A drop-in OpenAI-compatible gateway that routes every request to the cheapest model that still clears the quality bar.
Most agent and app traffic is routine work like classification, extraction, formatting, and summarization that never needed a frontier model, yet teams pay frontier prices for all of it. Manually splitting traffic across models is fragile, and quality claims are usually assumed rather than measured.
CogniRank is a cost-optimizing LLM gateway you drop in with a base-URL and key change. It classifies each request, assigns it a quality floor, and routes it to the cheapest model that clears that floor while reserving frontier models for reasoning, code, and creative work. Every call logs its real cost against a frontier baseline, and the platform closes the loop with route-then-verify judging, a learning loop that demotes weak models, and a self-serve benchmark. It runs on Postgres plus Redis with one command.
Routine sub-tasks are routed to small and mid models instead of expensive frontier ones.
Adopt it by changing your base URL and key and setting model to auto.
Every request logs real cost versus the frontier baseline instead of assuming savings.
Each task gets a quality floor and can be verified by a judge that escalates weak answers.
Classifies each task and picks the cheapest or best model that clears its quality floor, restricted to configured providers. A dry-run preview shows a routing decision without spending.
Bundle domain specialists behind one endpoint that auto-selects the right expert for a task. High-stakes experts are verified and PII-handling experts redact data automatically.
Describe a task and an LLM drafts a live specialist for it. You can then fine-tune it by generating data and launching an OpenAI fine-tuning job that auto-pins the tuned model.
A judge scores answers and thumbs feedback rolls up per category and model. Once a pairing underperforms, the router demotes it so quality improves over time.
Equivalent prompts reuse a prior answer for free via exact-hash then embedding-similarity matching, using HNSW-indexed pgvector at scale.
Compose multi-step pipelines where each step is routed independently so cheap steps stay cheap. Chains run in sequence and swarms fan out specialists in parallel with a per-step cost trace.
Run an objective task suite scored by deterministic assertions across auto versus individual models, producing a cost-vs-quality leaderboard with pass rate, cost, latency, and tokens.
Every gateway key carries a monthly budget, token limit, and per-minute cap enforced on each call, and optional PII redaction happens before a prompt ever leaves the gateway.
The gateway labels the task using heuristic patterns, falling back to a cheap LLM only when confidence is low, and assigns it a quality floor.
It picks the cheapest model under the cost policy or the best under the quality policy that clears the floor, honoring providers, learned demotions, and any active A/B split.
It calls the provider and can optionally judge the answer and escalate to a better model if the result is weak.
It records tokens, real cost, and cost avoided versus the frontier baseline for every request.
It is a drop-in OpenAI-compatible endpoint, so adopting it is just a base-URL and key change with the model set to auto, and the Integrate tab gives you pre-filled snippets for OpenAI, cURL, LangChain, and the Vercel AI SDK.
It routes routine work to cheaper models to cut frontier spend 60-90%, and every request logs its real cost against the frontier baseline so savings are measured rather than assumed.
Yes, you can pin a model to bypass routing, force best quality with the quality policy, add route-then-verify, or preview a decision without spending using the route preview endpoint.
Reach out for early access, a live demo, or a partnership conversation.