Managed AI inference platform for production workloads
PolarGrid is an AI inference platform for teams that need production models to stay fast and available. We run your models on dedicated NVIDIA GPUs and handle serving, scaling and monitoring, while you keep control of your model weights, configuration and data. Tell us what you're deploying and we'll send you a deployment plan and quote.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://<your-endpoint>/v1",
});
await client.chat.completions.create({
model: "your-fine-tuned-model",
messages,
});- Uptime SLA
- 99.99%
- NVIDIA GPUs, never shared
- Dedicated
- To bring new capacity online
- Minutes
- OpenAI-compatible API
- 1 endpoint
Managed inference on dedicated NVIDIA GPUs
On PolarGrid, your models run on dedicated NVIDIA GPUs reserved for your workloads. Managed inference means your inference provider operates the infrastructure that serves your model, so your team can ship product instead of running GPU clusters.
PolarGrid runs the GPUs, the serving stack, scaling and monitoring. You keep control of the model you deploy, its weights, how it is configured and the data that passes through it.
If your team would rather manage the full stack on its own hardware, see our bare metal GPU servers.
PolarGrid runs
- Dedicated NVIDIA GPUs
- The serving stack
- Scaling
- Monitoring
You control
- The model you deploy
- Its weights
- How it is configured
- The data that passes through it
Inference as a service with base, expansion, and burst capacity
PolarGrid delivers inference as a service in three layers of capacity, all behind one endpoint.
Base capacity
The dedicated GPU capacity your production traffic runs on every day. Our engineers size it with you in your deployment plan, based on your model and expected traffic.
Expansion capacity
Covers growth. As your traffic increases, new capacity comes online in minutes, not the months it takes to stand up a reserved GPU cluster.
Burst capacity
Handles spikes on the same endpoint. When traffic surges in one region, nearby sites in our distributed compute network take the overflow, so requests don't queue and you don't have to add nodes.
See pricing for plan details.
Deploy your model in four steps
- 01
Tell us about your model and traffic
Share the open-source or fine-tuned model you want to run and the request volume you expect.
- 02
Get a deployment plan and quote
Our engineers size your base capacity and choose where in the network your model should run.
- 03
Deploy on dedicated hardware
We load your model onto reserved, dedicated GPUs and provision your endpoint.
- 04
Call your endpoint and scale
Send requests through the API while we scale and monitor the deployment, with burst capacity ready on the same endpoint when traffic spikes.
Starting from an open-source model? Browse the models we already support.
Why AI labs choose PolarGrid as their inference provider
AI labs choose an inference provider on two things: whether their endpoints stay up, and whether capacity can follow their traffic. PolarGrid is built for both.
Reliability built from redundancy
Your deployment is routed across independent supply in our distributed compute network, so one outage never takes your endpoint down. That redundancy is what backs our 99.99% uptime SLA.
Flexibility in how you run
Use managed endpoints for open-source or fine-tuned models, dedicated deployments for steady traffic, and burst clusters for traffic peaks or training and fine-tuning jobs.
Capacity others can't reach
We secure long-term compute and energy at scale, so you aren't limited to whatever GPUs are left to rent.
Inference that improves over time
Telemetry from every node tunes your deployment, and you keep full visibility into how it runs.
Need GPU capacity without the managed layer? See GPU as a service.
AI inference platform FAQs
What is a managed AI inference platform?
A managed AI inference platform runs the infrastructure that serves your model in production: the GPUs, the serving stack, scaling and monitoring. On PolarGrid, your team sends requests to an endpoint and we operate everything behind it, while you keep control of the model, its weights and its configuration.
How is PolarGrid different from a serverless inference API?
Serverless inference APIs run your requests on shared GPUs, so performance can vary with other customers' traffic and models may cold-start. PolarGrid runs your model on dedicated NVIDIA GPUs reserved for your workload, sized to your traffic, with burst capacity on the same endpoint when demand spikes.
Can I run my own fine-tuned model on PolarGrid?
Yes. You can deploy an open-source model or your own fine-tuned weights. Tell us what you want to run and the traffic you expect, and we'll include it in your deployment plan.
Is the PolarGrid inference endpoint OpenAI-compatible?
Yes. Models are served behind an OpenAI-compatible API, so most teams switch by changing the base URL and model name in the client they already use.
How does PolarGrid keep inference private?
Your model runs on dedicated GPUs reserved for your workloads rather than hardware shared with other tenants. You keep control of your model weights, its configuration and the data that passes through it.
Ready to put your model into production?
Tell us what you're deploying and the traffic you expect. We'll size your capacity and send a plan.