The inference provider that tells you what you’re actually running
OpenAI-compatible endpoints for open-weight models. We publish the exact quantisation served, the hardware behind it, live latency and 30-day uptime — for every model, on a page anyone can read.
Your first top-up gets 10% extra credit.
from openai import OpenAI
client = OpenAI(
base_url="https://api.infersia.com/v1",
api_key="isk-v1-...",
)
stream = client.chat.completions.create(
model="qwen/qwen3-8b",
messages=[{"role": "user", "content": "Explain KV caching."}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")Change two lines. The official openai SDKs work unmodified — streaming, tools, and all.
Serving right now
Latency and uptime measured from real traffic, not a benchmark.
stepfun-ai/step-3.7-flash
qwen/qwen3.6-35b-a3b
qwen/qwen3-14b
qwen/qwen3-8b
Next up: deepseek-ai/deepseek-v4-flash, qwen/qwen3-30b-a3b-instruct-2507, google/gemma-4-26b-a4b-it, qwen/qwen3-235b-a22b, openai/gpt-oss-120b, meta-llama/llama-4-scout, qwen/qwen3-1.7b. We list what’s planned as clearly as what’s running.
Most providers won’t tell you the quantisation
It’s the number that determines whether the model you’re paying for behaves like the model you benchmarked. We treat hiding it as a bug.
Quantisation, always disclosed
Every endpoint states its precision as a first-class field — fp8, awq-int4, mxfp4 — alongside the engine and version. It’s in the catalogue, in /v1/models, and in a response header on every request.
Real numbers, not marketing ones
Latency and throughput come from your traffic and ours, measured continuously and published per model. Uptime is computed from health probes we keep on record — including the days it wasn’t 100%.
Zero prompt retention, structurally
There is no column in our database a prompt could go in, and the logger drops any field not on an allowlist. It isn’t a policy we follow — it’s a shape the system has.
Works with what you already use
We implement the OpenAI API surface exactly — request shapes, response shapes, streaming, error types and rate-limit headers. If a tool supports a custom OpenAI base URL, it supports us.
- openai-python & openai-node — Official SDKs, unmodified
- Cline, Continue, Aider — Point at our base URL
- LangChain, LlamaIndex, CrewAI — ChatOpenAI-compatible
- LiteLLM, OpenWebUI — Standard provider config
curl https://api.infersia.com/v1/chat/completions \
-H "Authorization: Bearer $INFERSIA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3-8b",
"messages": [{"role": "user", "content": "Hello"}],
"stream": true
}'x-request-id: isr_01KYGHW7S1GTR2AVE40C
x-infersia-provider: infersia
x-infersia-quantization: awq-int4
x-infersia-cost-usd: 0.000001950Every response tells you what served it and what it cost — to the nano-dollar.
Get 10% on your first top-up
Sign up, get a key, and point your existing code at a real endpoint. Your first top-up lands with 10% extra credit — $10 minimum, no subscription, no expiry.