We ve added clear volume usage metrics to give teams better visibility into storage consumption over time. You can track: Date and time Total capacity Available storage Actual usage This helps teams monitor growth, plan capacity, and avoid surprises as applications scale. More observability improvements coming soon.
Bult.ai lets you deploy production-ready databases with automated backups and monitoring directly from the Canvas UI using built-in templates or custom images.
Supported out of the box: PostgreSQL MySQL Redis for caching and queues MongoDB 500+ additional databases available from Docker Hub
Example: Deploy PostgreSQL Select the PostgreSQL template Choose compute size and volume Click Create and get a ready-to-use connection string
No manual server setup. No DevOps overhead. Databases ready in minutes.
We ve improved change tracking in Bult.ai with a Canvas view.
You can now see all uncommitted changes in one place, review what s about to be deployed, approve or discard changes, and roll back when needed. It makes fast iteration safer and deployments more predictable.
You can add any Docker Hardened Image from Docker Hub to Bult.ai and run it as a service in minutes. Import ML & AI frameworks and stacks like LangChain, TensorFlow, PyTorch, and 500+ others in just a few clicks. No manual infrastructure setup, no complex configuration.
You can connect several GitHub accounts at the same time and manage all your repositories and projects in one place. It s especially useful if you work across personal projects, client work, or multiple organizations.
No more logging in and out or switching contexts. Everything stays connected and ready to deploy.
We ve just published a new n8n tutorial for Bult.ai.
It shows how to deploy n8n on Bult.ai in minutes and build a personal AI assistant connected to Telegram, Gmail, Google Tasks, Google Contacts, and Calendar. The guide covers deployment, importing a ready-made workflow, credentials setup, and testing text and voice prompts.
We just shipped a new feature on Bult.ai that many developers have been asking for.
We added a built-in Terminal, which allows you to access and debug running services directly inside the platform. This makes troubleshooting much faster and removes the need for external tools or custom setup.
If you are deploying microservices, experimenting with templates, or debugging environments, this should make your workflow much smoother.
More improvements are on the way. Happy to answer questions and would love your feedback.
