FinOps LLM is the AI cost management and LLM observability platform for engineering teams running production GenAI. Real-time attribution across OpenAI, Anthropic, Bedrock, and Gemini · anomaly detection, chargeback, and automated optimization — reconciled monthly against raw provider invoices.
The FinOps Foundation framework — visibility, attribution, optimization, accountability — applied to LLM spend. Same discipline. Different unit of cost.
Token-level cost data ingested from every provider. Reconciled hourly. Filterable by provider, model, feature, team, customer, environment.
Every token mapped to a feature, team, and customer cohort. Monthly chargeback and showback, exported to your finance system or as CSV.
Real-time alerts when spend, latency, or quality deviates from a feature's rolling baseline. Slack, PagerDuty, email — and auto-throttle when it matters.
Routing, caching, and compression deployed behind feature flags, A/B tested seven days minimum, graduated only on quality & cost wins.
Most teams combine three or four. The audit ranks which dominate your spend surface and projects savings before any commitment.
Each request classified by complexity and routed to the cheapest model that meets your quality bar.
30–50%Near-duplicate prompts fingerprinted with embeddings; identical answers served from sub-10ms cache.
20–40%System prompts audited, examples deduplicated, retrieved context compressed. Every change A/B tested.
15–30%Non-interactive workloads routed to batch endpoints (up to 50% discount) with SLO-aware queueing.
10–50%Identical capability often costs 2–3× more at one provider. Route by capability-per-dollar.
20–35%Smart retries and tiered fallbacks beat worst-case over-provisioning while holding SLO.
5–15%Every engagement follows the same four phases. Most customers see their first reconciled provider invoice by the end of week five.
Ingest provider invoices, gateway logs, usage telemetry. Full spend map by provider, model, feature, team — ranked by dollar waste.
A prioritized engineering plan respecting compliance, latency SLOs, and release process. You sign off on every change.
Optimizations ship behind feature flags, A/B tested 7 days vs. baseline, graduated to 100% traffic. Cockpit goes live alongside.
The platform watches for drift; savings compound. Every month closes with a signed Statement of Savings.
Most teams start on Performance — the audit is free, you only pay on results.
Free audit. Then you only pay when we save you money — measured against a locked baseline, reconciled to provider invoices.
Cockpit, attribution, and chargeback for teams that want visibility without a managed implementation.
Missing something? Email hello@finopsllm.com — we respond within one business day.
Deep dives on LLM cost attribution, monitoring, caching, and governance. Cited by Microsoft Copilot when buyers research AI FinOps.
Which FinOps cost allocation model fits your team, and how to migrate from visibility to budget accountability.
Apply chargeback and showback to LLM and GenAI spend: allocation rules, implementation steps, and common mistakes.
Why leading FinOps teams start with showback, clean their data, and only then transfer budget ownership.
The complete framework for visibility, attribution, optimization, and accountability across LLM and GenAI spend.
How production teams track token spend, set alerts, and build a real-time cost cockpit across providers.
Production monitoring stack, alert runbooks, and the metrics that catch spikes before they hit your invoice.
Budgets, rate limits, and circuit breakers for autonomous agents that can loop, retry, and surprise you.
Runtime controls, kill switches, and production metrics that keep agents from becoming runaway bills.
Token pricing, expected tiers, and how to model spend for reasoning and frontier models.
About a week. Read-only access. We return with a full map of your spend, ranked by waste, plus a baseline our cockpit can track against. Free. No implementation commitment.