Optimize every Kubernetes node. Automatically.
TERAKUBE optimizes your nodes, right-sizes workloads and HPAs, and scales idle resources to zero. Powered by AI, it autonomously makes decisions, forecasts costs, and predicts demand.
No application code changes · Least-privilege access · Node optimization & management
Illustrative figures from a sample cluster — your numbers come from your own usage.
Watch how the savings happen
From a cluster full of idle capacity to measurable savings.
One platform for node optimization, reliability & AI
A core that runs in your cluster and an AI service that drives the decisions — covering node optimization, workload & HPA right-sizing, remediation, automation, and cost dashboard, so you don't need a stack of separate tools.
Intelligent Node Autoscaling
Match node capacity to real demand across managed nodegroups and Karpenter, with pod-capacity safety checks — the core of node optimization.
Node Scheduling & Management
Place, schedule, and consolidate nodes around operational windows and cost objectives, keeping the fleet right-sized as demand changes.
Workload & HPA Right-Sizing
Compare requests, limits, and HPA targets against real utilization to reclaim over-provisioned capacity and pack tighter.
Workload Scale-to-Zero
Scale idle workloads to zero and restore them automatically when demand returns — one tracked cycle with the zero-period cost measured.
PDB Operator
Automatically manage PodDisruptionBudgets so scaling, remediation, and node changes stay safe and available.
Hotspot Detection
Surface nodes and workloads running hot so you can rebalance before they cost you or page you.
Kubernetes FinOps
Cost, potential savings, and realized savings in one place — computed from your own usage.
GPU Cluster Optimization
Track GPU utilization, bin-pack GPU workloads, right-size GPU nodes, and reclaim idle accelerators — the most expensive capacity in your cluster.
Cluster utilization & node count
● liveEverything you'd buy separately — in one platform
Most teams assemble node autoscaling, right-sizing, remediation, and a cost dashboard from different tools. TERAKUBE replaces the whole stack with one AI-driven platform that runs in your cluster.
TERAKUBE replacesone platform ↓
- Node autoscaler / provisioner
- Bin-packing & consolidation
- Workload & HPA right-sizing tool
- Idle / scale-to-zero controller
- Pending-pod & OOMKill remediation
- Node pressure & eviction handling
- Cost / FinOps dashboard
- Spot-price forecasting
- PDB management & scheduling
- Alerting & notifications
One platform, not a stack
Node optimization, right-sizing, remediation, automation, and FinOps in a single system — instead of stitching together five point tools.
AI that decides, not just charts
Forecasting and learned policies drive real actions, so the platform anticipates cost and demand rather than only reporting them.
Reliability built in
Pending pods, OOMKills, and node pressure are remediated automatically — optimization never trades away availability.
Runs in your account
In-cluster core, least-privilege access, no application code changes — your data stay with you.
TERAKUBE optimizes and manages your nodes — so you don't have to
Right-size nodes and workloads, scale idle workloads to zero, and let AI forecast cost and demand. Warm-but-unused capacity is a recurring bill — TERAKUBE finds it and acts, automatically.
Node optimization & management
TERAKUBE right-sizes node capacity to real demand — on managed nodegroups and Karpenter — schedules and consolidates nodes, and never provisions a node that can't run your critical pods. This is the heart of the platform.
GPU cluster optimization
GPUs are the most expensive capacity you run. TERAKUBE tracks GPU utilization, bin-packs GPU workloads, right-sizes and consolidates GPU nodes, and reclaims idle accelerators — where a single wasted node can cost more than a whole CPU fleet.
Workload & HPA right-sizing
Requests and HPA targets set far above real usage waste capacity on every node. TERAKUBE right-sizes workloads and tunes HPA against actual utilization so pods pack tighter and you run fewer nodes.
Idle workloads scaled to zero
Dev, staging, and off-hours services run with no traffic. TERAKUBE detects them and scales eligible workloads to zero, restoring them the moment demand returns.
AI predictions & forecasting
Predictive models forecast price trends and workload demand, so node and scaling choices anticipate cost and load instead of only reacting to them.
PDB operator & safe scaling
Unmanaged PodDisruptionBudgets make scaling and node changes risky. The PDB operator manages budgets automatically so optimization never threatens availability.
Hotspot detection
Hot nodes and workloads cause instability and surprise cost. TERAKUBE surfaces hotspots so you can rebalance before they page you or blow the budget.
Kubernetes FinOps & cost visibility
Spend hides across nodes, namespaces, and workloads. TERAKUBE gives you FinOps in one place — monthly cost, potential savings, and realized savings — computed from your own usage and attributed where it belongs.
Savings dashboard & analytics
A live dashboard shows what was optimized, what it saved, and the trend over time — so the impact of every action is always visible and easy to report upward.
Dashboards & AI-driven insight
Realized savings over time
savings dashboardWhere savings come from
FinOpsWorkload & HPA right-sizing
requested vs usedPredicted demand drives replicas
AI predictionsNodes reclaimed as optimization runs
FinOps in one place
Cost attributed per namespace, with the savings TERAKUBE can reclaim — export-ready for finance and platform reviews.
| Namespace | Cost/mo | Reclaimable | Util |
|---|---|---|---|
| production | $8,420 | $1,980 | 61% |
| data-platform | $4,760 | $1,220 | 47% |
| ml-training (GPU) | $3,980 | $1,510 | 34% |
| Total | $17,160 | $4,710 |
See what optimization could return
Estimated current spend for your inputs: $2,803/mo
A rough, transparent model from your inputs — not a guarantee. Install the free Insight plan to see your real numbers.
Everything commercial cost platforms do — plus reliability, your own AI, and no lock-in
Commercial platforms optimize cost from their SaaS control plane. TERAKUBE matches the optimization and adds reliability remediation.
Your AI, your account
Run the ML models in your own cloud — encrypted, never leaving your boundary. No mandatory vendor SaaS control plane holding your cluster data.
% -of-savings billing
Flat, transparent per-cluster pricing. You keep 100% of what you save instead of handing a cut to the vendor every month.
Cost AND reliability
Not just a bill-cutter: pending-pod remediation, OOMKill fixes, node-pressure guards, PDB management and alerting keep the cluster healthy while it gets cheaper.
| Capability | TERAKUBE | Commercial cost platform | Cluster autoscaler | Manual / scripts |
|---|---|---|---|---|
| Cost & savings visibility (FinOps) | partial | |||
| GPU cluster optimization | partial | manual | ||
| Intelligent node autoscaling | basic | |||
| Nodegroups + Karpenter | partial | one | ||
| Workload & HPA right-sizing | manual | |||
| Idle workload scale-to-zero | partial | manual | ||
| Pending-pod remediation | partial | partial | manual | |
| OOMKill detection & fix | partial | manual | ||
| Node pressure handling | partial | partial | manual | |
| PDB operator + hotspot detection | partial | |||
| Time-based node scheduler | partial | manual | ||
| Automation engine + notifications | partial | |||
| Integrated AI models (forecast, policy) | partial | |||
| Neural spot-price forecasting | ||||
| Self-hostable AI — models stay in your account | n/a | |||
| Runs fully in-cluster (no data leaves) | ||||
| No mandatory SaaS control-plane | ||||
| Transparent per-cluster pricing | partial | n/a | n/a | |
| No % -of-savings billing | n/a |
Category comparison, not a specific vendor. Capabilities vary by product and configuration — verify against current vendor docs.
How TERAKUBE AI works with Kubernetes
The core runs in your cluster; the AI service runs where you choose. Every call is authenticated and cross-checked.
Real clusters. Real math. No hand-waving.
We don't promise a magic percentage. Here's an anonymized design-partner cluster with the numbers shown — and how the savings were reached.
A 42-node EKS cluster, one billing cycle
- 15 nodes reclaimed via autoscaling + right-sizing
- 9 dev/staging workloads scaled to zero off-hours
- Requests tuned against real utilization → tighter bin-packing
Built to pass security review
Your data never leaves your account
TERAKUBE runs in your cluster; the AI can run in your own cloud. No mandatory SaaS control plane ingesting your metrics.
Least-privilege by design
Scoped IAM (IRSA) and RBAC — only the permissions optimization needs. No application code changes.
From cluster to measurable savings
Observe first, automate when you're ready — you stay in control the whole way.
Connect
Install into your cluster with least-privilege access. No app changes.
Analyze
TERAKUBE reads workloads, nodes, utilization and efficiency.
Optimize
AI-driven policies act on idle workloads and infrastructure.
Measure
Track optimization activity and estimated savings over time.
Start free — see your savings today
The free Insight plan installs read-only in minutes and shows your real cost and savings — no card, no sales call. Start in observe-only, then enable automation when you're confident.
Install with Helm
One command deploys the core (EFS-backed), UI, and optional ML.
helm upgrade --install terakube \ ./terakube -n terakube-core \ --create-namespace \ --set storage.fileSystemId=fs-xxxx
Set up IAM (IRSA)
Scope least-privilege AWS access for the core. Supports nodegroups and/or Karpenter.
# EKS: nodegroups and/or Karpenter bash terakube-irsa-setup.sh
Connect & optimize
Open the UI, review savings in observe-only, then enable automation.
kubectl -n terakube-core \ get pods,svc,pvc # open the LoadBalancer URL
Packaging that scales from one cluster to your fleet
Per-cluster, billed monthly. Start free, automate when ready.
Insight
See where your Kubernetes budget goes.
- Cost & utilization visibility
- Idle workload detection
- Savings estimates (read-only)
- Single cluster
- Active optimization
- AI models
Starter
Active optimization, up to ~10 nodes.
- Everything in Insight
- Active optimization & automation
- Scale-to-zero
- Node autoscaling & scheduling
- AI forecasting
- 1 cluster · ~10 nodes
Pro
Active optimization, up to ~50 nodes.
- Everything in Starter
- Up to ~50 nodes / cluster
- Full AI model suite
- Savings analytics history
- Central ML
- Priority support
Scale
Active optimization, up to ~150 nodes.
- Everything in Pro
- Up to ~150 nodes / cluster
- Central ML/AI
- Advanced scheduling
- Priority support
Enterprise
For large Kubernetes fleets.
- Unlimited nodes
- Many clusters
- Custom policies & SLAs
- Private / self-hosted AI
- Security review
- Dedicated support
Node ceilings: Starter ~10, Pro ~50, Scale ~150. A 14-day grace applies if you temporarily exceed your tier. Savings estimates come from your own cluster data — no guaranteed percentages.
Common questions
Deploy TERAKUBE and start optimizing
A practical guide: prerequisites, IAM, install with Helm, connect your cluster, and turn on automation safely.
Getting started
Create an account, connect a cluster in observe-only, and review your first savings estimate.
Kubernetes integration
Least-privilege RBAC in-cluster, plus scoped IAM via IRSA on EKS.
Architecture
Core + ML service + UI. Data on EFS so it survives node scale-down.
Cluster & tools
An EKS (or AKS/Kubernetes) cluster, kubectl and helm 3, and — on EKS — the aws-efs-csi-driver add-on plus an EFS filesystem whose mount targets cover your node subnets.
One command deploys the namespace, RBAC, service account, EFS StorageClass + PVC, the core (EFS-backed), the UI, and optionally the ML service.
helm upgrade --install terakube ./terakube \ --namespace terakube-core --create-namespace \ --set serviceAccount.roleArn=arn:aws:iam::ACCT:role/terakube-core-irsa-role \ --set storage.fileSystemId=fs-xxxxxxxx \ --set core.publicUrl=https://your-core-lb-or-domain \ --set imagePullSecret.create=true \ --set imagePullSecret.dockerconfigjson="$(base64 -w0 ~/.docker/config.json)"
The hardened script auto-detects managed nodegroups, Karpenter, or both, and scopes to exactly the node role(s) in use.
# Prompts for cluster name, region, policy/role names bash terakube-irsa-setup.sh # Detects node role(s), ensures the OIDC provider, creates the # IAM policy + IRSA role, and annotates the service account.
kubectl -n terakube-core get pods,svc,pvc
kubectl -n terakube-core rollout status deploy/terakube-core
# Grab the UI / API LoadBalancer address
kubectl -n terakube-core get svc terakube-core \
-o jsonpath='{.status.loadBalancer.ingress[0].hostname}'Observe
Review cost, idle workloads, and estimated savings while the core runs read-only.
Scope policies
Choose which namespaces and workloads are eligible for optimization.
Enable automation
Turn on scale-to-zero, autoscaling, and scheduling when confident.
Measure
Watch realized savings and optimization activity accumulate.
Start optimizing your Kubernetes infrastructure
Connect your cluster, see wasted capacity, and let AI-driven optimization reduce your cloud spend.
Start FreeTalk to the TERAKUBE team
Sales, enterprise, technical questions, or support — send a message and we'll get back within one business day.
Sales & enterprise
Large or multi-cluster environments, procurement, security review.
Technical questions
Integration, architecture, and per-platform behavior.
Support
Existing users needing help with an active cluster.
Prefer email? contact@terakube.com