Cutting LLM spend 41% at a Fortune 100 insurer
A Fortune 100 insurer had 40+ AI workloads across Azure and Bedrock with no unified cost view, no routing controls and a spend curve that was going to cross eight figures inside 18 months. pronix.ai stood up an LLM FinOps program that cut spend 41% with no measurable quality regression.
- Client
- Fortune 100 insurer, 40+ AI workloads
- Industry
- Insurance
- Platform
- Azure AI Foundry · Amazon Bedrock · Kore.ai
*Representative outcome; results vary by client, scope and platform configuration.
The challenge
AI spend had grown 6x in twelve months with no per-workload attribution, no routing policy and no monthly review. Finance couldn't defend the number. The CIO wanted a 30% cost reduction target without cutting workloads or degrading model quality.
Our approach
Instrumented every request
Per-request token, latency, provider, workload, business unit and outcome tagging into a unified cost warehouse. Two weeks to instrument, one week to reconcile — and finance had a defensible number for the first time.
Cheap-first cascade routing
Introduced a router that tried a cheaper model first, escalated on confidence or eval failure. Cached prompts on repeat contexts. Cascade caught 78% of traffic at 1/6th the unit cost.
Monthly FinOps rhythm
A recurring 60-minute governance meeting with the CIO, CFO delegate and workload owners — actual spend vs guardrail, deltas explained, actions assigned. Boring on purpose.
Guardrail budgets, not caps
Per-workload monthly budgets with alerting at 70% and 90%. Only two workloads ever tripped the alert — the visibility itself changed behavior.
Illustrative case study. Scenarios, metrics, quotes and client details are representative composites based on Pronix engagements and industry benchmarks unless a named client is shown with written consent. Outcomes vary by client, scope, data quality and platform configuration. Nothing on this page is a guarantee, warranty or professional advice. See our Terms of Use for the full disclaimer.
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