One endpoint, global inference delivery.
Managed inference on dedicated NVIDIA B300s. Custom models on bare-metal GPUs available today.
Built for inference, not retrofitted from the cloud.
Time to capacity
- PolarGrid
- Minutes
- Reserved GPU cluster
- Months
- Serverless inference API
- Minutes, on shared GPUs
Hardware
- PolarGrid
- Dedicated NVIDIA B300s, single tenant
- Reserved GPU cluster
- Dedicated
- Serverless inference API
- Shared
Your model and serving image
- PolarGrid
- Any checkpoint, on a built-in engine or your own image
- Reserved GPU cluster
- Yes, you run it
- Serverless inference API
- Supported models and runtimes only
Endpoint
- PolarGrid
- One endpoint: OpenAI-compatible or your image's own API
- Reserved GPU cluster
- You build it
- Serverless inference API
- The provider's API
Traffic spikes
- PolarGrid
- Burst capacity on the same endpoint
- Reserved GPU cluster
- Buy more nodes
- Serverless inference API
- Scales on shared capacity
Served from
- PolarGrid
- The best of many sites
- Reserved GPU cluster
- One region
- Serverless inference API
- A few regions
Site failure
- PolarGrid
- Traffic moves to the next nearest site
- Reserved GPU cluster
- Your failover plan
- Serverless inference API
- Handled by the provider
Releases
- PolarGrid
- Evals, canary and automatic rollback
- Reserved GPU cluster
- You build it
- Serverless inference API
- Varies
Monitoring
- PolarGrid
- Metrics by site, public status page
- Reserved GPU cluster
- You build it
- Serverless inference API
- Provider dashboards
| Capability | Reserved GPU cluster | Serverless inference API | |
|---|---|---|---|
| Time to capacity | Minutes | Months | Minutes, on shared GPUs |
| Hardware | Dedicated NVIDIA B300s, single tenant | Dedicated | Shared |
| Your model and serving image | Any checkpoint, on a built-in engine or your own image | Yes, you run it | Supported models and runtimes only |
| Endpoint | One endpoint: OpenAI-compatible or your image's own API | You build it | The provider's API |
| Traffic spikes | Burst capacity on the same endpoint | Buy more nodes | Scales on shared capacity |
| Served from | The best of many sites | One region | A few regions |
| Site failure | Traffic moves to the next nearest site | Your failover plan | Handled by the provider |
| Releases | Evals, canary and automatic rollback | You build it | Varies |
| Monitoring | Metrics by site, public status page | You build it | Provider dashboards |
From checkpoint to production, without the detour.
Served from the site nearest every user.
Fast everywhere
Each request is served from the site nearest the user, so time to first token stays low in every region, not only near one data center.
Elastic capacity
When traffic surges in one region, nearby sites take the overflow. A single site would queue it.
Resilient by design
If a site goes down, its traffic moves to the next nearest site and the endpoint stays up.
Sovereign data
Models can be pinned to specific regions for data residency.
Frequently asked questions.
Further reading on voice and latency.
- Engineering · 8 min read
Why Voice Agents Need a Different Inference Stack
Chances are, your intelligent voice AI agent gives relevant responses. But every exchange has this half-second pause that makes the whole thing feel broken. That pause killed it. Here's why centralized inference fails voice — and what the right stack looks like.
Read the post - Engineering · 8 min read
Anatomy of a Sub-400ms STT→LLM→TTS Pipeline
At the one-second mark, users begin to disengage. Human speech has a rhythm — and voice AI that can't match it is broken, regardless of how many parameters its model has. Here's exactly how we built our 364ms pipeline.
Read the post - Infrastructure · 6 min read
Latency Will Decide Who Wins in Real-Time AI
For the last decade, the internet trained us to expect immediacy. Now AI is the next major shift — and the infrastructure patterns that made the web feel instant weren't designed for what AI demands.
Read the post
Get a deployment plan and quote
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