Deploy any open-source LLM with a single API call.
Production-grade model serving without the ops burden. Point CogniCloud Inference Gateway at any Hugging Face model ID or private weights, and get an OpenAI-compatible endpoint in seconds. Auto-scales to zero when idle and bursts to thousands of replicas under load.
Drop-in replacement for the OpenAI Chat Completions and Embeddings APIs. Switch providers by changing one URL — no SDK changes required.
vLLM-powered continuous batching maximises GPU throughput by processing tokens from multiple requests simultaneously, delivering 3–5× higher throughput than naive batching.
Pair a small draft model with your main model to predict multiple tokens per forward pass. Reduces median TTFT by up to 40% with no quality degradation.
Deployments with no traffic incur zero cost. Sub-2-second cold start means the first request after idle still meets most latency SLOs.
Server-sent events streaming out of the box. Compatible with any SSE client library, enabling real-time progressive text generation in your UI.
Serve INT4, INT8, and FP8 quantised models to reduce VRAM footprint and increase throughput. AWQ and GPTQ quantisation supported natively.
| API format | OpenAI Chat Completions (v1) |
| Serving engine | vLLM / TensorRT-LLM |
| Cold start | < 2 seconds |
| Max context | 128k tokens (model dependent) |
| Streaming | SSE, token-by-token |
| Quantisation | FP8, INT8, INT4 (AWQ/GPTQ) |
| Multi-LoRA | Hot-swap LoRA adapters per request |
| Min replicas | 0 (scale to zero) |
Inference Gateway is currently in development — estimated Q2 2026.
No pricing yet. We offer tailored solutions only.
CogniCloud is in active development. Join the waitlist to get early access and stay updated on our roadmap. No pricing yet — we'll work with each team to find the right fit.
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