23 posts tagged with "BYOC"

August was about making migrations smoother and showing how you can already use Defang to deploy agentic apps at scale. We expanded our sample projects for popular multi-agent frameworks like CrewAI, LangGraph, Autogen, and Strands, validating them on Playground, AWS, and GCP so you can run multi-agent workloads in production without extra DevOps. Our new Heroku migration flow inspects dynos and add-ons, generates a clean Compose file, provisions managed equivalents like Postgres and Redis, and ships to your own cloud in one command. This cuts costs and removes lock-in. We also introduced MCP BYOC prompts so you can deploy to AWS and GCP straight from your IDE. Railpack on GCP now delivers faster, more reliable no-Dockerfile builds with clearer logs and closer parity with AWS.

July was all about making cloud deployments even smoother and smarter. We focused on removing friction from deployments and giving you better visibility into costs. Railpack now builds production-ready images automatically when no Dockerfile is present, and our real-time cost estimation feature now supports Google Cloud alongside AWS. We also added managed MongoDB on GCP, introduced an Agentic LangGraph sample, and connected with builders at Bière & Code & Beer MTL. Here’s what’s new.

From Vibe-Coding to Production… Without a DevOps Team​

Building apps has never been easier. Tools like Cursor, Windsurf, Lovable, V0, and Bolt have ushered in a new era of coding called vibe coding, rapid, AI-assisted app development where developers can go from idea to prototype in hours, bringing ideas to life faster than ever before.

And with the recently released AWS Kiro, we have now entered a new phase of AI-assisted development called "spec-driven development" where the AI breaks down the app development task even further. You can think of a "PM agent" that goes from prompt to a requirements document, and then an "Architect agent" that goes from the requirements document to a design document, which is then used by "Dev", "Test" and "Docs" agents to generate app code, tests, and documentation respectively. This approach is much more aligned with enterprise use cases and produces higher quality output.

The Hard Part Isn’t Building. It’s Shipping.​

However, cloud app deployment remains a major challenge! As Andrej Karpathy shared during his recent YC talk:

"I vibe-coded the app in four hours… and spent the rest of the week deploying it."

While AI-powered tools make building apps a breeze, deploying them to the cloud is still frustratingly complex. Kubernetes, Terraform, IAM policies, load balancers, DNS, CI/CD all add layers of difficulty. This complexity continues to be a significant bottleneck that AI tools have yet to fully address, making deployment a critical challenge for developers.

The bottleneck is no longer the code. It's the infrastructure.

June was a big month at Defang. We rolled out powerful features across our CLI, Playground, and Portal, expanded support for both AWS and GCP, and introduced new tools to help you ship faster and smarter. From real-time cloud cost estimation to internal infra upgrades and community highlights, here’s everything we accomplished.

May was a big month at Defang. We shipped support for managed LLMs in Playground, added MongoDB support on AWS, improved the Defang MCP Server, and dropped new AI samples to make deploying faster than ever.

April flew by with big momentum at Defang. From deeper investments in the Model Context Protocol (MCP), to deploying LLM-based inferencing apps, to live demos of Vibe Deploying, we're making it easier than ever to go from idea to cloud.

"I'm building a project, but it's not really coding. I just see stuff, say stuff, run stuff, and copy-paste stuff. And it mostly works."

– Andrej Karpathy

Welcome to the world of vibe coding, an AI-assisted, intuition-driven way of building software. You do not spend hours reading diffs, organizing files, or hunting through documentation. You describe what you want, let the AI take a pass, and keep iterating until it works.

Wow - another month has gone by, time flies when you're having fun!

Let us share some important updates regarding what we achieved at Defang in March:

Anthropic recently unveiled the Model Context Protocol (MCP), “a new standard for connecting AI assistants to the systems where data lives”. However, as Docker pointed out, “packaging and distributing MCP Servers is very challenging due to complex environment setups across multiple architectures and operating systems”. Docker helps to solve this problem by enabling developers to “encapsulate their development environment into containers, ensuring consistency across all team members’ machines and deployments.” The Docker work includes a list of reference MCP Servers packaged up as containers, which you can deploy locally and test your AI application.

However, to put such containerized AI applications into production, you need to be able to not only test locally, but also easily deploy the application to the cloud. This is what Defang enables. In this blog and the accompanying sample, we show how to build a sample AI application using one of the reference MCP Servers, run and test it locally using Docker, and when ready, to easily deploy it to the cloud of your choice (AWS, GCP, or DigitalOcean) using Defang.

Welcome to 2025! As we had shared in our early Dec update, we reached our V1 milestone with support for GCP and DigitalOcean in Preview and production support for AWS. We were very gratified to see the excitement around our launch, with Defang ending 2024 with twice the number of users as our original goal!

We are excited to build on that momentum going into 2025. And we are off to a great start in Jan, with some key advancements: