Prove the model before production.
Teams adopting open-weight models usually learn about fit the expensive way: on live traffic, after the integration is already built. InferNext moves that discovery to the front of the process. Evaluate on your own data, benchmark under your own traffic shape, then promote the exact artifact you validated.
Requests in flight. One is judged on first-token latency, another on throughput, another on simply not failing.
The gap
A leaderboard score is not a fit test.
An open-weight model gets picked on published benchmarks, wired into an application, and shipped. The questions that actually decide whether the choice was right go unanswered until then: does accuracy hold up on our inputs, does the tail of the latency distribution fit our budget, what does a request cost at the traffic shape we really see.
Those answers arrive with production traffic, which is the most expensive place to get them and the hardest place to act on them. Swapping a model after the integration is built means redoing the integration.
The workflow
Evaluate, benchmark, promote.
Three steps in a fixed order, because each one answers a question the next one depends on. The artifact that comes out the far end is the same one that went in.
01
Evaluate
Deploy an open-weight model to a test endpoint and run your own evals against your own data. Accuracy on your task, measured by you, before anything is wired into the application.
02
Benchmark
Drive the same endpoint with your real traffic shape and measure what production will care about: time to first token, throughput, and cost per request under load you recognise.
03
Promote
Move the exact artifact you validated onto a dedicated or private endpoint. Same weights, same configuration, nothing rebuilt on the way to production.
How it runs
Kubernetes underneath, open-source inference servers doing the serving.
- Test endpoints
- Isolated endpoints for validation. Deploy a model, point an eval harness or a load generator at it, take the answer, tear it down.
- Dedicated endpoints
- Capacity reserved for one customer. Nobody else's traffic shares the queue, so the behaviour you measured is the behaviour you keep.
- Private endpoints
- The same stack running inside your own cloud account, so model traffic and data stay on infrastructure you already control.
- Artifact promotion
- Weights and configuration travel together as one validated artifact. There is no rebuild between test and production for drift to enter through.
- Billing
- By the hour for the capacity you hold, rather than per token.
Status
Early, and working with design partners.
InferNext is pre-launch. We are working with a small number of design partners to shape the evaluation and benchmarking workflow around real adoption decisions rather than imagined ones.
If you are choosing an open-weight model right now and would rather decide on evidence than on a leaderboard, write to founders@infernext.ai.