sub-second cold starts
popular models run on warm gpus. your request hits a pre-deployed instance - no boot, no pull, no wait.
serverless gpu
serverless gpu for ai. scale to zero. pay per second.
run any ai model on demand with no infra to manage. sub-second cold starts on warm models, auto-scale to thousands of concurrent jobs, and pay only for the time you use. plus a runtime built for agents, not just functions.
terminal
$ belt app run flux/schnell-fp8
✓ warm start · infer 1.8s · $0.012
$ belt app run veo/3.1-fast
✓ h100 · 12 concurrent · $0.42/min
$ belt deploy ./my-agent
✓ deployed → https://infsh.dev/u/my-agent
from code to cloud
everything you need to ship.
per-second billing
metered to the millisecond. scale to zero when idle - when nothing runs, the bill is zero.
hundreds of pre-deployed models
flux, veo, seedance, claude, elevenlabs, qwen, seedream, wan, and more. one api call, no setup.
observability built in
traces, logs, cost per request. every signal captured automatically. no instrumentation sdk.
deploy in seconds
push a model or agent live with belt deploy. zero downtime, zero reconfiguration. github-native.
byok - bring your own keys
route through your own cloud accounts. use our orchestration with your gpus, your commitments, your data.
gpu tiers
bring your own model. or use ours.
| tier | gpus | best for |
|---|---|---|
| small | a4000 · rtx 4000 · rtx 2000 | image gen, sub-13b llms |
| medium | a5000 · l4 · l40s | mid-size image and video, 13b–34b llms |
| large | a100 40g · a100 80g | flagship video models, large llm inference, batch jobs |
| flagship | h100 · h200 · b200 | veo 3, seedance 2, frontier llms |
per-second billing across all tiers. scale to zero. see pricing for details.
what teams build
from a chat app to a film studio.
inference at scale
serve image, video, audio, and text generation for any model, any concurrency.
agent runtime
multi-step agents that survive 24-hour workflows. checkpointing, retries, human-in-the-loop.
creative pipelines
compose script, tts, voice clone, video, dub, render into one gpu-backed flow.
private deploys
deploy a fine-tune, research model, or closed-source weights into your own serverless endpoint.
how we compare
serverless gpu, plus everything around it.
| inference shell | RunPod | Modal | Replicate | fal.ai | |
|---|---|---|---|---|---|
| per-second billing | yes | yes | yes | yes | yes |
| sub-second cold starts | yes | yes | yes | - | yes |
| durable agent runtime | yes | - | - | - | - |
| observability (no sdk) | yes | - | - | - | - |
| hundreds of pre-deployed apps | yes | - | - | yes | - |
| byok (bring your own keys) | yes | - | - | - | - |
| self-hostable runtime | yes | - | - | - | - |
| skill & knowledge system | yes | - | - | - | - |
not just serverless. durable.
most serverless gpu platforms give you stateless functions. your code runs, returns a result, and forgets everything. that works for single-shot inference. it breaks for agents.
inference shell adds a durable execution layer on top of serverless gpus. multi-step workflows survive crashes and restarts. workspaces persist independently of containers. skills and knowledge compound across sessions. your agent can generate an image, edit it based on feedback, and remember what worked - across days, not just within a single request.
serverless gpu is the foundation. the agent runtime, the skill system, and the knowledge layer are what make it compound.
three things. sub-second cold starts on warm models. a durable agent runtime (not just stateless functions) so multi-step ai workflows survive crashes and 24-hour jobs. and observability without an sdk - every request gets a trace automatically.
ready to ship?
start with the hosted platform. deploy your own when you're ready.
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