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AI Engineers Labs
AI Cost Optimization

Token spend grew 3×.Your AI roadmap didn’t.

Most LLM features ship without cost discipline. We audit where token spend is leaking, then engineer the routing, caching, prompt, and retrieval changes that take 30–80% out — without dropping answer quality or NPS. Eval-gated. Provider-agnostic.

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Token budgets stop being a footnote at around $50K/month. By then the routing decisions, prompt patterns, retrieval shape, and model selection that worked at prototype scale are quietly burning enterprise cash. Cost optimization is engineering work — not vendor negotiation, not “use a cheaper model.” We instrument what you’re actually paying for, then ship the architecture changes that hold the savings.

What you get

Reductions, not benchmarks.

  • A cost audit

    Every route, intent, and customer segment instrumented. Per-token costs attributed to features. Projected savings band before a line of code changes.

  • A routing layer

    Cheaper model when context allows. Premium model only when eval shows it's needed. Per-request observable, per-tenant tunable, per-intent cost-capped.

  • Caching that actually hits

    Exact-match + semantic + structured-response cache. Hit rate as a KPI on your dashboard, not a footnote in your runbook.

  • Prompt and context discipline

    System prompts and few-shots audited and tightened. RAG that retrieves what the LLM will actually use, not what scored highly on a benchmark.

  • An eval harness gated on quality AND cost

    Every routing or prompt change passes the same regression bar as a model upgrade. Cost-per-resolved-intent is a tracked metric, not a quarterly estimate.

What we change

Specifics, because ‘use a cheaper model’ isn’t a strategy.

Token attribution
Instrument what's actually spent per route, intent, customer, and feature. Every dollar attributed before any change is shipped.
Model routing
Provider-aware routing across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, and open-weights via Together / Groq / Replicate. Failover and cost caps per provider.
Prompt compression
System prompts audited line-by-line. Few-shots replaced with structured output where it saves more. Token counts measured per intent, not eyeballed.
Context window discipline
Retrieve only what the LLM will use. Aggressive top-k tuning. Chunk-level relevance gates so the generation step isn't paying for noise.
RAG cost reduction
Smaller embedding models when corpus quality allows. Lower-tier vector store when latency permits. Hybrid scoring tuned to cut downstream LLM calls.
Caching layer
Exact-match + semantic + response-shape cache. TTLs tied to underlying data freshness. Cache invalidation owned, not hand-waved.
Inference routing
Bedrock and Vertex enterprise contracts honoured where price is right. Together / Groq for open-weights latency. Load-balanced with health checks and cost caps.
Engagement model

How it runs

Timeline4–8 weeks to first savings ship
Pod size1 architect · 1 ML engineer · 1 MLOps
DeliverablesAudit · routing layer · caching · eval harness · runbook
Pricing postureFixed-scope; savings-share available over $1M/year run-rate
Proof
A Tier-1 retailer's customer-service AI burned $480K/month in OpenAI tokens. We instrumented per-intent spend, routed 62% of queries to a smaller model behind eval gates, and added a response-shape cache. Spend fell to $115K/month over six weeks; CSAT moved up 4 points in the same window.

Tier-1 retailer · Customer service AI · OpenAI + open-weights routing

Frequently asked

What buyers actually ask

What's the typical reduction range, and where will we land?
30–80%. Teams already routing across providers, with caching in place, and with prompt discipline tend to land at the lower end — there's less low-hanging fruit. Teams running a single premium model on every request with verbose system prompts and uncached responses tend to land at the upper end. We can usually project the band within the first two weeks of the audit.
Will answer quality drop?
No. Every routing, prompt, or retrieval change passes the same regression bar as a model upgrade — golden-set evals, LLM-as-judge for faithfulness, latency budgets. We block the deploy on regression, not the other way around. If a cheaper model fails the eval gate for a given intent, that intent stays on the premium path.
Does this work with our Azure OpenAI, Bedrock, or Vertex enterprise contracts?
Yes. We honour the procurement work your team has already done — if you have committed spend on Azure OpenAI or Bedrock, those endpoints stay first-class in the routing layer. We add the engineering discipline on top: per-route attribution, eval-gated routing, cache layers.
What about open-weights routing?
Routed in where residency, latency, or unit economics require it — typically via Bedrock (Llama, Mistral), Together, Groq, or Replicate. We're provider-agnostic on the inference side and provider-honest on the eval side: an open-weights model only takes a route if it passes the same regression bar as the proprietary one.
How long until we see real savings?
First wins ship in week 2–3 — caching, obvious routing changes, prompt cleanups against the highest-volume intents. The full audit and architecture work lands in weeks 6–8. Savings are tracked weekly against the pre-engagement baseline and reported to the steering committee.
What's NOT in scope?
Negotiating your cloud or model-provider contracts. That's a procurement function. We engineer the reductions — the routing, caching, prompts, retrieval, and architecture changes — and we leave you a defensible cost-per-intent number you can take into the next round of those negotiations.
How do you make sure the savings persist after handover?
The runbook covers exactly that. The eval harness keeps running on every PR in your CI. The cost dashboard ships with alerting on per-route spend anomalies. The routing layer is something your team owns and tunes, not something we sit on top of. Steady-state engagement is optional, not built in.
Next step

Talk to an engineer, not a salesperson.

30 minutes. No slides. Bring an architecture, a stalled roadmap, or a vendor proposal you want a second opinion on. We'll tell you what we'd do.

Book a 30-min architecture reviewGet the Enterprise RAG Playbook