Deploy LLM inference without building your own serving stack.
Bitstride gives AI teams an OpenAI-compatible inference layer for custom and open models. Launch production endpoints, meter usage per API key, and keep control of routing, performance, and cost without standing up GPU ops from scratch.
Primary Outcome
Go from model to live endpoint in days instead of spending weeks stitching together runtimes, gateways, quotas, and observability.
Control Surface
Production inference for teams that need control, not black boxes.
Use Bitstride when hosted model APIs are too limiting and self-hosting is draining engineering time. You get a clean serving surface with the controls serious teams need to ship reliably.
Built for operators, not demo traffic
Expose OpenAI-compatible endpoints so your application can switch with minimal integration work.
Track usage by API key and organization, with the controls needed for internal quotas and customer billing.
Route traffic through a deployment-aware gateway built for custom models, not just generic API forwarding.
Scale serving capacity while keeping latency, health, and runtime behavior visible to the team operating it.
Launch
Deploy a model endpoint without piecing together GPU instances, gateways, and serving infrastructure by hand.
Integrate
Point your app at an OpenAI-compatible API surface so teams can adopt Bitstride without rewriting the product.
Operate
Monitor serving health, request behavior, and usage metering from the same control plane that owns deployment routing.
OpenAI-compatible serving surface for chat and model-driven application workloads.
Usage metering and request attribution built into the platform instead of added later through separate tooling.
Deployment-aware routing that preserves control over which model runtime answers each request.
Runtime abstraction that lets Bitstride evolve backend engines without forcing app-level changes.