AI FinOps Agent

Controls model spend with routing, caching and per-workload budgets. The agent attributes every call to a workload, team and business outcome, so cost per interaction is a number the business can manage. It routes each request to the cheapest model that passes the quality bar, applies prompt and response caching, and enforces per-workload budgets. Quality is measured alongside cost, so a saving that degrades answers is caught rather than celebrated.

It works across Model gateway, Billing / cost data, Agent runtimes, Observability. The named integration surface is Model gateway or provider APIs; Cloud billing and cost data; Agent runtimes for workload tagging; Evaluation harness for quality checks.

We deliver it on the platforms you already own — Azure OpenAI, AWS Bedrock, Google CCAI. Pronix.ai is a systems integrator, not a software vendor, so the code, prompts and evaluation sets belong to you.

Supervised autonomy. No routing change ships without passing the evaluation set. Per-workload budgets with alerting and hard stops on runaway spend. Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached. Cost and quality are always reported together, never separately.

Model traffic flowing through a gateway or otherwise instrumentable. An evaluation set to protect quality during routing changes. Agreed workload tagging so cost can be attributed.

Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account. Customer and employee data stays inside your tenancy and region; no training on your data by default. PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy. Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.

Delivery runs in 4 phases. Weeks 1–2 · Scope: Traffic and spend baseline, workload tagging model, quality bar agreed. Quarter 2+ · Scale & run: All workloads governed, with monthly FinOps review and forecasting.

40–70% lower cost per AI interaction at constant quality. Expected ranges from comparable production engagements: Cost per AI interaction 40–70% lower; Spend attributed to a named workload 95%+; Quality change during optimisation Held flat on the evaluation set; Forecast accuracy on monthly AI spend Within 10%. Your baseline is agreed before build starts and reported on the same measures after go-live.

Production pilot: Top workloads instrumented and optimised with a before/after cost and quality report. Scale and run: Enterprise AI FinOps practice with budgets, forecasting and monthly governance reporting. Fixed-price pilot with milestone billing, then a retained pod with quarterly outcome reviews.

Price on request. Every agent is scoped to your integration surface, data readiness and compliance posture, so we send a written estimate after a 30-minute scoping call rather than publish a band that would not apply to you.