We've added OpenClaw to our template library. Now launching your AI agent in the cloud is even faster.

We've automated deployment for AI projects:

1. Connect a repository

Users can now get instant answers to questions about the platform and documentation at any time. The AI assistant is available 24/7 and helps developers onboard faster, resolve issues quickly, and understand how to use Bult.ai more effectively.

More improvements to our support experience are on the way.

We just released a production ready RAG project-template for Bult.ai.

If you want to deploy a serious Retrieval Augmented Generation system, not a toy demo, this is for you.

We just shipped Backups in Bult.ai
You can now protect your data with built-in backup management:
Enable or disable automatic backups
Choose flexible schedules from hourly to biweekly
Create backups manually when needed
Download, restore, or delete backups

Backups are created while the service is running.

Thank you to everyone who supported Bult.ai this year. Launching on Product Hunt, getting featured, and learning from the maker community was a huge experience for us.

Wishing all makers a year full of shipping, learning, and meaningful wins.
See you in the next launches

Our ready-to-use templates on Bult.ai, making it easy to deploy popular services in just one click.

Instead of spending time on manual configuration, networking, storage, and environment setup, you can now launch production-ready services in minutes directly from the Bult.ai dashboard.

I m curious how other founders and builders approach early traction when launching a new product.

Specifically:

• Do you start with a structured traction plan, or iterate week by week?

• How much do you rely on templates vs. fully custom strategies per product?

• At what point do you decide that an idea is not worth pushing further?

From my experience, the hardest part isn t execution itself, but deciding what not to do in the first weeks especially when time and focus are limited.

We support flexible compute sizing in Bult.ai so apps only use what they actually need.

XS to XL sizes cover everything from prototypes to databases, with per-minute billing and the ability to resize anytime from the Canvas UI.
Compute options:
XS: 0.1 vCPU, 256 MB RAM, $2.7 per month. Ideal for prototypes
S: 0.25 vCPU, 512 MB RAM, $5.4 per month. Suitable for small apps
M: 0.5 vCPU, 1 GB RAM, $12 per month. Designed for web applications
L: 1 vCPU, 2 GB RAM, $24 per month. A good fit for APIs
XL: 2 vCPU, 4 GB RAM, $48 per month. Optimized for databases

Custom sizes are also available for advanced workloads available on request.
Happy to answer questions about sizing or use cases.

Vibe coding is about moving fast from idea to production without getting stuck on infrastructure.

A simple stack that works well:

• Frontend and Backend: @Google Antigravity, @Cursor, @Claude Code

• Databases and RAG: @Pinecone

• Cloud and DevOps: Bult.ai

• LLMs: @OpenAI, @Google Gemini, @Hugging Face, @Ollama

Bult.ai handles deployment, infrastructure, SSL, domains, scaling, and CI CD, so you can focus on building the product, not managing servers.