AI Cost Optimization Consulting
Cut LLM waste with routing, caching, and context engineering — you pay the provider, we do not resell tokens, and evals keep quality honest.
- Service
- LLM Platform
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
AI cost optimization consulting baselines LLM spend (API tokens, ChatGPT Enterprise and Microsoft Copilot seats, judges, retries), then reduces waste with routing, prompt/semantic caching, and context engineering — with evals so quality does not drop. You pay OpenAI, Azure, Anthropic, or Google directly; ReinforcedX does not markup tokens. A four-week standard stands up the control plane in your stack with SSO/IdP unchanged.
Why teams pick this engagement
LLM Platform × EnterpriseQuality gates on savings
We do not shrink context or swap to a cheap model unless the golden set still passes. Cost-only LLMOps is how products quietly get worse.
Unit economics you can read
Cost per successful task, not cost per token in a vacuum. Cache hits, retries, and judge calls are in the same ledger.
Router over a single vendor
Model-agnostic routing: small models for classification, larger for hard reasoning, batch where latency allows. Copilot and ChatGPT seats are a separate line from API waste.
Finance and platform in the room
FinOps gets a bill-of-materials; engineers get budgets per route. Shadow Custom GPTs that burn tokens without owners get unpublished.
Four-week standard
Week 1 spend and trace baseline, week 2 router and cache design, week 3 shadow with evals, week 4 handover of dashboards and policies.
Contracts stay yours
You keep OpenAI, Azure, Anthropic, or Google contracts. ReinforcedX never sits in the billing path. Client pays inference to the provider with no markup.
Key takeaways
- 01
The expensive line is usually unbounded context, retries, and a frontier model on work a small model would pass — not the list price of GPT.
- 02
Client pays inference to the provider with no markup. If a vendor needs to resell tokens to “save” you money, walk away.
- 03
Routing and caching are LLMOps changes; they ship only if golden-set evals and AI observability still look healthy.
- 04
Custom GPTs and Copilot seats are real money; unowned prototypes should be unpublished, not optimized.
- 05
Four weeks produces a baseline, a router, caches, and dashboards you own — not a one-time invoice scrub.
What the engagement covers
How we work
- 01
Discover
Week 1: bills, traces, seat lists, shadow GPTs, quality SLOs. Identify the three routes that dominate spend.
- 02
Design
Router policy, cache TTLs, context budget, eval slices that must not regress, FinOps dashboard spec.
- 03
Build
Week 2–3: router and caches in your cloud, instrumentation, license cleanup, evals on cheaper routes.
- 04
Validate
Side-by-side quality on golden sets, cost replay on last month’s traffic, rollback drill.
- 05
Enable
Week 4: dashboards, budgets, owners, 30 days on-call. You keep paying providers; we never invoice tokens.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
LLM Cost Baseline Worksheet
Map tokens, seats, caches, judges, and retries to cost-per-task — the same sheet we fill in discovery.
Get the worksheet ·Routing and Cache Eval Checklist
What must stay green when you add a cheaper model, prompt cache, or truncated context.
Get the checklist ·