Optimaq looks at your real traffic and tells you what to change to cut your LLM costs without losing quality en producción.
10 days · one line of SDK · zero commitment
* Internal audit on 2026-09-14 (GPT-5.6 → deepseek-flash). Provisional result on a small sample. Not a universal promise.
One line of SDK captures every call. No code rewrite, no touching production.
Your real traffic is the test bench. We measure quality by its real outcome in your product, not by whether it “looks” good.
You get a clear verdict: what to change, how much you save, at what risk. You decide.
Measured on 2026-09-14 across an internal 3-pipeline workload. The cheapest model does not always win: the one that holds quality does.
No detected degradation… at ~1/10 of the cost.
“Switching this flow from GPT-5.6 to deepseek-flash cuts cost per request by 90% with no quality degradation (safe zone). Provisional: the sample is still too small to promote it to an automatic recommendation.”
ProvisionalIn an internal audit, a flow moved from GPT-5.6 to deepseek-flash at 90% lower cost per request with no detected quality degradation. Four even cheaper candidates did degrade, and the system rejected them. (Measured 2026-09-14. Provisional result on a small sample, not a universal promise.)
Whenever you want, Optimaq sits in front of every call and routes to the optimal model. Opt-in and under your control — never blindly.
Point to Optimaq instead of the provider.
It only changes with guarantees: it doesn’t move traffic until equivalent quality is confirmed, and it’s reversible from minute zero.
A single line of SDK. Competitors make you rewrite code or add proxies.
Not another dashboard: an actionable verdict with savings, quality and risk.
Everyone else tells you if an answer “looks” good. We measure whether it actually worked in your product —the real business outcome— and certify it. Evidence on your own traffic, not another AI’s opinion.
Your data in Madrid (GCP). GDPR processor, per-tenant isolation, switch off anytime.
From OpenAI and Anthropic to the newest challenger — 50+ providers and 250+ models in one integration, and we add new ones every week. You pick where each task runs; we already have them ready.
Python and Node SDKs, 6-line integration. OpenTelemetry-compatible.
Optimaq is the unit economics layer for AI products. It analyzes your real LLM traffic and tells you which configuration to use to spend less without degrading quality, while controlling regression risk.
Observability (Helicone, Langfuse) tells you how much you spend; evaluation (Braintrust, Promptfoo) whether a response “looks” good. Optimaq goes one step further: it certifies quality by its real outcome in your product and decides what to change to spend less without breaking it — connecting cost, quality and risk.
With one line of SDK in Python or Node. It captures your calls without rewriting code or touching production. Full setup is 6 lines and it’s OpenTelemetry-compatible.
Never blindly. The inference gateway is opt-in and off by default; it only moves traffic once equivalent quality is confirmed, and it’s reversible from minute zero.
It depends on your flows. On high-volume tasks we’ve measured large reductions with no quality degradation; in an internal audit on 2026-09-14, ≈90% lower cost per request moving from GPT-5.6 to deepseek-flash, with no degradation detected. Provisional result on a small sample: a measured example, not a promise.
In the EU: GCP europe-southwest1 (Madrid). Optimaq acts as data processor (GDPR Art. 28), with per-tenant isolation and client-side PII redaction.
Paste your prompts into /audit and see, at once, how much you can save and the proof that quality holds. Or try it for 10 days.