Two engineering-grade products that turn cloud spend from a monthly surprise into a controlled, continuously optimized number. Built by the Confluentis cloud-native team.
40%+
Spend Commonly Wasted
3
Clouds, One View
1-click
GitOps Remediation
Any LLM
Bring Your Own Model
A single pane of glass across Azure, Google Cloud, and AWS. An AI agent surfaces waste and anomalies, you approve the fix, and your own pipeline ships the change.
Explore AnomalyzerPercentile and ML based tuning that right-sizes pods and nodes, bootstraps new workloads with sane defaults, and recommends the node types that actually fit your load.
Explore KubeTunerAnomalyzer connects to Azure, Google Cloud, and AWS and gives you one honest view of where the money goes. Stop switching tabs to chase spend. The agent reads usage and cost together, flags what is over-provisioned or idle, and proposes a concrete fix for each item.
Nothing changes without your sign-off. When you approve, the correction is applied the right way: as code, through your GitOps workflow, fully versioned and auditable. No console clicking. No manual deploy corrections.
Illustrative view. Sample data shown.
Azure, Google Cloud, and AWS spend and usage in a single, comparable view. No more tab-hopping between billing consoles.
The agent reads cost and utilization together to flag over-provisioned, idle, and anomalous resources, then proposes a specific fix for each.
Every action waits for your approval. Run in dry-run first, review the proposed change, then decide. The agent never acts on its own.
Approved fixes ship as code through your existing pipeline. Versioned, reviewable, and reversible, with a clean audit trail.
Plug in Claude, Gemini, OpenAI, or a model you host yourself. The reasoning layer is yours to choose and swap.
Let the agent sweep on a cadence and keep a running ledger of savings. Catch drift early, before it becomes a billing shock.
Link your clouds with scoped access and build one unified picture of cost and usage.
The agent flags waste and anomalies and drafts a concrete, costed fix for each finding.
You review each proposal and approve only what you want. Dry-run shows the impact first.
The change is committed and rolled out through your GitOps pipeline, fully audited.
Illustrative view. Sample data shown.
Most clusters are sized by guesswork, so they run hot in some places and pay for idle capacity in others. KubeTuner studies how each workload actually behaves over time and turns that into precise requests and limits, so pods get what they need and nothing more.
It uses percentile analysis and machine learning to bootstrap sensible defaults for brand-new workloads, tune the ones already running, and recommend the node types and pool settings that fit your real load. Apply a fix with one click, or export it to your manifests.
Requests and limits are set from real usage percentiles, not averages or guesses, so you size for genuine demand without padding.
Models learn each workload's pattern over time and adapt recommendations as traffic and behavior shift.
No history yet? KubeTuner sets safe, sensible starting values for fresh pods and nodes so you skip the trial-and-error phase.
Get clear guidance on which node SKUs and pool settings fit your workloads, so you stop overpaying for the wrong instance types.
Tune autoscaler min and max bounds per pool with confidence, balancing headroom for spikes against cost at the floor.
Apply a recommendation per workload in a click, or export it to your manifests and ship it through your own pipeline.
Collect real CPU and memory behavior for every pod and node across your clusters.
Apply percentile analysis and ML to learn each workload's true demand and rhythm.
Get right-sized requests, limits, node SKUs, and pool settings tailored to that demand.
Rectify in one click or export to manifests and roll it out through your workflow.
Approval-first by design. Dry-run anything before it touches production.
Fixes flow through your GitOps pipeline, so everything is reviewable and reversible.
Designed for Azure, Google Cloud, and AWS, with no lock-in to a single provider.
Bring your own LLM and keep reasoning where your governance needs it to be.
Want a walkthrough of Anomalyzer or KubeTuner on your environment? Our team will show you where the quiet waste is hiding and how fast it can be fixed.