/case-studies / fintech-ai-cost-finops
LLMOPS & AI PLATFORM

Cutting AI run cost with LLMOps FinOps for a fintech

client
Fintech Scale-up
industry
Fintech
services
LLMOps · Cloud
duration
3 months
fig.80// skillikzmodeltraininfervectorAImodel.evalrollout87%98%accuracyusage87coveragelive
// OVERVIEW

Fintech Scale-up — a fintech organisation — engaged Skillikz on ai cost finops: Token-cost control that halved AI spend without hurting quality. This case study sets out the business challenge, the AI-led approach we took, the technologies involved and the measurable outcomes delivered over 3 months.

// TECHNOLOGIES
LangSmith / MLflowKubernetesTerraformPrometheusAWS Bedrock
-52%
AI spend
same
quality
per-feature
cost view
weeks
to payback
01 // THE CHALLENGE

Rapid GenAI adoption sent model costs soaring with no visibility into where the spend came from or how to control it.

02 // OUR APPROACH

We instrumented token usage end to end, then applied caching, routing and model right-sizing under quality guardrails.

Per-feature token-cost instrumentation
Caching and prompt optimisation
Model routing and right-sizing
Quality guardrails to protect output
03 // THE RESULTS

AI spend dropped 52% with no quality loss, and every feature now has a clear, controllable cost.

We cut the AI bill in half and can finally see where every penny goes.

CTO · Fintech Scale-up
// HOW WE'D DELIVER THIS TODAY

The AI services behind this outcome

A project like this draws on a focused set of Skillikz services — from first assessment to a working pilot and a clear path to scale.

// MORE WORK
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