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. 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.
Full-precision Hinglish conversations at 1,000+ RPM
A social media app needed high-quality, multi-turn conversations in Hinglish — without the quality loss its users noticed on quantised models.
BF16
Full-precision Gemma 4 31B
~10K
Tokens per prompt, on average
1,000+
Requests per minute at peak
< 5 s
P95 end-to-end latency
The requirements
- Hinglish (Hindi–English code-mixed) content
- High-quality Gemma 4 31B generations — quantised versions from other providers led to users dropping off the app
- Long prompts, averaging around 10K tokens
- Multi-turn conversations
- Peak load above 1,000 requests per minute
How Blaze solved it
Full-precision quality
The customer runs the BF16 version of Gemma 4 31B on Blaze. Compared with quantised versions from other providers, user adoption in the app was higher.
High KV-cache reuse
Multi-turn prompts share most of their context from turn to turn. Blaze's KV-cache reuse lowered the effective price, and our team worked with the app developers to raise prompt cache hit rates.
Routing and scaling
Efficient routing and scaling optimisations handled peaks above 1,000 RPM while keeping P95 end-to-end latency under 5 seconds.
Keep your code. Change the endpoint.
Add your Blaze API key and update the base URL.
from openai import OpenAI
client = OpenAI(
api_key="BLAZE_API_KEY",
base_url="https://blaze.e2enetworks.net/v1"
)
response = client.chat.completions.create(
model="qwen/qwen3.8-27b",
messages=[
{"role": "user", "content": "Implement hello world in Python"}
]
)Built by E2E Networks
Blaze is built and operated by E2E Networks Limited, India's GPU cloud for AI and ML — the same infrastructure trusted by startups, enterprises and government workloads.
2009
Founded
NSE: E2E
India's first NSE-listed cloud provider
MeitY
Empanelled Cloud Service Provider
10,000+
Innovators building on E2E
One API. Infinite intelligence. Lower cost.
Get your API key and start calling Blaze in minutes — no infrastructure to manage.
Get API Key