Why We Built LLM Stack​

As developers working with LLMs, we've all been there. You start with a simple API call to ChatGPT, and before you know it, you're building an entire infrastructure around it. Rate limiting, error handling, retries, monitoring, cost tracking – suddenly your "quick LLM integration" has turned into a full infrastructure project.

The Real Problem​

The truth is, building AI applications today involves too much boilerplate. For every hour spent on actual application logic, developers spend 5-10 hours on infrastructure code. Want to switch between different providers? That's another week of work. Need production-grade monitoring? There's another sprint gone.

This isn't just inefficient – it's actively holding back innovation. Small teams that could be building interesting AI applications are instead stuck reinventing the infrastructure wheel.

What LLM Stack Actually Does​

At its core, LLM Stack is an infrastructure layer that handles all the boring parts of building AI applications:

• Instead of spending days setting up provider SDKs, rate limiters, and error handlers, you write a prompt and get an API endpoint
• Instead of building your own monitoring system, you get real-time visibility into costs, performance, and reliability
• Instead of managing multiple provider integrations, you get a unified interface that lets you switch models with zero code changes

The goal isn't to lock you into our platform – it's to handle the infrastructure so you can focus on what makes your application unique.

Long-term Vision​

We're building LLM Stack because we believe AI infrastructure shouldn't be a barrier to entry. In the long run, we want to be the platform that lets developers:

1. Deploy AI features as easily as they deploy web apps today
2. Experiment with different models without infrastructure overhead
3. Scale their applications without rebuilding their stack

Should You Use It?​

LLM Stack might be right for you if:

• You're building a production AI application and don't want to maintain infrastructure
• You need to move fast and iterate quickly on AI features
• You want production-grade monitoring and debugging from day one

It might not be right if:

• You need extremely custom infrastructure requirements
• You're just experimenting with LLMs (use the provider SDKs directly)
• Your use case requires specialized model deployment

What's Next​

We're focused on making the developer experience even better. Coming soon:

• More granular monitoring and debugging tools
• Built-in vector store integration
• Custom workflow builders

If you're tired of building and maintaining AI infrastructure instead of building features, give LLM Stack a try. We'd love to hear your feedback.

Get Started →