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.
Fortune 100 insurer, 40+ AI workloads · Azure AI Foundry · Amazon Bedrock · Kore.ai
- 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.
Take this case study into your next steering committee.
One page: the business problem, the solution, the technical architecture and the measured outcomes — sized to drop straight into a board pack. Share your work email to unlock it; one form unlocks every gated download on this site.
Board-ready briefs covering the platform and industry behind this program. Free with a work email.
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.
AWS, Salesforce, Microsoft, NICE, Genesys, Kore.ai, Google, ServiceNow, Amazon Connect and logos referenced on this site are trademarks of their respective owners. References are for descriptive purposes only and do not imply endorsement, sponsorship or partnership beyond stated partner relationships. See our Disclosures.
Talk to the team that shipped it.
Book a working session with a pronix.ai lead on this practice — we'll map the approach to your platform, industry and constraints.
Want a similar programme in your environment?
Four fields. Tell us the outcome and timeline, and the lead for this industry replies with indicative scope, team shape and commercial options.
industryInsurance — routed to this team