Find the waste. Show the arithmetic. Raise the pull request.

AI FinOps is the use of autonomous agents to find and remediate cloud waste, rather than only report it. Where traditional FinOps tooling surfaces spend and leaves the change to an engineer, an AI FinOps agent ranks findings by dollar impact, attaches the specific fix and can apply it once approved.

Visibility tools tell you what you spent and where. NudgeBee adds the remediation: a ranked recommendation with a published method behind it, the exact change required and the ability to apply that change as a cloud API call, a Kubernetes patch or a pull request against your repository.

Cloud cost management tools show what you spent, broken down by service, team and tag, and leave the change to an engineer. AI FinOps tools go further and rank what to do about it. NudgeBee attaches the exact remediation to every ranked finding, then applies it as a cloud API change, a Kubernetes patch or a pull request once you approve, so the saving actually lands.

CPU requests are set at the 99th percentile of max rate. Memory is set at observed peak plus 15%, or plus 25% where an OOMKill happened. It requires at least 50 data points across a two week window, so it will not act on thin data or a single quiet weekend.

Only where you have configured it to. Auto-Pilot supports dry run, resource filters and guardrails that skip execution if the agent is disconnected or a pull request is already open for that resource. Outside Auto-Pilot, every change is gated behind explicit human approval.

AWS, Azure and GCP, plus Cloud Foundry and Kubernetes, through one collector and one recommendation model. The rule set is 236 AWS rules, 220 Azure, 54 GCP and 34 generic. AWS has the deepest service coverage of the three. If you are evaluating the wider operations category alongside cost, see our comparison of the best AIOps platforms.

NudgeBee runs OpenCost inside your own cluster for Kubernetes cost allocation, so the allocation model will be familiar. The difference is what happens next: allocation data feeds the rightsizing algorithms and the ranked recommendation queue rather than terminating in a dashboard. Kubernetes your biggest line item? Compare the best Kubernetes cost optimization tools and see how NudgeBee lands rightsizing as an approval-gated pull request.

Cloud spend anomalies are detected against a 30 day clean baseline. A spike must clear three thresholds at once: a Z score of 3.0 or more, at least $50 in absolute change and at least 20% in relative change. It resolves automatically after two consecutive normal days.

No. NudgeBee is self-hosted in your own cluster with zero telemetry, and cloud billing and usage data is queried in place. AWS onboarding uses least-privilege STS AssumeRole with an External ID, so you are not handing over long-lived credentials.

Yes. The full implementation is readable: the rule set, the scoring bands and the rightsizing parameters. You can self-host it in your own cluster and run it in production internally for free, at any scale. The 2 cluster or cloud account limit applies to the hosted Cloud SaaS free tier.

AI-FinOps is included in every NudgeBee edition, alongside the other Persona Bees. The Community edition is free: you self-host it with no cluster limit. On the hosted Cloud SaaS there is a free forever tier covering up to two clusters or cloud accounts, with no credit card required. Enterprise Support starts at $500 a month, billed annually and priced by environments and clusters, and Enterprise Platinum is priced on environments, clusters and accounts. Full details are on the pricing page.