Cost gets you in the door; governance gets you through procurement. Compression that cuts input tokens by ~40%, guardrails, SSO, workspaces, evals and config-as-code routing — the eight things your platform team asks for before they sign, all shipped and live today rather than on a roadmap.
Log analysis, RAG and long-context prompts are mostly repetition. The compression pipeline strips it before the request is billed — composable, per-key configurable, and with zero code changes on your side.
| Engine | What it strips |
|---|---|
| normalize | Collapses redundant whitespace, normalises JSON and drops exact-duplicate blocks. The safe baseline pass, cheap on almost any content. |
| command-output | Detects long logs, diffs and stack traces and truncates them to their informative head and tail, with a note that the rest was cut. |
| rag-dedup | Removes verbatim duplicate blocks across messages — the retrieved chunk that arrives turn after turn is billed once, not N times. |
| caveman | Rule-based compression of verbose prose over 300 characters. User and system content only; the model's own phrasing is never rewritten. |
| tabular | Keeps the header and a bounded number of rows of a large CSV or markdown table, plus a readable notice of how many rows were omitted. |
| llmlingua2 | ML compression (LLMLingua-2) in its own sidecar service, verified at ~40–43% reduction on long-form prose. Fails open if unreachable. |
Eight surfaces, all live today. Each one exists because it is the question that stops a rollout in the review, not in the benchmark.
A configurable rule pipeline screens prompts and responses — PII redaction, banned content, custom webhook checks. Per-org and per-key, fail-closed, with blocked requests logged for review.
Search every request by model, cost, status, or your own custom metadata tags. Stream traces to your own APM over OpenTelemetry, with usage rollups and SLO dashboards built in.
Log in with your identity provider via generic OIDC single sign-on. Split an org into workspaces (teams), assign granular roles (viewer, member, billing, admin), and issue scoped service keys limited to exactly what they need.
Beyond the built-in providers: register any OpenAI-compatible endpoint, use Azure OpenAI, tap DeepInfra's 10-vendor open-weight catalog with automatic OpenRouter failover, or point at your own self-hosted models.
Version your routing policy like code: pin a model or run a weighted A/B split, matched on request metadata and workspace — reviewed, rolled forward, and rolled back without a redeploy.
Scheduled usage exports to your cloud storage, event webhooks (budget tripped, key created, data erased) signed with HMAC, and spend alerts over email or webhook — so the platform tells you, you don't have to watch it.
Benchmark models and routing strategies against your own prompt suites — quality score, cost, and latency per case. Compare a routing-config version against a model pin or a strategy override before you promote it to production.
A weekly sync discovers new models from every provider. A model unreachable for 48 hours is flagged for decommission. Admins promote discovered models to live routing with one click — and the catalog never hard-deletes anything.
Most teams should route to shared models — it is cheaper. But past a certain sustained volume, dedicated capacity wins on both cost and predictability. Reserved capacity and BYOM are onboarding design partners now, on the Enterprise tier.
Reserve dedicated replicas of any open-weight model in the NeuroRoute catalog — GLM-5.2, Kimi K2.7, Llama 3.3 70B, DeepSeek-V3 and more. Your own GPUs, your own throughput ceiling, billed per GPU-hour, not per token.
Have a fine-tuned or proprietary model and no wish to run GPU infrastructure? We host it on capacity isolated to your tenant alone, behind the same OpenAI-compatible endpoint, with the same routing, budgets, guardrails and audit trail.
Dedicated replicas do not queue behind other tenants. A known throughput ceiling and stable tail latency — what agentic loops, batch document processing and coding assistants actually need.
Capacity is provisioned per region so data residency is a deployment choice rather than a policy promise.
Saturating your reservation degrades into routing overflow to a shared model instead of rate-limit errors. You keep serving.
Reserved and BYOM usage lands in the same ledger and the same invoice as your routed traffic — no separate GPU console to reconcile.
The rest of the depth layer — the levers that do not need a chapter of their own, but do turn up in every serious evaluation.
Repeated and near-duplicate prompts return from cache; supported providers cache your system prefix for up to ~90% off repeated input tokens.
Cascade (cheap-first, escalate on failure), task-aware, and Fusion — fan the same prompt to the top-N models in parallel; an independent judge picks the winner verbatim.
Use NeuroRoute directly from Claude, IDEs, and agents over the Model Context Protocol — with OAuth 2.1 click-to-connect.
Route sensitive or high-volume workloads to your own vLLM models on your own hardware, through the same API.
Every decision is explainable: paste a request ID into the Routing Explorer to see which models were considered and why one won.
Thumbs-up / down feedback tunes routing to your definition of quality — not a generic benchmark.
