Who this is for
- Teams already calling two or more model APIs with no single front door
- A local box plus a frontier API, and no routing policy between them
- Spend, privacy class, and failover that need to be explicit
Who this is not for
- “Just give us ChatGPT” with no local or multi-provider plan
- Wanting us to hold production API keys forever as your hosted gateway vendor
What we deliver
- Unified LLM gateway: one OpenAI-compatible endpoint over local servers, burst GPUs, and commercial APIs
- Smart routing by cost, latency, privacy class, and capability — with automatic failover
- Token accounting, budgets, and per-team / per-app usage tracking
- Observability: request logging, tracing, and quality evaluation hooks
- Vendor-independence audit: know exactly what breaks if any single provider changes terms or prices
How an engagement runs
- Inventory. Current providers, spend, and which prompts must stay local.
- Policy. Routing rules by privacy class, cost, and latency. Frontier APIs stay on the 5% that actually needs them.
- Deploy. Gateway + budgets + logs on infrastructure you keep. Runbook included.
Stack we actually use
- LiteLLM
- OpenAI-compatible apps
- llama.cpp / vLLM backends
- optional cloud burst
Tested on our fleet
Dated lab notes — not a client logo wall.
Related services
Common questions
Why not just use one provider's API?
Single-provider setups create pricing lock-in, rate-limit ceilings, and data-handling terms you don't control. A hybrid pipeline lets you keep sensitive workloads local, route commodity traffic to the cheapest capable endpoint, and switch providers without rewriting your applications.
What is a "token pipeline"?
Everything between your application and the model weights: gateways, routers, caches, logging, evaluation, and fallbacks. Owning it means your AI behavior, costs, and data flows are under your control rather than dictated by a single vendor dashboard.
Can you mix self-hosted models with commercial APIs?
Yes — that's the point. A typical setup serves steady-state traffic from on-prem hardware, bursts overflow to cloud GPU providers, and reserves frontier commercial APIs for tasks that genuinely need them.
LiteLLM vs Portkey vs just environment variables?
Env vars are how teams start and how they lose the plot. We deploy LiteLLM when you need one OpenAI-compatible front door, per-route budgets, and logs you own. Portkey and friends are products; we are not reselling one. If LiteLLM is the wrong tool for your constraint, we say so.
Do prompts leave the building?
Per route. Local backends never send the prompt out. API routes do, by definition — that is why privacy class is a routing rule, not a slogan.
What breaks if OpenAI changes terms or prices?
That is the vendor-independence audit. We write down which apps, which models, and which fallback already works so a terms change is a config edit, not a rewrite.