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Replicate's changelog

Agent skills for Replicate – Replicate changelog Fallback model for Nano Banana Pro – Replicate changelog MCP server auto-discovery – Replicate changelog Filter predictions by source – Replicate changelog The little things, week ending December 19, 2025 – Replicate changelog The little things, week ending December 5, 2025 – Replicate changelog The little things, week ending November 21, 2025 – Replicate changelog Code mode for Replicate's MCP server – Replicate changelog The little things, week ending November 7, 2025 – Replicate changelog Deployment setup monitoring – Replicate changelog The little things, week ending October 24, 2025 – Replicate changelog Set deadlines for predictions – Replicate changelog The little things, week ending October 10, 2025 – Replicate changelog Download invoices from billing settings – Replicate changelog Sort models by creation date via API – Replicate changelog Update model metadata via API – Replicate changelog The little things, week ending September 26, 2025 – Replicate changelog New search API, now in beta – Replicate changelog The little things, week ending September 12, 2025 – Replicate changelog Torch compile caching – Replicate changelog The little things, week ending August 29, 2025 – Replicate changelog The little things, week ending August 14, 2025 – Replicate changelog Run all models with the same API endpoint – Replicate changelog The little things, week ending August 1, 2025 – Replicate changelog Purchase prepaid credit – Replicate changelog Introducing a new Cog runtime – Replicate changelog The little things, week ending July 18, 2025 – Replicate changelog The little things, week ending July 4, 2025 – Replicate changelog Set a monthly spend limit deprecated – Replicate changelog See up to 24 hours of data on deployment metric graphs – Replicate changelog
Deployments – Replicate changelog
2023-10-04 · via Replicate's changelog

You can now create a deployment to get more control over how your models run. Deployments allow you to run a model with a private, fixed API endpoint. You can configure the version of the model, the hardware it runs on, and how it scales.

Using deployments, you can:

  • Roll out new versions of your model without having to edit your code.
  • Keep instances always on to avoid cold boots.
  • Customize what hardware your models run on.
  • Monitor whether instances are booting up, running, or processing predictions.
  • View predictions that are flowing through your models.

Deployments work with both public models and your own private models.

🚀 Check out the deployments guide to learn more and get started.