Kubernetes Workloads and AI infrastructure.
Tuned together.

Cast AI continuously optimizes Kubernetes and the resources beneath it. Start read-only, automate when you’re ready, and verify the results in production.

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See where capacity is idle

Six workloads sit across four partially used nodes.

Resources usedResources requested

6 workloads4 active nodes

Trusted by 2,100+ companies

Start with the problem you have today

Pick the one that sounds like your week, and see how we fixed it for other teams.

Your engineers spend too much time on infrastructure upkeep

Cast AI rightsizes workloads, provisions nodes from your node templates, and consolidates underused ones, all within the policies you set. When a fix belongs in code, such as Helm values or Terraform, it opens a pull request for your team to review.

See Kubernetes automation in action

Automation activityWithin policy

Applied within policy

Workload requests adjustedapi-service
2000m → 750m CPU✓
Underused nodes consolidatedEligible workloads moved
4 nodes → 3✓
Node provisioned from your node templatePending pods scheduled
+1 node✓

Sent to your team as a pull request

Update Helm valuesdeployment.yaml

cpu: 2000m → 750m

Checks passedAwaiting your review

85–130h

of engineering time recovered each month

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You pay for capacity your workloads never use

Cast AI sets each workload’s CPU and memory requests from observed usage, then bin-packs pods onto fewer, better-fitting nodes and moves eligible workloads to spot instances. Your node count follows real demand instead of peak requests.

See how cost optimization works

Workload optimizationPolicy active

1. Requests set from observed usage

api 4.0 → 1.5 vCPU

worker 8.0 → 4.2 vCPU

inference 12 → 4 GiB

RequestedUsedNew request

2. Pods repacked onto fewer nodes

Before: 6 nodes

After: 3 nodes

Spot

Node count follows demand, not peak requests6 → 3 nodes

50–80%

lower Kubernetes costs

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The GPUs you need aren't available where you run

When your region runs out of GPUs, Cast AI provisions nodes in other clouds and regions through OMNI Compute and schedules eligible workloads there, while your control plane stays in your current cluster. For GPUs you already have, time-slicing and MIG let several workloads share one device.

Learn about cross-cloud GPUs

GPU capacity

Capacity from other clouds and regions, or your own machines

Oracle Cloud
Nebius
AWS
Google Cloud

Your own VM or bare metal machineConnected to your cluster

OMNI Compute

Your clusterControl plane stays here

H200 in your region: 0 available
Remote H200 node added: Ready
Eligible workloads scheduled on it: Running

H200

GPUs sourced from Oracle when Azure ran short

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Full disks crash your apps, and throttled disks slow them down

Cast AI expands volumes before they fill and tunes IOPS and throughput on supported disks in GKE and EKS. It starts in dry-run mode, so you review every change first.

See storage autoscaling in action

Storage AutoscalerDry-run

Capacitypostgres-data-0

82% used, above the 80% trigger100 → 120 GiB planned

PerformanceEBS gp3

Demand above current IOPS

Demand Current: 3,000 IOPS125 MB/s Planned: 6,000 IOPS250 MB/s

2 planned changes.Nothing applied yet.

Storage Autoscaling

Start in dry-run.

Review planned capacity and performance changes before enabling them.

Read the documentation

Your database is slow and overloaded

Cast AI’s Database Optimizer sits in front of PostgreSQL and MySQL. It caches repeated reads, pools connections so traffic spikes don’t exhaust your connection limit, and flags slow queries with suggested fixes.

Learn about Database Optimizer

Database OptimizerCaching enabled

PostgreSQL · MySQL

ApplicationOptimizer proxyDatabase

Repeated readsReturned from cache, not your database
Cache hit
Connections pooledTraffic spikes stay under your connection limit
480 → 40
Slow query flaggedorders by customer, 1.8 s.Suggested fix: add an index on customer_id
Review fix

Writes and uncached reads pass through to your database.

80–90%

cache hit rate with Database Optimizer

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You don't trust automation with production yet

Cast AI starts in read-only mode, so you can review recommendations on selected clusters first. Once you enable changes, Cast Radar reports show cost next to error rate and latency (P50, P95, P99), so you can verify the savings came without a performance hit.

Read the guide

Cast RadarSelected clusters

Cluster cost

$90,219.34/mo

10.02% lower

LatencyError rate Stable

P508.14 ms

P9564.32 ms

P99580.23 ms

65%

lower production-cluster costs, with no impact on application availability

Read the story

Cast AI observes, decides, and acts

It reads how your workloads and nodes behave, works out what should change, and applies eligible changes to live infrastructure within your policies. When a change belongs in your repo, it opens a pull request for your team to review.

Observe and decide

Workloads and nodes, tuned together

Workload rightsizing informs node capacity, bin-packing, and placement. Node conditions and available capacity inform workload adjustments and remediation. Cast AI connects both while preserving existing policies and integrations.

Keep your existing Cluster Autoscaler, Karpenter or use Cast AI's native autoscaler.

Workload optimizationWithin policy

Workload demand

CPU

Memory

HPA replicas

Infrastructure

Capacity

Placement

Purchasing

Your integration and policies set the limits.

Act at runtime

Change it live

Resize workloads, add nodes, move pods, and grow volumes, within your policies, where your setup supports it.

Infrastructure activityExample

Workload resized2000m 750m
Node provisioned+1 node
Eligible pods moved6 pods
Volume expanded200Gi 260Gi

Within your policies

Act in code

Fix the source

When a change belongs in code or configuration, Kimchi agents open a pull request. Your team reviews and merges, and your CI/CD ships it.

Sync resource requestsExample PR

deployment.yaml

cpu: 2000m cpu: 750m

Checks passedAwaiting review

“I had an aha moment – an iPhone moment – with Cast. Literally two minutes into the integration, we saw the cost analytics, and I had an insight into something I had never had before and had tried to get for a very long time.”

Dekel ShavitSr. Director of Engineering

“And with Cast AI, we didn’t do anything. Like, we didn’t do the move before, we didn’t do the move after. So there was a lot of human resources and time saved here. That was a very good experience. And again, from a cost perspective, it was highly optimized.”

Achi SolomonDirector of DevOps

“In my mind, it’s one less thing to worry about, and therefore teams can be focused on other things of potentially higher value. So having [Cast AI] just run in the background with a good level of confidence that we’re running as efficiently as we can, balancing the service that we’re providing – that’s great.”

Dan WhiteleyChief Technology Officer

Ranked #1 in Cloud Cost Management on G2

Among 223 solutions Winter 2026 Report

G2 Winter 2026 Users Most Likely to Recommend

AWSQualified Software
Google CloudPremier Partner
Cloud NativeComputing Foundation
FinOps FoundationSilver Member
TokenomicsPremier Member

One platform for Kubernetes and the infrastructure beneath it

Workloads, nodes, storage, databases, and GPU capacity, with automation you can verify, not just trust.

Kubernetes Optimization

Right-size workloads, and the nodes follow

Cast AI sets CPU and memory to real usage, then consolidates pods and fits node capacity to match. Keep Cluster Autoscaler or Karpenter, or use Cast AI’s own autoscaler.

api-serviceRight-sized

CPU

Memory

PreviousNew requestUsed

Cast Radar