8 posts tagged with "LLMs"

Deploying Agentic Apps to the Cloud Shouldn’t Be This Hard…​

Agentic apps are redefining how software is built: multi-agent workflows, persistent memory, tool-using LLMs, and orchestrated autonomy. But deploying them to the cloud is still painful - for example, your agentic app typically needs to provision:

• Managed databases like Postgres or MongoDB
• Fast, scalable caching (hello Redis)
• Containerized compute that scales
• Secure networking and service discovery
• Managed LLMs like AWS Bedrock or GCP Vertex AI

And for many teams, these apps must run inside the customer’s cloud, where sensitive data lives and compliance rules apply. That means you cannot just spin up your own environment and call it a day. Instead, you are deploying across AWS, GCP, DigitalOcean, or whichever stack your customers demand, each with its own APIs, quirks, and limitations.

Now you are not just building agents; you are picking the right infrastructure, rewriting IaC templates for every provider, and untangling the edge cases of each cloud.

The result: weeks of DevOps headaches, lost momentum, and engineers stuck wiring infrastructure instead of shipping agents.

We Made it Simple with Cloud Native Support for Agentic Apps​

That’s where Defang comes in. We make it easy to deploy full-stack agentic apps to your cloud of choice: native, secure, and scalable. Defang understands the common ingredients of agentic apps and makes them first-class citizens:

• Compute: Your Dockerized services deploy as cloud-native workloads (e.g. AWS ECS, or GCP Cloud Run)
• Databases: Provision managed Postgres or MongoDB with one config line
• Caching: Add Redis and Defang spins up a managed Redis instance in your cloud
• LLMs: Integrate directly with Bedrock or Vertex AI - even provision an OpenAI gateway for compatibility with OpenAI APIs.
• Secure Defaults: : TLS, secrets, IAM, and service accounts handled out of the box

Built for All your Favorite Agentic Frameworks​

Defang works seamlessly with leading agentic frameworks. Try them out with our ready-to-deploy samples:

• Autogen - demo featuring Mistral AI + FastAPI, deployable with Defang’s OpenAI Access Gateway.
• CrewAI - sample app showing multi-agent orchestration in action.
• LangGraph - workflow sample that defines and controls multi-step agentic graphs with LangChain.

More framework templates coming soon.

Why It Matters​

Agentic apps need to be fast, secure, and ready to scale. Defang delivers cloud-native deployments in your environment (AWS, GCP, DO), so you can move from idea to production quickly with consistent behavior across dev, test, and prod.

The Developer Journey, Simplified​

1. Build your agentic app locally using Docker Compose
2. Test in Defang's free playground with defang compose up
3. Deploy to your cloud:

defang compose up --provider=aws  # or gcp, do

It just works. No Terraform. No YAML explosion. No vendor lock-in.

The Future of AI Apps Is Agentic and Cloud-Native​

Agility and scalability should not be a trade-off. With Defang, you get both. Developers focus on agents, tools, and outcomes. Defang takes care of the cloud infrastructure.

Try it out​

Explore more samples at docs.defang.io Join our community on Discord

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.

Railpack Integration​

We’ve integrated Railpack into Defang to make deployments even smoother. Railpack automatically builds OCI-compliant images from your source code with minimal configuration. This helps eliminate one of the most common issues our users face: missing or invalid Dockerfiles, especially when they’re generated by LLMs or created by users with limited Docker experience. Now, if no Dockerfile is provided, Defang will seamlessly use Railpack to build a working image for you, so you can focus on your code, not your container setup.

GCP Cost Estimation​

In June, Defang announced real-time cost estimation for AWS. In July, we took our live cloud cost estimation to the next level by extending support to GCP. Defang now makes it easy to compare real-time pricing for both cloud providers. All you need is your project's compose.yaml file. Whether you’re optimizing for cost, performance, or flexibility, Defang makes it easy to get the information you need to deploy with confidence.

Managed MongoDB on GCP​

Defang now supports managed MongoDB on GCP through MongoDB-compatible APIs provided by Google Cloud. This integration allows you to spin up a fully managed Firestore datastore and interact with it just like a standard MongoDB instance without any manual setup or configuration.

Agentic LangGraph Sample​

We have published a new Agentic LangGraph sample project that demonstrates LangGraph agent deployment with Defang. As AI agent development grows, Defang makes it simple to deploy and scale agents, including those built with LangChain or LangGraph. You can explore the example to see how it works in practice.

Events and Community​

In July, we hosted the Bière & Code & Beer MTL during Startupfest in Montreal. It was an incredible evening with great energy, tech conversations, and the chance to connect with so many talented builders over drinks.

We are excited to see what you will deploy with Defang next. Join our Discord to ask questions, get support, and share your builds with the community.

More coming in August.

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.

Enter Defang: Your AI DevOps Agent​

Defang is an AI-enabled agent that takes care of your entire deployment workflow, going from app code to a production-ready deployment on your favorite cloud in a single step.

By understanding your app stack (using Docker Compose), Defang provisions the right infrastructure and securely deploys to AWS, GCP, or DigitalOcean, following each cloud's best practices.

Whether you're launching a side project or scaling a multi-agent app, Defang ensures secure, smooth, scalable cloud-native deployments.

Defang Deployment Features at a Glance​

• One Command Deployment: Run defang compose up and you're live
• Secure and Scalable: Built-in TLS, secrets, autoscaling, IAM, and HTTPS
• Multi-Cloud Ready: Deploy to your cloud (AWS, GCP, DO) using your own credentials
• Language & framework agnostic: Next.js, Go, Python (Django/Flask), C#, …
• Managed LLM: Add x-defang-llm: true and Defang auto-configures cloud-native LLMs like Bedrock, Vertex AI, and the Defang Playground
• Configures managed services (e.g. managed Postgres, MongoDB, Redis) using the target cloud's native services (e.g. RDS for Postgres on AWS, Cloud SQL on GCP).
• Tailored deployment modes (e.g. affordable, balance, high-availability) optimized for different environments (dev, staging, production)
• AI Debugging: Get context-aware assistance to quickly identify and fix deployment issues

Native Integration with AI-Assisted Coding Tools​

Defang can be accessed directly from within your favorite IDE - Cursor, Windsurf, VS Code, Claude, or Kiro - via Defang's MCP Server. You can now deploy to the cloud with a natural language command like "deploy my app with Defang".

For Developers and CTOs Who Want to Move Fast​

If you're a developer shipping fast or a CTO scaling lean, Defang acts as your drop-in DevOps engineer without needing to build a team around it.

You focus on building great software.
Defang gets it live.

Try Defang Now​

• Deploy your full-stack app with a single command
• Explore samples and templates
• Join our Discord community
• Check out our YouTube channel for tutorials

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.

🚀 Live AWS Cost Estimation​

We just launched something we’re really excited about: live AWS cost estimation before you deploy. Most devs ship to the cloud without knowing what it’s going to cost and that’s exactly the problem we’re solving. With Defang, you can now estimate the cost of deployment of an Docker Compose application and choose the deployment mode - affordable / balanced / high_availability - that best suits your needs.

👉 Check out the docs

🧠 CrewAI + Defang Starter Kit​

In June, we launched a full-stack starter kit for building real-time RAG and multi-agent apps with CrewAI + Defang. It’s designed to help you move fast with a production-style setup — including Django, Celery, Channels, Postgres (with pgvector), Redis for live updates, and Dockerized model runners you can easily customize. CrewAI handles the agent workflows, and with Defang, you can deploy the whole thing to the cloud in a single command. Whether you’re building a smart Q&A tool or a multi-agent research assistant, this stack gives you everything you need to get started.

👉 Try it out here

📊 Deployment Info in Portal​

We’ve added active deployment information to the Defang Portal. You can now see your currently active deployments across various cloud providers and understand the details of each, while still managing your cloud environments through the provider’s own tools (e.g. the AWS Console).

☁️ Playground Now Runs on AWS + GCP​

Internally, we also hit a big milestone: The Defang Playground now runs on both AWS and GCP, showing the power of Defang’s multi-cloud infrastructure. We’ve also enabled load balancing between the two platforms and plan to share a detailed blog post on how it works soon.

🧩 VS Code Extension Released​

We also released the Defang VS Code Extension, making it even easier to deploy and manage cloud apps right from your editor. No terminal needed.

• One-click deploy
• Built-in tools to manage services
• Zero config, fast setup

👉 Try it out here

💬 Ask Defang via Intercom​

You can now try out the Ask Defang chatbot directly within Intercom! This new integration makes it easier than ever to get instant answers and support while you work. Ask Defang itself is deployed using Defang to our own cloud infrastructure.

🐳 Docker x Defang White Paper​

And one more thing: bridging local development and cloud deployment just got easier. We’ve published white papers on how Defang extends Docker Compose and GCP workflows to the cloud — using familiar tools at scale. An AWS white paper is coming soon.

👉 Read the white paper here

👉 Read the GCP white paper

Events and Community​

In June, we showcased a powerful new demo at AWS events: “What If You Could See AWS Costs Before You Deployed?” Jordan Stephens walked through how to go from Docker Compose to AWS infra with real-time cost estimates and easy teardown, all via Defang.

👉 Watch the demo here

We can’t wait to see what you deploy with Defang.
👉 Join our Discord

More coming in July.

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.

🚀 Managed LLMs in Playground​

You can now try managed LLMs directly in the Defang Playground. Defang makes it easy to use cloud-native language models across providers — and now you can test them instantly in the Playground.

• Managed LLM support
• Playground-ready
• Available in CLI v1.1.22 or higher

To use managed language models in your own Defang services, just add x-defang-llm: true — Defang will configure the appropriate roles and permissions for you.

Already built on the OpenAI API? No need to rewrite anything.

With Defang's OpenAI Access Gateway, you can run your existing apps on Claude, DeepSeek, Mistral, and more — using the same OpenAI format.

Learn more here.

Try it out here.

📦 MongoDB Preview on AWS​

Last month, we added support for MongoDB-compatible workloads on AWS via Amazon DocumentDB.

Just add this to your compose.yaml:

services:
  db:
    x-defang-mongodb: true

Once you add x-defang-mongodb: true, Defang will auto-spin a DocumentDB cluster in your AWS — no setup needed.

🛠 MCP Server Improvements​

We've made the MCP Server and CLI easier to use and deploy:

• Users are now prompted to agree to Terms of Service via the portal login
• MCP Server and CLI are now containerized, enabling faster setup, smoother deployments, and better portability across environments

🌎 Events and Community​

We kicked off the month by sponsoring Vancouver's first Vibe Coding IRL Sprint. Jordan Stephens from Defang ran a hands-on workshop on "Ship AI Faster with Vertex AI" with GDG Vancouver (GDG Vancouver). Around the same time, our CTO and Co-founder Lio joined the GenAI Founders Fireside panel hosted by AInBC and AWS.

Big moment for the team — we won the Best Canadian Cloud Award at the Vancouver Cloud Summit. Right after, we hit the expo floor at Web Summit Vancouver as part of the BETA startup program and got featured by FoundersBeta as one of the Top 16 Startups to Watch.

Our Campus Advocates also kept the momentum going, hosting Defang events around the world with live demos and workshops.

Last month's Defang Coffee Chat brought together the community for product updates, live demos, and a great convo on vibe deploying.

We're back again on June 25 at 10 AM PST. Save your spot here.

We can't wait to see what you deploy with Defang. Join our Discord to ask questions, get support, and share your builds.

More coming in June.

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.

MCP + Vibe Deploying​

This month we focused on making cloud deployments as easy as writing a prompt. Our latest Vibe Deploying blog shows how you can launch full-stack apps right from your IDE just by chatting.

Whether you're working in Cursor, Windsurf, VS Code, or Claude, Defang's MCP integration lets you deploy to the cloud just as easily as conversing with the AI to generate your app. For more details, check out the docs for the Defang Model Context Protocol Server – it explains how it works, how to use it, and why it's a game changer for deploying to the cloud. You can also watch our tutorials for Cursor, Windsurf, and VS Code.

Managed LLMs​

Last month we shipped the x-defang-llm compose service extension to easily deploy inferencing apps that use managed LLM services such as AWS Bedrock. This month, we're excited to announce the same support for GCP Vertex AI – give it a try and let us know your feedback!

Events and Programs​

On April 28, we kicked things off with an epic night of demos, dev energy, and cloud magic at RAG & AI in Action. Our own Kevin Vo showed how fast and easy it is to deploy AI apps from Windsurf to the cloud using just the Defang MCP. The crowd got a front-row look at how Vibe Deploying turns cloud infra into a background detail.

We finished the month with our signature Defang Coffee Chat, a casual hangout with product updates, live Q&A, and great conversations with our community. Our Campus Advocates also hosted workshops around the world, bringing Defang to new students and builders.

We wrapped up the month with our latest Defang Coffee Chat, featuring live demos, product updates, and a solid conversation around vibe deploying. Thanks to everyone who joined.

The next one is on May 21 at 10 AM PST. Save your spot here.

Looking Ahead​

Here's what's coming in May:

• Web Summit Vancouver – Defang will be a startup sponsor, please come see us on the expo floor.
• More MCP tutorials and dev tools.

Let's keep building. 🚀

"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.

The Tools of Vibe Coding

Vibe coding would not exist without a new generation of AI-first tools. Here are some of the platforms powering this new workflow.

• Cursor
• Windsurf
• VS Code / VS Code Insiders
• Claude Desktop

While each has it's own strengths and weaknesses, they all support the basic vibe coding workflow described above.

Using Defang for "Vibe Deployment"

Once your app runs locally with these vibe coding tools, the next question is: how do you get it live in the cloud so you can share it with the world?

That is where Defang comes in.

Defang takes your app, as specified in your docker-compose.yml, and deploys it to the public cloud (AWS, GCP, or DigitalOcean) or the Defang Playground with a single command. It is already used by thousands of developers around the world to deploy their projects to the cloud.

And now with the Defang MCP Server, you can "vibe deploy" your project right from your favorite IDE! Once you have the Defang MCP Server installed (see instructions here), just type in "deploy" (or any variation thereof) in the chat, it's that simple! It is built for hobbyists, vibe coders, fast-moving teams, and AI-powered workflows.

Currently, we support deployment to the Defang Playground only, but we'll be adding deployment to public cloud soon.

How it works:

The Defang MCP Server connects your coding editor (like VS Code or Cursor) with Defang's cloud tools, so you can ask your AI assistant to deploy your project just by typing a prompt. While natural language commands are by nature imprecise, the AI in your IDE translates that natural language prompt to a precise Defang command needed to deploy your application to the cloud. And your application also has a formal definition - the compose.yaml file - either something you wrote or the AI generated for you. So, the combination of a formal compose.yaml with a precise Defang command means that the resulting deployment is 100% deterministic and reliable. So the Defang MCP Server gives you the best of both worlds - the ease of use and convenience of natural language interaction with the AI, combined with the predictability and reliability of a deterministic deployment.

We are so excited to make Defang even more easy to use and accessible now to vibe coders. Give it a try and let us know what you think on our Discord!

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:

Managed LLMs: One of the coolest features we have released in a bit is support for Managed LLMs (such as AWS Bedrock) through the x-defang-llm compose service extension. When coupled with the defang/openai-access-gateway service image, Defang offers the easiest way to migrate your OpenAI-compatible application to cloud-native managed LLMs without making any changes to your code. Support for GCP and DigitalOcean coming soon.

Defang Pulumi Provider: Last month, we announced a preview of the Defang Pulumi Provider, and this month we are excited to announce that V1 is now available in the Pulumi Registry. As much as we love Docker, we realize there are many real-world apps that have components that (currently) cannot be described completely in a Compose file. With the Defang Pulumi Provider, you can now leverage the declarative simplicity of Defang with the imperative power of Pulumi.

Production-readiness: As we onboard more customers, we are fixing many fit-n-finish items:

1. Autoscaling: Production apps need the ability to easily scale up and down with load, and so we've added support for autoscaling. By adding the x-defang-autoscaling: true extension to your service definition in Compose.yaml file, you can benefit from automatic scale out to handle large loads and scale in when load is low. Learn more here.

2. New CLI: We've been busy making the CLI more powerful, secure, and intelligent. • Smarter Config Handling: The new --random flag simplifies setup by generating secure, random config values, removing the need for manual secret creation. Separately, automatic detection of sensitive data in Compose files helps prevent accidental leaks by warning you before they are deployed. Together, these features improve security and streamline your workflow. • Time-Bound Log Tailing: Need to investigate a specific window? Use tail --until to view logs up to a chosen time—no more scrolling endlessly. Save time from sifting through irrelevant events and focus your investigation. • Automatic generation of a .dockerignore file for projects that don't already have one, saving you time and reducing image bloat. By excluding common unnecessary files—like .git, node_modules, or local configs—it helps keep your builds clean, fast, and secure right from the start, without needing manual setup.

3. Networking / Reduce costs: We have implemented private networks, as mentioned in the official Compose specification. We have also reduced costs by eliminating the need for a pricy NAT Gateway in "development mode" deployments!

Events and Programs​

In March, we had an incredible evening at the AWS Gen AI Loft in San Francisco! Our CTO and Co-founder Lionello Lunesu demoed how Defang makes deploying secure, scalable, production-ready containerized applications on AWS effortless. Check out the demo here!

We also kicked off the Defang Campus Advocate Program, bringing together advocates from around the world. After launching the program in February, it was amazing to see the energy and momentum already building on campuses world-wide. Just as one example, check out this post from one of the students who attended a session hosted by our Campus Advocate Swapnendu Banerjee and then went on to deploy his project with Defang. This is what we live for!

We wrapped up the month with our monthly Coffee Chat, featuring the latest Defang updates, live demos, and a conversation on vibe coding. Thanks to everyone who joined. The next one is on April 30. Save your spot here.

As always, we appreciate your feedback and are committed to making Defang even better. Deploy any app to any cloud with a single command. Go build something awesome!