> ## Documentation Index > Fetch the complete documentation index at: https://docs.inference.net/llms.txt > Use this file to discover all available pages before exploring further. # Manage and Monitor > Start, stop, and delete deployments. Monitor production performance and scale when you need to. Deployments list with per-deployment metrics ## Lifecycle operations * **Start** — bring a stopped deployment back online * **Stop** — take the deployment offline * **Delete** — remove the deployment entirely ## Scaling By default, your model deploys on a single dedicated GPU. If you need additional GPUs or auto-scaling, [reach out to our team](https://inference.net/meet-with-us/). ## Monitoring Once your deployment is live, click into it from the **Deployments** page to see metrics and individual inference calls. You get the same [Gateway](/platform/gateway/overview) experience — latency, error rates, token usage, and full request/response payloads — scoped to that deployment. Deployment metrics and inference calls ## The loop continues Your custom model is live. Use [Gateway](/platform/gateway/overview) to watch its production performance, run [evals](/platform/eval/overview) to catch regressions, and [train](/platform/train/overview) the next version when you're ready. Monitor production traffic. Catch quality regressions. Build the next version.