AI developers and engineers
Bring your own weights or pick an open model, deploy with a single command, and call it from an OpenAI-compatible API.
- Bring your own weights
- OpenAI-compatible API
- Usage-based pricing
About PolarGrid
The inference cloud,built for production.
PolarGrid runs managed inference on a network of GPU sites close to your users. We own the full path from hardware to API, so teams can ship models to production without stitching together clouds, schedulers, and serving stacks.
The platform
One stack,from silicon to API.
- 01
Inference layer
OpenAI-compatible endpoints, autoscaling replicas, and request routing to the nearest healthy site.
- Endpoints
- Autoscaling
- Routing
- 02
Orchestration layer
Scheduling, rollouts, and failover across sites, so a deployment is described once and runs everywhere.
- Scheduling
- Rollouts
- Failover
- 03
Compute layer
Dedicated GPU capacity, tuned runtimes, and storage paths sized for loading large checkpoints fast.
- GPUs
- Runtimes
- Storage
- 04
Hardware layer
Partner data centers with power, cooling, and networking we specify, accept, and operate with the site teams.
- Sites
- Networking
- Power
Why full stack
Production inferenceneeds the whole path.
Latency you can plan around
Requests are served from the site nearest your users, and owning every layer means fewer hops between them and the GPU.
Reliability by design
Failover, health checks, and capacity planning are built into the platform rather than bolted on after an outage.
One team to call
When something needs tuning, the people who run the hardware and the people who run the API are the same team.
Who we serve
Built for teamsrunning AI in production.
Enterprise AI teams
Dedicated capacity, regional placement, and a deployment plan built with our engineers for workloads that can't go down.
- Dedicated capacity
- Regional placement
- Hands-on deployment support
In the news
What people aresaying about us.
- BetaKitOpinion
Latency may be invisible to users, but it will define who wins in AI
Rade Kovacevic on why AI needs its own delivery network, and why edge inference will decide who wins.
Readon BetaKit (opens in a new tab) - Investing NewsFeature
AI infrastructure moving to the edge to transform user experience
How PolarGrid's prototype edge network cuts AI inference lag by more than 70 percent versus centralized hyperscalers.
Readon Investing News (opens in a new tab) - BetaKitPodcast
The Canadian company solving AI’s latency problem
Rade Kovacevic on The BetaKit Podcast: why AI feels slow, and what real-time voice and video need.
Readon BetaKit (opens in a new tab)
Build on PolarGrid.
Tell us about your model and traffic, and we'll come back with a deployment plan.