Ship AI Faster: From model to service in minutes
Infraence bridges the gap between models and production. Deploy on-premises, in the cloud, or both — without exposing your hardware to the internet. Built for seamless scaling and secure access, so ML teams can focus on innovation, not infrastructure.
1from infraence import Infraence
2
3infraence = Infraence("Your connection string")
4
5@infraence.define(name="hello-world", request_body={
6 "audio": {
7 "data_type": "audio",
8 "required": True,
9 "constraints": {
10 "allowed_format": "mp3",
11 "max_size_mb": 1.5,
12 },
13 },
14})
15def handle_request(payload):
16 return {
17 "status": "success",
18 "message": "audio received successfully"
19 }
20
21infraence.run()The gap between trained models and production inference services is costing you time, budget, and momentum.
Getting models to production isn't just about code, it's about building the backend infrastructure to serve it, secure it, and keep it running reliably. That usually means pulling in backend and DevOps engineers, slowing your ML team down and burning resources.
From trained models to production-ready HTTP services, without infrastructure headaches
Infraence removes the need for custom backend code or complex deployment scripts that turn into a liability in the long run. Define your APIs, run your service, and it's instantly available to your team and applications anywhere on the internet.
| Traditional way | With Infraence |
|---|---|
Write and mantain server code | Built-in declarative SDK to create production-ready APIs in just a few lines of code. |
Implement/maintain auth | Manage access from the Infraence studio. No extra code required. |
Set up proxies, gateways and networking | Zero networking setup — No port forwarding, no VPNs. Even for on-premises or hybrid setups. |
Weeks of development | Get online in minutes. |
Why Infraence
Team Autonomy
Get inference services up and running without waiting on backend or DevOps support.
Instant Connectivity
Reach your services right away. No port forwarding or VPNs required.
Built for resilience
Designed with availability from the ground up: Automatic load balancing, fault tolerance, and recovery to ensure requests reach your services.
Cost Savings
Leverage existing on-premises infrastructure to reduce cloud bills while eliminating the complexity of backend and networking setup.
Features Overview
Run Anywhere
Deploy services on your own hardware, in any cloud, or both at once.
API toolkit for ML teams
The Infraence SDK allows your ML/DS team to create inference APIs declaratively in record times. No deep backend knowledge needed.
Managed API Gateway & Security
Built-in TLS, auth, and rate limiting.
Scalable Load Balancing
Infraence balances traffic across all available instances of your service. No reverse proxy needed.
Advanced Data Support
Work with multiple data and file types out of the box along with different types of sources. No need for custom decoding/encoding or validation logic.
Automatic API Documentation (Coming soon)
Generate live, shareable API docs for every deployed service automatically.
Multi-mode request support (Coming soon)
Support synchronous requests, asynchronous WebHook responses, job queueing or batching—ready to go out of the box.
Built-in Observability (Coming soon)
Track your services usage in real time.
How It Works
Client requests
Your clients or applications make inference requests to your previously defined endpoints.
Infraence Gateway
Infraence takes care of validation, auth, rate limiting and forwarding the request to your service.
Your service
Your service gets ready-to-work-with data to run through your model. No direct exposure.
Stop waiting on infrastructure. Start shipping models.
Give your ML team the ability to deploy, secure, and scale inference services — anywhere.