Do we need Cast AI, or can you do this with open source only?

Cast AI is optional. As a Cast AI partner we can deploy, integrate and operate the platform, which is the fastest route to continuous automated optimization. We also deliver the same outcomes with open-source tooling such as Karpenter, KEDA, HPA and VPA. If you would rather avoid a commercial platform, we will build it with open source and hand it over.

Will rightsizing and Spot adoption risk our production reliability?

Not when it is done in the right order and with guardrails. We rightsize from observed production usage rather than assumptions, keep headroom on latency-sensitive services, and roll out Spot to workloads that can tolerate interruption with proper disruption budgets and draining. Changes go in phases against a measured baseline, with rollback available at every step.

How long does the assessment take, and what do we get?

The assessment runs for around two weeks and is fixed scope. You get a spend and utilization baseline by cluster, namespace and workload, a prioritized backlog of actions with estimated savings for each, and a recommended node, Spot and commitment strategy. You can take that roadmap and implement it yourself, or continue with us.

Do you support clusters outside the major cloud providers?

Yes. We work across AWS EKS, Google GKE and Azure AKS, and also on OpenShift, Rancher, kOps and self-managed clusters in data centers. The optimization levers are the same; only the provisioning and commitment mechanics differ. If you run hybrid or on-premises Kubernetes, mention it in the assessment and we will scope it accordingly.

Kubernetes Cost Optimization

Kubernetes Cost Optimization that holds up in production

Most clusters reserve far more CPU and memory than they use. We find that waste, fix the configuration behind it, and automate the node and workload decisions that keep spend down, using Karpenter, KEDA and the Cast AI platform.

See how an engagement runs

Assess → Implement → Operate · AWS EKS, GKE, AKS, OpenShift and bare metal

Optimizing clusters running on

On-premise Kubernetes
Amazon EKS
Google GKE
Azure AKS
OpenShift
Rancher

Why Kubernetes bills grow faster than usage

Cost problems on Kubernetes are rarely caused by the cloud provider. They are caused by defaults: requests nobody revisited, environments nobody scheduled, node groups nobody resized, and autoscaling components that were deployed but never tuned together.

The fix is not a discount negotiation. It is an engineering problem, and it responds to engineering work.

Explore our Kubernetes consulting practice

01
Requests sized for peak, paid around the clock
Teams set CPU and memory requests for the worst case they can imagine, and the scheduler reserves that capacity permanently. Utilization settles in the single digits while the bill reflects the reservation.
02
Non-production that never sleeps
Development, staging and review environments run the same 24/7 footprint as production, even though nobody is using them overnight or at the weekend.
03
Fragmented nodes and static node groups
Fixed node groups and partially filled nodes leave gaps the scheduler cannot use. Workloads wait for capacity that is already paid for but not usable.
04
On-demand by default
Spot is never adopted, or is adopted for a week and abandoned after the first interruption. The discount that should be funding the next initiative stays on the table.
05
Autoscaling that cannot finish the job
The HPA scales pods, the pods do not fit, and the cluster scales a node. Scaling decisions are made in isolation, so the cluster pays for them twice.

Three levers that actually move the bill

Resource optimization, infrastructure optimization and event-driven autoscaling solve different parts of the same problem. Pulled together, they compound. Pulled in isolation, each one leaves savings behind.

01Resource optimization

Make the workloads tell the truth about what they need

Most of the waste sits in the gap between what a container requests and what it consumes. We measure real usage per workload and feed accurate requests back into the scheduler, which is the precondition for every other optimization to work.

Continuous rightsizing of CPU and memory requests at the container level
HPA and VPA tuning, including removing over-requests that block bin-packing
Guaranteed vs burstable QoS review so critical workloads keep their headroom
Cost allocation by cluster, namespace, team and workload label

02Infrastructure optimization

Provision the fleet your workloads actually ask for

Karpenter replaces static node groups with just-in-time provisioning from a broad instance pool, then consolidates what is left over. We run it with the guardrails that keep it safe in production, across clouds and on-premises clusters.

Already on Karpenter? The remaining leakage is usually in pod requests and Spot handling. Our Cast AI partnership closes exactly that gap.

Karpenter NodePools, instance diversity and consolidation policy design
Spot adoption with interruption handling and graceful draining
Node fragmentation and bin-packing improvements beyond native consolidation
Reserved Instance and Savings Plan coverage matched to a stable baseline
Multi-cloud and on-premises: EKS, GKE, AKS, OpenShift, Rancher, kOps

03Autoscaling

Scale on the signals that actually predict demand

Kubernetes autoscaling is CPU-centric by default, which is the wrong signal for queue consumers, cron jobs and bursty event workloads. KEDA lets those workloads scale to zero and back on the metric that matters.

KEDA scalers for Kafka, SQS, RabbitMQ, Prometheus, cron and more
Scale-to-zero for event-driven and batch workloads
Scheduled scale-down for non-production environments overnight and at weekends
Hibernation of development and staging clusters to remove idle spend

Cast AI partner

CloudRaft

A Cast AI partner, and the engineers to back it

CloudRaft is a Cast AI partner. We connect your clusters, integrate Cast AI with the Karpenter, Prometheus and Terraform stack you already run, and operate it as part of your platform rather than a dashboard somebody checks occasionally.

The platform does the continuous work. Our engineers own the architecture decisions around it, so the automation is landing on clusters that are configured to benefit from it.

Not ready for a platform decision? We deliver the same outcomes with open-source Karpenter, KEDA, HPA and VPA alone. No vendor lock-in either way.

What the platform adds on top of Karpenter and HPA

Continuous workload rightsizing that feeds accurate requests back to Karpenter
Bin-packing and rebalancing beyond native node consolidation
Predictive Spot interruption handling, not reactive draining
Cost visibility by cluster, namespace and workload in real time

Industry benchmarks

30–70%: typical cloud cost reduction
69%: of clusters over-provision CPU
8%: average CPU utilization
~77%: compute savings, Spot-heavy fleets

Source: Cast AI Kubernetes cost benchmarks (2025–2026). Figures are the platform vendor's published industry benchmarks, not a guarantee of results for any individual cluster.

How an engagement runs: Assess, implement, operate

Cost optimization fails when it is treated as a one-off audit that produces a PDF. We work in the order that protects reliability: understand the baseline, change one thing at a time, and keep measuring after the project ends.

Assess

Fixed scope, about two weeks

We map spend by cluster, namespace and workload, measure real utilization against requests, and quantify the gap. You get a prioritized roadmap with estimated savings per action, an owner for each item, and no obligation to proceed.

Cost breakdown and utilization baseline
Prioritized optimization backlog with estimated savings
Recommended node, Spot and commitment strategy

Implement

Phased, with rollback at every step

We implement against the baselined cluster in phases, starting with the lowest-risk, highest-return changes. Rightsizing and autoscaling go first, then node provisioning, then Spot and commitments once the workload profile is predictable.

Karpenter, KEDA and rightsizing rolled out with guardrails
Spot and commitment strategy executed
Savings measured against the original baseline

Operate

Ongoing, or hand over with enablement

Cost optimization is not a one-time project. We monitor for regression, tune as traffic patterns change, and report savings monthly. If you would rather run it in-house, we hand over runbooks and train your platform team instead.

Regression monitoring and continuous tuning
Monthly savings and efficiency reporting
Runbooks and team enablement for in-house ownership

Frequently asked questions about Kubernetes cost optimization

It depends on how the cluster is currently configured, which is why the engagement starts with a two-week assessment rather than a promise. Clusters that have never been rightsized and run on-demand by default typically have the most to recover. Cast AI's published industry benchmarks put typical reductions at 30 to 70 percent, and we give you an estimate specific to your cluster before you commit to any implementation work.

Karpenter works on the infrastructure side: it decides what nodes your cluster should run, provisioning them just in time from a broad instance pool and consolidating underused ones. KEDA works on the workload side: it scales workloads on external signals such as queue depth or event volume, and can scale them to zero. They are complementary. Karpenter reduces what you pay for capacity; KEDA reduces how much capacity you need in the first place.

Book a Kubernetes cost assessment

Tell us what your clusters look like. We will tell you where the money is going and what it would take to get it back.

Start with the numbers

Bring a recent cloud bill and cluster access, or just a rough sense of your monthly spend. The assessment works either way, and it is the fastest route to a credible savings estimate.

We work with your stack

Karpenter, KEDA, Prometheus, Terraform, Crossplane, Cast AI. We adapt to what you already run rather than replacing it. If you are still building the platform underneath, our platform engineering and observability teams can help there too.

No lock-in

Every recommendation comes with the open-source alternative spelled out. If you would rather own the implementation, we hand over the runbooks and enable your team.

Book a cost assessment

Reach out to a Kubernetes cost optimization engineer for a technical discussion.

What our customer say about us

CloudRaft played a key role in establishing robust, industry-standard DevOps practices for Enkrypt AI. Their expertise in scalable enterprise Kubernetes deployments, networking, monitoring, and operations ensured seamless customer deployments. Their reliable support significantly strengthened our platform, and we highly recommend CloudRaft.

Prashanth Harshangi

CTO, Enkrypt AI

You guys were awesome to deal with and quickly understood our problem, and implemented a solution. Appreciate your support and we will keep you in mind for future engagements.

Joseph Vinikoor

Director, IFF

CloudRaft supported Rezolve.ai in upgrading Kubernetes and Postgres while safely managing live production infrastructure. They researched relevant technologies, provided valuable recommendations, and delivered the project with professionalism and dedication. We highly recommend CloudRaft.

Udaya Bhaskar Reddy

CTO, Rezolve.ai

CloudRaft have carved out a niche and established themselves as leaders in Observability consulting. At KloudMate, we are thrilled to collaborate with them, together aiming to set newer standards for APM and Observability practices, for businesses across the board.

Pranab Buragohain

CEO, KloudMate

CloudRaft demonstrated strong Red Hat OpenShift backup expertise and quickly earned our trust. They resolved complex configuration issues, set up a reliable test environment with replicated production data, and delivered a successful outcome. Their professionalism and commitment make them a partner we would confidently engage again.

Ran Livneh

CTO, WeRTech Israel

Our partnerships in the ecosystem

Kubernetes Cost Optimization that holds up in production

Why Kubernetes bills grow faster than usage

Requests sized for peak, paid around the clock

Non-production that never sleeps

Fragmented nodes and static node groups