Take Kubernetes on-demand spend to $0
By 2028, 95% of new AI deployments will use Kubernetes (Gartner®). Flexera One Container Optimization helps teams eliminate Kubernetes overprovisioning and safely unlock spot usage at scale, freeing up dollars and valuable engineering time.
Maximal savings
It’s simple: you can save more with automation than you can with manual efforts
No engineering overhead
Unlock savings through autonomous infrastructure and workload rightsizing while reducing friction between FinOps and Engineering teams
Stronger FinOps
Make better business decisions with workload-level visibility of container usage and cost
No tradeoffs
Get the most out of your cloud spend without sacrificing performance or reliability
How we do it
Discover what fully automated Kubernetes optimization feels like
Up to
70%
potential Kubernetes
savings
15–25%
smaller clusters
with continuous right sizing
(Flaschenpost)
<2
hours
to production
deployment
Say no to tradeoffs with reliable K8s automation that delivers maximal performance at minimal cost
Flexera One optimizes both Kubernetes nodes and the pods they support, maximizing cluster efficiency through pod and node rightsizing, bin‑packing, and intelligent autoscaling
Flexera One Container Optimization pays for itself.
That’s right. There are no risks and no upfront costs. Our outcome-based pricing model is aligned with your goals.
You pay us a small percentage of what we save you. That’s a guarantee.
Why it matters
Spot usage – at scale
- Automatically provisions the lowest cost instance: spot, covered, on-demand
- Proactively avoids spot interruptions and ensures reliable capacity
Application-aware autoscaling and bin packing
- Node/VM autoscaling ensures all pods have a place and capacity to run
- Efficiently bin-packs pods onto nodes and scales down when appropriate
Autonomous pod rightsizing
- Container-level rightsizing recommendations enable you to tune requests and limits
- Auto-rightsizing automatically implements recommendations without stopping the pod
Get my free savings analysis
Secure read-only access. Metadata only—no changes to your production environment
FAQ – Flexera One Container Optimization
Customers typically see significant, measurable cost reduction quickly:
- Up to 70% savings reported with automated optimization
- Immediate reduction in overprovisioned compute and unused capacity
- Improved performance and cluster efficiency at the same time
👉 You don’t just see insights—you get continuous cost and performance improvements
Container Optimization continuously optimizes both infrastructure and workloads in real time—not just recommendations.
- Automatically selects the lowest-cost instance (Covered → Spot → On-Demand)
- Rightsizes pods in place based on actual usage (no restarts)
- Scales clusters up/down and bin-packs workloads to eliminate waste
👉 Result: savings are realized automatically, not dependent on engineering action
Most tools optimize one layer (e.g., cluster scaling or pod sizing). Container Optimization optimizes both together, continuously:
- Combines node/VM autoscaling + pod-level rightsizing
- Adds commitment-aware provisioning (uses RIs/SPs first)
- Replaces multiple tools with a fully managed, closed-loop system
👉 Outcome: higher savings and lower operational overhead vs. DIY stacks
No—Container Optimization is designed to make Spot safe for production:
- Predicts interruptions and proactively replaces instances
- Falls back to on-demand capacity when needed, then reverts to Spot
- Maintains application availability during transitions
You get deep discounts without sacrificing reliability
No. Container Optimization is designed to fit into your existing DevOps tooling, so you can treat optimization like code and embed it into the CI/CD processes.
- Works with common IaC, code delivery, and config mgmt systems (Terraform, AWS CloudFormation, Spinnaker, Chef, Ansible) so engineering teams can focus on performance and innovation.
- You can roll out cluster-by-cluster while keeping ownership with platform/DevOps teams and visibility for FinOps.