Production case study · Nick Cerutti
LLM cost reduction program
$200K/yr aggregate reduction in cloud and LLM spend.
Portfolio of client platforms across the advisory engagements. Cost optimization program · FinOps.
Context and scope
The $200K/year figure is aggregate cloud and LLM spend reduction across advisory engagements. It is not the result of a single sprint, a promised saving, or a measured percentage applicable to every workload.
The same integration mistakes repeated across client codebases: context re-injected without caching, token-heavy integrations, and infrastructure patterns that multiplied spend.
What shipped
- Refactored Anthropic SDK, n8n, and Copilot Studio integrations around token optimization, prompt caching, and low-infrastructure patterns.
Reported outcomes
- $200K/yr aggregate reduction in cloud and LLM spend across the portfolio.
Anonymized client work, reported by Nick Cerutti. Client identities and raw operational records are not published.
What to check in your system
The common pattern was integration overhead: repeated context and token-heavy execution paths. The work focused on those paths and their infrastructure requirements. Estimating a new project's savings still requires its traffic mix, model prices and quality constraints.
- How much repeated context is sent on each request?
- Which tasks require the current model and which have alternatives?
- How will you check quality and latency alongside a lower bill?
Stack: Anthropic SDK · n8n · Copilot Studio · AWS Bedrock
Working on a similar problem?
Three weeks: find where the money goes, fix the largest lines, and leave the controls that stop it coming back.
LLM Cost & Routing Sprint · $15-20K