One API.
Infinite intelligence.
Lower cost.
Call any model through a single OpenAI-compatible endpoint, backed by a high-performance inference stack running on our own GPU infrastructure.
One API in front of every agent — serving open-weight models ourselves, on E2E Networks' high-performance GPU infrastructure.
One API. Every model, one integration.
Higher throughput. Custom kernels, not stock runtime defaults.
Built to scale. Capacity that grows with you.
Built for the workloads that need it most
Blaze is tuned for inference-heavy products where latency and cost per token compound fast.
Coding agents
Low-latency completions and tool calls, with session affinity that keeps multi-turn agent loops on a warm cache instead of starting cold every turn.
Document generation & OCR
Bulk extraction and generation jobs get routed to whichever pod has headroom right now — no hand-tuned load balancer required.
Image & video generation
Bursty, compute-heavy generation workloads spread across our fleet automatically, so you never have to provision GPUs yourself.
The engine underneath
A full inference stack tuned end-to-end — custom kernels, cache, and scheduling — with intelligent routing on top.
Custom inference kernels
Hand-tuned CUDA kernels and a highly optimized serving engine squeeze more tokens per second out of every GPU, at lower latency than stock runtimes.
Unlimited KV cache
Context doesn't get evicted the moment memory tightens. Long conversations and large documents stay warm, so repeat and multi-turn work stays fast and cheap.
Throughput-tuned serving
Continuous batching, scheduling, and memory management tuned specifically for high-concurrency inference — not a generic model server running defaults.
Intelligent routing
Every request lands on the pod with the most headroom and the warmest cache — automatically, across every model we serve.
One API. Infinite intelligence. Lower cost.
Get API access and start calling Blaze in minutes — no infrastructure to manage.
Get API access