Claude Code is impressive, perhaps even revolutionary. You can go from idea to working AI agent faster than ever: writing tools, wiring up workflows, testing logic in real time. For developers and technical teams building automations, it's become the fastest way to get a project working.

But when you try to deploy it, the trouble begins.

If you've built something in Claude Code and found yourself wondering how to actually get it in front of users (or how to run it reliably in production) you're not alone. This is one of the most common friction points for developers building with AI today. Claude Code is a powerful prototyping and development environment, but it hands off the moment you need real infrastructure.

This guide covers what you need to know.

What Claude Code Doesn't Handle

Claude Code is a development tool. It helps you write, test, and iterate on AI-powered applications. What it doesn't provide is any of the infrastructure required to run those applications at scale, securely, for real users.

That gap is bigger than it sounds. Here's what you're responsible for the moment you move beyond local development:

Rate limiting and API cost management. Your Claude API calls cost money. Without rate limiting, a single misbehaving client or a traffic spike can generate thousands of dollars in unexpected charges overnight. You need to implement per-user and per-endpoint rate limits, set hard spending caps, and build logic to gracefully handle limit exhaustion, otherwise your cost structure is essentially uncontrolled.

Authentication and access control. Who is allowed to use your agent? How do they log in? How do you make sure one user can't access another user's data or conversation history? Without a proper auth layer, your application is either wide open or manually gatekept, neither of which works at any real scale.

Monitoring and observability. When something goes wrong in production, and it will, you need to know what happened. Which requests failed? Where did the agent produce unexpected outputs? What does latency look like across different workflows? Without structured logging and monitoring, you're flying blind.

Data encryption and storage. Any data your agent handles (user inputs, outputs, internal knowledge or documents, conversation history) needs to be stored securely. That means encryption at rest and in transit, controlled access policies, and a storage architecture that doesn't inadvertently expose sensitive information.

Compliance and audit trails. For enterprise use cases especially, you need to be able to demonstrate who accessed what, when, and why. That requires structured logging, retention policies, and often SOC 2 or ISO-compliant infrastructure depending on your industry.

None of this is unique to AI. It's the standard checklist for any production web application. But AI agents add complexity — longer-running processes, higher per-request costs, more sensitive data handling — that makes getting it right both more important and more difficult.

The DIY Deployment Stack

If you want to deploy your Claude Code agent yourself, here's what a reasonably complete stack looks like:

Backend hosting. You'll need somewhere to run your application logic. The main options are AWS (Lambda for serverless, EC2 or ECS for containerized workloads), Google Cloud (Cloud Run is popular for containerized AI apps), and Azure (App Service or Container Apps). Each has different trade-offs around cost, scalability, and operational complexity. All three require meaningful configuration to get right.

Frontend and API gateway. If your agent has a user-facing interface, you'll need to host it. Vercel and Netlify are common choices for frontend deployment. You'll also need an API gateway layer to handle routing, rate limiting, and authentication headers before requests reach your backend. AWS API Gateway, Kong, and Nginx are common options here.

Authentication. Building auth from scratch is a bad idea. Use a managed auth provider. Auth0, Clerk, and Supabase Auth are all solid options. You'll need to integrate your chosen provider with both your frontend and backend, configure OAuth flows if you're supporting social login, and implement role-based access control if different users should have different permissions.

Database. Your agent needs somewhere to store conversation history, user data, workflow outputs, documents. PostgreSQL on Supabase or AWS RDS is a common choice. You'll need to configure connection pooling, set up backups, and ensure your database is not publicly accessible.

Secrets management. Your Claude API key, database credentials, and other sensitive configuration should never be hardcoded. Use AWS Secrets Manager, Google Secret Manager, or a tool like Doppler to manage environment variables securely.

Monitoring and logging. At minimum, you need structured logging (Datadog, Logtail, and AWS CloudWatch are common), error tracking (Sentry is the standard), and ideally some form of LLM-specific observability to track prompt performance, latency, and output quality over time. LangSmith and Helicone are purpose-built for this.

Rate limiting. Implement rate limiting at the API gateway layer and consider per-user limits at the application layer too. You'll also want to set hard budget alerts on your Anthropic account so you're notified before costs spiral.

That's a realistic minimum. Depending on your use case, you may also need a queue system for async workflows (Redis, SQS), a vector database for RAG (Pinecone, Weaviate, pgvector), a CDN for frontend assets, and a CI/CD pipeline for deployments.

Each of these pieces is a project in itself. Each introduces new configuration, new failure modes, and new security surface area to manage.

Where Most Teams Get Stuck

The list above isn't just long, it's interconnected. Getting auth wrong affects your database security. Getting your API gateway wrong affects your rate limiting. A misconfigured storage bucket can expose data that your auth layer was supposed to protect.

Security vulnerabilities in production AI applications tend to compound. An exposed API key leads to unauthorized usage leads to unexpected costs leads to data exposure. The blast radius of a single misconfiguration is much larger than in traditional web applications, because AI agents often have broad access to internal systems and sensitive data by design.

This is the part that catches most teams off guard. Building the agent is the fun part. The infrastructure that makes it safe, reliable, and scalable is a different skill set. For teams that primarily think of themselves as AI builders rather than DevOps engineers, it's genuinely hard to get right.

Your Options

When you're ready to move from Claude Code prototype to production deployment, you have a few paths:

DIY on cloud infrastructure. AWS, GCP, and Azure give you maximum control and flexibility. If you have strong DevOps capacity on your team and specific infrastructure requirements, this is the right choice. Budget significant time for setup and ongoing maintenance.

Vercel + Supabase + Auth0. A popular stack for smaller teams that want managed services without full cloud complexity. Vercel handles frontend and serverless functions, Supabase handles database and auth, Auth0 handles more complex authentication flows. Still requires meaningful integration work but reduces operational overhead significantly.

Railway or Render. Simpler alternatives to AWS/GCP for teams that want container-based deployment without the full cloud provider complexity. Good for getting to production faster, with some trade-offs on scalability and enterprise compliance features.

Purpose-built AI deployment platforms. Tools like StackAI are designed specifically for teams that want to deploy AI agents and automations without building the underlying infrastructure from scratch. Authentication, access control, monitoring, rate limiting, secure storage, and enterprise compliance are handled at the platform level, so you're configuring your agent rather than your infrastructure. This trades flexibility for speed and significantly reduces the surface area you're responsible for securing.

The right choice depends on your team's technical depth, your timeline, your compliance requirements, and how much of your energy you want to spend on infrastructure versus building the actual AI workflows that create value.

The Honest Trade-off

There's no free lunch here. Building your own infrastructure gives you control and avoids vendor lock-in, but it takes time, requires expertise, and creates ongoing maintenance overhead. Using a platform trades some flexibility for dramatically faster deployment and a smaller attack surface.

For most teams building AI agents, especially enterprise-facing ones where security and reliability are non-negotiable, the question worth asking is: is infrastructure our core competency, or is it a means to an end? If your competitive advantage is in the AI workflows you're building, not in how you configure an API gateway, it's worth being honest about where your time is best spent.

Claude Code gets you to a working agent faster than anything else out there. What you do with it after that is the decision that determines whether it becomes a real product or stays a prototype.

Are you a Claude Code enthusiast wondering how to bring automation workflows safely to your team? We’d love to give you a demo of StackAI here.