Hybrid & Multi-Provider AI Infrastructure
The strongest stacks are hybrid: local hardware for privacy-critical and steady-state workloads, burst to cloud GPUs or APIs when you need scale. We wire it into one gateway with routing, failover, observability, and cost controls — so you own the pipeline end to end.
What we deliver
- Unified LLM gateway: one API endpoint fronting local servers, cloud providers, 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
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.