can run OpenCost directly, for free, forever, and upgrade to commercial Kubecost only if they need the polished UI, longer retention, or enterprise support.

Neither DevZero nor Cast AI discloses an open-source component in its public documentation; both are proprietary SaaS platforms with in-cluster agents. As for formal FinOps Foundation certification, none of the three vendors has publicly confirmed a specific “FinOps Certified Platform” badge as of this writing, according to the available public documentation, so any FinOps-alignment claims from any of the three should be treated as marketing positioning rather than a verified third-party credential until stated otherwise on the FinOps Foundation’s own site.

What Independent Benchmarks and 2026 Reports Actually Show

Pulling from multiple 2026 tool roundups gives a more balanced read than any single vendor’s own marketing. NudgeBee’s 13 Best Kubernetes Cost Optimization Tools guide frames the category around three questions: does the tool make cost visible, does it recommend fixes, and does it execute those fixes without a human in the loop. Under that framework, Kubecost consistently scores highest on visibility, DevZero and Cast AI score highest on execution, and none of the three sweeps all three categories cleanly.

Cast AI’s own comparison blog, which specifically analyzes Kubecost’s limitations, makes a candid point: Kubecost’s vCPU-based pricing can become unpredictable as a cluster’s vCPU count grows, since the bill scales with monitored capacity rather than with a flat per-cluster fee. That’s a fair critique from a competitor with an obvious incentive to make it, but the underlying pricing mechanics it describes match what Kubecost’s own published tiers show. Meanwhile, DevZero’s own 2026 category guide recommends a simple decision rule: teams spending $10,000 to $100,000 per month on cloud infrastructure should consider DevZero’s free tier as a starting point, while teams spending over $100,000 per month should pair DevZero for automation with Kubecost for financial reporting, an explicit admission from one vendor that the other fills a gap it doesn’t.

The consistent theme across every 2026 roundup reviewed for this piece, including ScaleOps’ benchmark writeup and Amnic’s tool comparison, is that no single platform in this category has fully solved both visibility and automation at once. Every “best of 2026” list ends up recommending a stack of two or more tools rather than a single winner, which is itself useful information for anyone hoping to standardize on one vendor.

5 Use Cases: Which Tool Fits Your Team

The right pick depends far more on team structure and existing cloud spend than on which vendor has the flashiest homepage number.

• Early-stage startup under 250 cores. Kubecost’s Foundations tier is free indefinitely at this scale, with no automation to worry about breaking something before the team has the headcount to babysit it. This is the lowest-risk entry point in the category.
• Mid-market SaaS company, $10K–$100K/month cloud spend, thin platform team. DevZero’s automation-first model removes the burden of manually applying rightsizing recommendations, which matters when there’s no dedicated FinOps engineer to do that work by hand every sprint.
• Enterprise with a dedicated FinOps or finance team. Kubecost’s allocation-by-namespace, by-label, and by-team reporting feeds chargeback models that finance actually trusts, especially once it’s tied into a broader FinOps practice like the one we outlined in our AWS FinOps Agent coverage.
• AI/ML platform teams running GPU-heavy training or inference clusters. Cast AI’s explicit GPU utilization focus and its 2026 report’s granular GPU telemetry make it the strongest documented option here, particularly given the 5% average GPU utilization baseline the same report exposes industry-wide.
• Organizations spending over $100,000/month across multiple clouds. Running two tools in tandem, an automation layer like DevZero or Cast AI plus Kubecost for reporting, is the pattern multiple 2026 guides converge on, rather than expecting either category of tool alone to cover both jobs.

Common Mistakes When Evaluating These Three Tools

Most botched evaluations in this category share a handful of avoidable errors. The first is comparing sticker prices without normalizing for what each price actually buys. A $449/month Kubecost Business quote and a $1,000/month Cast AI Growth quote aren’t the same purchase: one buys reporting and recommendations, the other buys reporting plus execution. Comparing them on price alone, without weighting for the labor a platform engineer would otherwise spend manually applying Kubecost’s recommendations, understates the true cost of the cheaper option.

The second mistake is granting full autonomous write access on day one. Every vendor in this category, including DevZero and Cast AI, benefits from a shadow or advisory-only trial period before flipping automation on for production namespaces. A rightsizing engine that hasn’t seen a full traffic cycle, including a Black Friday spike or an end-of-quarter batch job, doesn’t yet know what “normal” looks like for that specific workload, and an overly aggressive rightsizing pass during an unusual traffic pattern can cause the exact outage the tool was supposed to prevent.

The third mistake, particularly relevant given Cast AI’s percentage-of-savings pricing, is failing to define the savings baseline in the contract itself. If the baseline is measured against the current, already-wasteful spend, a vendor gets credit for savings that basic housekeeping would have captured anyway. Pinning the baseline to a specific date, with a snapshot of cluster configuration attached to the contract, avoids a dispute six months later over what counts as a “saving.”

Migration Guide: Moving Between Kubecost, Cast AI, and DevZero

Because Kubecost is built on the open-out a long procurement cycle. A standard install

helm repo add kubecost https://kubecost.github.io/cost-analyzer/
helm repo update
helm install kubecost kubecost/cost-analyzer 
  --namespace kubecost --create-namespace 
  --set kubecostToken="YOUR_TOKEN"

kubectl port-forward --namespace kubecost deployment/kubecost-cost-analyzer 9090

That gets a cluster reporting cost allocation within minutes, well under the 250-core free tier ceiling for most staging environments. Moving from Kubecost to a more automated tool like DevZero or Cast AI doesn’t require ripping Kubecost out; both vendors’ agents run alongside existing monitoring tooling rather than replacing it, since they act on the cluster rather than only reading from it. The practical migration sequence most 2026 guides recommend is:

1. Run Kubecost or OpenCost first, for two to four weeks, purely for baseline visibility before changing anything.
2. Export or screenshot the baseline cost-per-namespace report as a before-state for measuring later ROI.
3. Install the automation vendor’s agent (DevZero or Cast AI) in monitoring-only or shadow mode if that option exists, rather than granting write access immediately.
4. Review the first batch of proposed changes manually before flipping to fully autonomous mode.
5. Set a hard budget alert threshold in Kubecost so the reporting layer catches any anomaly the automation layer might introduce.
6. Re-run the cost-per-namespace report after 30 days and compare against the baseline to validate the vendor’s savings claims independently.
7. Decide whether to keep both tools running in tandem (the common enterprise pattern) or consolidate to one, based on whether finance needs Kubecost’s granular chargeback data beyond what the automation vendor’s own dashboard shows.

Switching away from Cast AI’s percentage-of-savings billing requires more care than switching away from a flat-rate tool, since the contract terms determine whether savings already banked continue to accrue fees after cancellation. Reading the termination clause before signing, not after, avoids an unpleasant surprise on the final invoice.

Pros and Cons of Each Platform

DevZero

• Pros: Autonomous rightsizing out of the box, cross-cloud SKU price shopping, transparent per-core pricing, works across EKS, AKS, GKE, and OCI.
• Cons: Free tier is monitoring-only and caps at two clusters, spot automation is weaker on Google Cloud than AWS by the vendor’s own admission, smallest funding base of the three which may affect long-term roadmap pace.

Kubecost

• Pros: Genuinely free forever tier up to 250 cores, open-source OpenCost foundation, backed by IBM’s enterprise infrastructure, best-in-class cost allocation and chargeback reporting.
• Cons: No autonomous remediation, someone still has to manually apply every recommendation, vCPU-based pricing can scale unpredictably as clusters grow, OpenCost remains CNCF Sandbox-stage rather than Graduated.

Cast AI

• Pros: Strongest documented GPU optimization focus of the three, largest independent funding base at $272 million, automated remediation, named enterprise customers including BMW and Cisco.
• Cons: No published rate card, percentage-of-savings billing complicates budget forecasting, free tier withholds the automation that’s the product’s main selling point.

The Verdict: Which Kubernetes Cost Tool Wins in 2026

There isn’t a single winner here, and every credible 2026 industry roundup reviewed for this piece reaches the same conclusion. Kubecost wins on trust and cost, a permanently free tier up to 250 cores, an open-source foundation, and the deepest allocation reporting, but it demands a human to close the loop on every recommendation. DevZero wins on autonomous execution and cross-cloud price shopping for teams in the $10K to $100K monthly spend range that don’t have a dedicated FinOps engineer. Cast AI wins specifically for GPU-heavy AI and ML workloads, backed by its own industry-wide telemetry showing 5% average GPU utilization, the single most damning statistic in this entire comparison.

For a team that has to pick exactly one tool today: under 250 combined cores, start with Kubecost’s free tier since it costs nothing and there’s no downside. Above that, with a thin platform team and no dedicated GPU workloads, DevZero’s per-core automation pricing is the most predictable path to real savings. Running GPU clusters for training or inference, Cast AI’s explicit focus on the 5%-utilization GPU problem makes it the tool most directly built for the most expensive kind of Kubernetes waste in 2026. For anyone with the budget to run two tools, the pattern the industry has converged on, automation plus reporting, beats betting everything on one vendor’s roadmap.

Frequently Asked Questions

Is Kubecost still free in 2026?

Yes. Kubecost’s Foundations tier remains permanently free for unlimited clusters up to a combined 250 cores, with 15-day metric retention, and it does not require a credit card or trial expiration. Above that core count, Business tier pricing starts around $449/month or roughly $3.42 per vCPU/month depending on the purchase channel.

Does Cast AI charge a percentage of the money it saves?

Cast AI does not publish an official rate card, but multiple third-party pricing trackers describe its paid tier as tied to a percentage of realized savings, commonly cited between 15% and 20%, alongside a free monitoring-and-recommendations-only tier that doesn’t include automated changes.

Can DevZero, Kubecost, and Cast AI run in the same cluster at once?

Yes, and several 2026 industry guides describe this as a common pattern for larger organizations. Kubecost or OpenCost handles cost visibility and financial reporting while DevZero or Cast AI handles the automated remediation layer, since none of the three tools fundamentally conflicts with another’s read access to cluster metrics.

Which tool is best for GPU cost optimization?

Cast AI has published the most detailed public data on GPU waste specifically, reporting average GPU utilization of just 5% across the clusters analyzed in its 2026 State of Kubernetes Optimization Report. Neither DevZero nor Kubecost has published comparably detailed GPU-specific telemetry or optimization tactics as of this writing.

Is OpenCost the same thing as Kubecost?

No. OpenCost is the open-nated to the Cloud Native Computing Foundation, where it remains a Sandbox-stage project as of 2026. Kubecost is the commercial product, now sold under IBM’s Apptio portfolio, that wraps OpenCost’s engine with a polished UI, longer data retention, budgeting alerts, and enterprise support

How much does DevZero cost for a mid-size Kubernetes cluster?

DevZero’s paid Pro tier is priced at roughly $5 per CPU core/month through its direct plans, or around $7 per core/month through its AWS Marketplace consumption listing. For a cluster consuming 200 vCPUs, that works out to approximately $1,000 to $1,400 per month, though DevZero’s free tier covers basic monitoring for up to two clusters at no cost.

Do any of these tools automatically manage spot or preemptible instances?

DevZero and Cast AI both automate spot instance blending, though DevZero’s own documentation notes its spot automation “works perfectly on AWS” with more limited support on Google Cloud. Kubecost tracks how much of a cluster’s spend rides on spot instances but does not itself provision or manage the spot fleet.

What is the average Kubernetes CPU utilization in 2026?

According to Cast AI’s 2026 State of Kubernetes Optimization Report, average CPU utilization across production clusters analyzed was 8%, down from 10% the prior year, with CPU overprovisioning rising to 69% year over year. Average memory utilization was 20%, down from 23%, with 79% of clusters overprovisioning memory.

Which company owns Kubecost now?

Kubecost is sold under IBM’s Apptio portfolio, following IBM’s acquisition of Apptio. The free OpenCost engine underneath it remains an independent CNCF Sandbox project, so teams that only want the open-

Should a startup pick DevZero, Kubecost, or Cast AI first?

For a startup running under 250 combined CPU cores, Kubecost’s free Foundations tier is the lowest-risk starting point since it costs nothing and requires no automation trust. Once cloud spend grows past that ceiling and a dedicated FinOps hire still isn’t in the budget, DevZero’s per-core pricing and autonomous rightsizing typically deliver faster payback than manually acting on Kubecost’s recommendations.