Why Infercom
Built for serious builders
Premium tokens for production workloads - fast, compliant, and yours to integrate in minutes, with no infrastructure to manage.
Trustworthy
- EU jurisdiction, European data centers
- No US parent, no CLOUD Act exposure
- Zero data retention by default
Seamless
- OpenAI- and Anthropic-compatible
- Latest open-weight models, no lock-in
- Streaming, tools, structured output
Ultra Fast
- Up to 10x faster than GPUs
- Hundreds of tokens/sec, sustained under load
- Purpose-built for inference, not training
Why Speed Matters
Speed decides what you can build
It shows up two ways: it compounds across an agent's many calls, and you feel it the instant a real-time app responds.
1 · It compounds
For agents, latency multiplies
A chatbot makes one call per turn. An agent makes 50-100 inference calls to complete a single task - each one waiting on the last.
At that scale, per-call latency stops being a detail and starts deciding what you can ship. Same agent, same logic, same model - the infrastructure underneath is the difference between a task that finishes in minutes and one that drags for a quarter of an hour.
Illustrative: 50 sequential calls at ~5s vs ~17s per call. Actual results depend on model, context length, and workload.
Voice agents
No awkward pause before the reply
Live coding assistants
Suggestions that keep up with typing
Interactive chat
Answers that start streaming instantly
2 · You feel it
For real-time apps, the first token is everything
In a voice assistant or a live coding tool, what makes it feel human isn't total throughput - it's how fast the response *starts*. A long pause before the first word breaks the illusion of a conversation.
The dataflow architecture delivers a fast time to first token, so real-time apps feel responsive instead of laggy. Every API response reports its measured TTFT, so you can hold your app to it.
Integration
One line to switch
Point the OpenAI SDK at Infercom and keep the rest of your code. Chat completions, streaming, structured output, and function calling all work the way you expect.
from openai import OpenAI
client = OpenAI(
base_url="https://api.infercom.ai/v1",
api_key="your-api-key"
)
response = client.chat.completions.create(
model="MiniMax-M2.7",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)
# usage includes measured tokens/sec + TTFTEU Sovereign models
- in EU data centersGlobal Catalog
- routed outside the EUAn extended catalog of additional open-weight models, routed to partner infrastructure outside the EU for teams that need broader model choice.
For GDPR-sensitive data, use the EU Sovereign models. The Global Catalog runs on external infrastructure outside EU jurisdiction.
View full model catalogAccess Tiers
Start building, scale to production
Self-service on shared infrastructure. Move up as your usage grows.
Self-Service
Start hereDeveloper
Pay as you go
Sign up, create an API key, and start building. Pay only for the tokens you use, against published list prices.
- All EU Sovereign + Global models (list)
- Standard rate limits
- Predictable per-token pricing
- No minimums, no commitment
Build With It
Works with your stack
Point any OpenAI- or Anthropic-compatible client at Infercom and start shipping. Here's where to begin.
FAQ
Questions, answered straight
Need more control?
Same platform, same performance - with more isolation when you need it.