Inference API for open-source and fine-tuned models

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Teams building with AI need to run inference on open-source and fine-tuned models, but existing providers force a painful tradeoff: pay high costs for managed infrastructure with always-on GPUs, or manage the complexity themselves with slow cold starts and DIY orchestration.

Solution concept

A managed inference API that offers fast, cost-efficient access to open-source and fine-tuned models through specialized hardware optimization and per-token pricing with no idle costs. The product achieves this by optimizing the inference runtime for specific hardware (like GH200 accelerators) to maximize throughput while eliminating wasted capacity charges.

Who has it

Software teams and companies using fine-tuned or custom AI models who want managed inference without overprovisioning costs or infrastructure management overhead.

How they handle it today

Today, teams either use managed inference providers that charge for always-on GPU capacity (paying for idle time), or use DIY platforms where they configure vLLM themselves and deal with slow cold starts and orchestration complexity.

Why now

Proliferation of fine-tuned and open-source models has created demand for efficient inference beyond the standard APIs; specialized hardware (GH200) now makes cost-optimized inference feasible; growing AI team adoption increases demand for managed infrastructure that doesn't force overprovisioning.

How it could charge

Per-token pricing (input and output tokens charged separately), with no idle or capacity fees, allowing teams to pay only for actual inference consumed.