> ## Documentation Index
> Fetch the complete documentation index at: https://runinfra.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy to your own RunPod

> Bring your own cloud: deploy an optimized LLM or embedding model to your own RunPod account. You control and pay for the GPU; RunInfra serves the optimized endpoint for free on Core and above.

Deploy an optimized pipeline to **your own RunPod account** instead of RunInfra's managed cloud. You keep full control of the GPU, the spend, and the account; RunInfra provisions the endpoint, serves inference through the same `api.runinfra.ai` gateway, and meters usage at **zero cost** on Core and above.

<Columns cols={2}>
  <Card title="Managed RunInfra Cloud" icon="cloud">
    Full custom-image optimization, billed by RunInfra per token. The default, and the only option for image, speech, and voice models.
  </Card>

  <Card title="Your RunPod" icon="key">
    Runs on your RunPod account with env-level optimization. You pay RunPod for the GPU; RunInfra inference is free on Core and above.
  </Card>
</Columns>

## When to pick this

* **You already have RunPod credits, quota, or a negotiated rate** and want the GPU bill on your own account.
* **Cost control**: you set the scaling, the idle behavior, and the spend ceiling in your RunPod console.
* **Account ownership**: the serverless endpoint lives in your RunPod console where you can see it, audit it, and remove it at any time.

## What it supports, honestly

|                           | Managed                                                         | Your RunPod                                                   |
| ------------------------- | --------------------------------------------------------------- | ------------------------------------------------------------- |
| Modalities                | LLM, embedding, image, speech, voice                            | **LLM and embedding only**                                    |
| Optimization              | Full custom image (kernels, quantized artifacts, tuned runtime) | **Lighter env-level optimization** on a stock RunPod template |
| GPU billing               | RunInfra per-token                                              | **Your RunPod account**, at RunPod's rates                    |
| RunInfra inference charge | Per-token                                                       | **Free** (Core and above)                                     |
| Tier                      | Flex or Active                                                  | Flex only (scaling is yours to manage on RunPod)              |
| Plan requirement          | Core                                                            | Core                                                          |

The optimization difference matters: managed deploys run RunInfra's custom-built image carrying everything the optimizer produced. Your-RunPod deploys run a stock RunPod template with the optimizer's settings applied as environment configuration. That captures the serving-level wins (engine flags, KV cache, context sizing) but not custom kernels or RunInfra-quantized local artifacts. Pipelines that need those are refused with a clear error and stay deployable on managed.

## How it works

<Steps>
  <Step title="Connect your RunPod account">
    In the deploy tab, pick **Your RunPod** as the Destination and paste a RunPod API key (create one at RunPod console, Settings, API Keys). The key is validated live against your account, then encrypted with AES-256-GCM. It is never shown again, never logged, and only decrypted server-side at provision time.
  </Step>

  <Step title="Deploy">
    Click Deploy as usual. RunInfra provisions a serverless endpoint on your RunPod account with the optimized serving configuration. The endpoint appears in your own RunPod console.

    RunInfra sizes the readiness window to the model and includes time for a new account or host to pull the runtime and uncached weights. Larger models receive longer startup budgets, which reduces false deployment timeouts during a healthy cold start.
  </Step>

  <Step title="Use the same gateway">
    Your API keys, SDK snippets, and `api.runinfra.ai` URLs work unchanged. Inference is proxied to your endpoint using your stored key; usage (tokens, latency) is recorded for analytics at zero cost.
  </Step>

  <Step title="Tear down and disconnect">
    Terminate the deployment from RunInfra (it is deleted on your RunPod account), then disconnect the key from the deploy tab or Settings. Disconnecting is blocked while live deployments still need the key for teardown, so nothing gets stranded.
  </Step>
</Steps>

## Key handling and security

* The key you paste is validated against RunPod, encrypted at rest, and **never returned to the browser**.
* RunInfra's frontend cannot decrypt it. Only the deployment engine decrypts it, at provision, status, teardown, and inference time, after verifying the credential belongs to your workspace.
* The inference proxy will only sign **inference requests** to your endpoint with your key; it refuses every other RunPod operation.
* Re-linking a key for the **same** RunPod account just rotates it. Switching to a **different** RunPod account is blocked while deployments are live on the current one.

## Billing details

* **RunPod bills you** for GPU time at your account's rates. RunInfra never adds a margin on top of it.
* **RunInfra bills nothing per inference** on Your-RunPod deploys for Core and above. Optimization runs are billed normally.
* If your workspace drops below Core, Your-RunPod inference is suspended with a `byoc_plan_required` error until you upgrade or redeploy on managed.

## Troubleshooting

| Error                                       | Meaning                             | Fix                                           |
| ------------------------------------------- | ----------------------------------- | --------------------------------------------- |
| `byoc_runpod_not_linked`                    | No RunPod key on file               | Connect your RunPod account in the deploy tab |
| `byoc_modality_unsupported`                 | Pipeline needs a custom image       | Deploy on managed RunInfra Cloud              |
| `byoc_plan_required`                        | Workspace is below Core             | Upgrade, or redeploy on managed               |
| "Reconnect RunPod in Settings"              | Stored key was unlinked or rejected | Re-link the key, then redeploy                |
| "Check the endpoint in your RunPod console" | Your endpoint is unreachable        | Inspect or restart it from the RunPod console |

Related: [Deployment targets](/docs/deployments/targets), [GPU pricing](/docs/features/gpu-pricing), [Plans](/docs/introduction/plans).