Serve popular open-source models behind an OpenAI-compatible API that scales with your traffic - no servers to manage, low latency worldwide, pay for what you serve.
Inference turns open models (Llama, Mistral, Qwen, DeepSeek, Gemma) into production endpoints in one step. Drop-in OpenAI-compatible API means existing clients just change a base URL. Autoscaling follows demand to zero and back; zero egress keeps costs predictable.
Swap the base URL, keep your app.
Autoscaling absorbs bursts, scales to zero when idle.
Run open models on isolated infra you can also self-host.
From the catalog.
curl https://api.podstack.ai/v1/chat/completions \
-H "Authorization: Bearer $PODSTACK_KEY" \
-d '{"model":"llama-3.1-70b","messages":[{"role":"user","content":"Hi"}]}'Inference is PodStack’s product for serving open-source models as low-latency endpoints. It exposes an OpenAI-compatible API and autoscales on NVIDIA GPUs so you can ship models to production instantly.
Yes. Inference endpoints speak the OpenAI-compatible API, so you can point an existing OpenAI client or SDK at your PodStack endpoint without rewriting your integration.
Inference is built for open-source models. You bring the open model you want to serve and PodStack hosts it behind a low-latency, autoscaling endpoint.
Endpoints autoscale to match traffic, and you are billed per second for the GPU fraction in use with zero egress fees. Pricing is sales-led - contact sales@podstack.ai for a quote.
Inference runs on NVIDIA A10G, L40S, A100 40/80GB and H100 80GB, all supporting fractional allocation from 12.5% to 100% via PodVirt, so you can right-size the GPU to the model.
Endpoints run on PodStack’s operator-owned hardware in ISO 27001 certified data centres - no peer marketplace hosts - with one operator, one SLA and one bill.