Frugal AI

Traditional FinOps tooling is retrospective: it tells you the invoice total and leaves you to work out why. Frugal AI takes the opposite stance, pushing cost awareness into the development lifecycle. It reads your code alongside infrastructure-as-code, billing and observability data, and predicts the cost of a change while it is still being written or reviewed, catching expensive regressions before they merge into the main branch.

Its attribution layer is what makes that possible. Shared and aggregated spend gets mapped to the components, call sites and queries driving it, so engineers can see which small portion of code is responsible for the majority of the bill. From there Frugal identifies waste across a wide surface — object storage, logs, metrics and traces, serverless functions, container orchestration, databases, and AI inference via OpenAI, Anthropic and the major clouds — and produces concrete patches, such as rebalancing API versus storage costs, right-sizing logging, applying prompt caching and structured outputs to cut token spend, or tuning function and pod sizes.

Deployment is designed to be safe for cautious buyers: read-only access to source, observability and billing data, TLS 1.2+ and AES-256 encryption, least-privilege permissions, a commitment not to train AI models on your data, and a private deployment option that runs entirely inside your own cloud. The company states it is SOC 2 Type 2 audited. A free audit is the standard entry point, delivering cost attribution to the exact function or query, the top five highest-impact fixes and a shift-left plan compatible with existing CI/CD.