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 (routing, caching, compression) · monthly reconciliation against raw provider invoices.
Reconciled against every major provider
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.
Model routing, semantic caching, and prompt compression deployed behind feature flags, A/B tested for seven days minimum, graduated only on quality & cost wins.
Each request classified by complexity and routed to the cheapest model that meets your quality bar. Reasoning to flagships; classification and extraction to small fast models.
Near-duplicate prompts fingerprinted with embeddings; identical answers served from sub-10ms cache. Similarity thresholds tuned per feature.
System prompts audited for redundancy. Examples deduplicated. Retrieved context compressed with LLMLingua-style techniques. Every change A/B tested.
Non-interactive workloads routed to batch endpoints (up to 50% discount) with SLO-aware queueing for time-sensitive paths.
Identical capability often costs 2–3× more at one provider. We route by capability-per-dollar, not by the SDK your team happened to start with.
Smart retries and tiered fallbacks beat worst-case over-provisioning. Maintain SLO without paying flagship prices on every call.
We ingest provider invoices, gateway logs, and usage telemetry. By Friday, you have a full map of your LLM spend — by provider, model, feature, team — ranked by dollar waste.
The audit becomes a prioritized engineering plan respecting your compliance posture, latency SLOs, and release process. Quality guardrails agreed per endpoint. You sign off on every change.
FinOps LLM engineers pair with yours to ship each optimization behind feature flags, A/B test for seven days vs. baseline, and graduate to 100% traffic. The cockpit goes live alongside.
Prices move. Traffic shifts. Models ship. The platform watches for drift; we recommend and implement follow-ups so savings compound. Every month closes with a signed Statement of Savings.
Range across engagements, depending on architecture and traffic mix.
Kickoff → first reconciled provider invoice.
Every change A/B tested for 7 days minimum.
Free — credited against implementation.
Free audit. Then you only pay when we save you money — measured against a locked baseline, reconciled to provider invoices.
No savings, no fee · audit credited against implementation · cancel anytime
Cockpit, attribution, and chargeback for teams that want visibility without a managed implementation. Bring your own optimization.
Annual or monthly · upgrade to Performance any time
Missing something? Email hello@finopsllm.com — we respond within one business day.
Visibility, attribution, optimization, accountability — applied to tokens. With a 30-page reference architecture.
A practical playbook for reducing AI spend through routing, caching, batching, and prompt discipline.
Tagging strategies, chargeback math, and gateway integration patterns. With reference code.
The metrics, alerting, and owner boundaries that keep spend changes from hiding in monthly invoices.
Per-feature baselines, seasonality, and how to wire alerts that engineers won't mute.
Reduce retrieval-driven token waste with better chunking, tighter context caps, and selective reranking.
Definitions for attribution, showback, chargeback, cache-read tokens, routing, and cost-per-successful-task.
How to report AI spend by team and product before turning it into budget allocation.
Request tags, invoice reconciliation, retries, and workload classes for OpenAI spend.
Track Anthropic spend by feature, owner, workload, and route behavior without breaking reconciliation.
How to send easy requests to cheaper models and still keep quality, latency, and fallback behavior intact.
The request-level fields that make AI cost changes explainable to engineering and finance.
Compare equivalent model capability across providers without undercounting migration and overhead costs.
Monthly cost-per-successful-task across GPT, Claude, Gemini, Mistral, Llama, on standardized workloads.
Public case studies will be published only after customer approval. Until then, evaluation references are handled privately.
Two weeks. 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.
30-MIN DISCOVERY · READ-ONLY BY DEFAULT · NDA + DPA ON REQUEST · NO COMMITMENT