I built a 'Heroku for LLMs' from scratch. I got 0 users. Here's why.

The Dream: Building the Ultimate AI Platform

A year ago, I set out to build my dream platform: cubik.ai. The vision was ambitious: a single, powerful environment where anyone could host, manage, and build applications on top of any Large Language Model (LLM).

It wasn't just another API wrapper. This was the whole stack.

What I Built: The Swiss Army Knife of AI

The Feature Set

The platform I architected was comprehensive:

• Model Hosting: Users could upload open-source models like Llama 3 or their own private LLMs
• Built-in Chat Application: Direct interaction with any hosted model
• RAG Support: Upload documents to create custom knowledge bases
• Auto-scaling Backend: Written in Go, supporting real-time, batch, and async APIs
• Deployment Pipeline: Users could deploy their entire configured app as a standalone service

The Technical Architecture

The backend was a marvel of engineering - handling concurrent requests, managing GPU resources, and providing multiple API interfaces. The auto-scaling system monitored metrics and dynamically allocated resources based on demand.

The Result: A Number I'll Never Forget

After a year of development and launch, the user count was: Zero.

Standing in the quiet aftermath of that launch, I realized my technical success was completely detached from business reality. But this wasn't a failure - it was the most expensive and valuable education I could have asked for.

The Three Critical Errors

1. The "Who is this for?" Problem

I was building for a hypothetical "AI developer". But who is that exactly?

• Data scientists need powerful fine-tuning tools
• Frontend developers need a simple, reliable API
• Startups need cost-effective, focused solutions

By trying to be everything for everyone, cubik.ai wasn't the perfect fit for anyone.

2. The Feature Buffet

The platform offered model hosting, RAG, multiple API types, and a chat UI. This wasn't a minimum viable product; it was a Minimum Viable Platform.

The value proposition was blurry. Instead of saying:

"This is the easiest way to add RAG to your app"

I was saying:

"Here's a complex system with 10 features, pick what you need"

This was a tough sell.

3. The "Last 10%" Trap

The platform was 90% done in 5 different areas. But that last 10% - polishing the UI, writing documentation, fixing edge cases - is another 90% of the work. I had multiple features that were almost ready, but none that were truly production-ready.

The Pivot: From Platform to Product

The lesson was burned into my mind: Start with a single, sharp tool, not a whole toolbox.

What I Should Have Built Instead

Instead of a comprehensive platform, I should have focused on one specific problem: building a simple document Q&A service that does one thing incredibly well.

The New Strategy

1. Pick one specific use case: Document Q&A for customer support teams
2. Make it incredibly easy: 3-line integration
3. Perfect the experience: Focus on accuracy and speed
4. Expand gradually: Add features based on actual user feedback

Lessons for Fellow Builders

Start Narrow, Go Deep

The wrong approach is building a comprehensive platform with hundreds of dependencies and complex setup. The right approach is creating something so simple that users can get value in minutes, not hours.

Validate Before You Build

Before writing a single line of code:

1. Talk to 10 potential users
2. Identify their biggest pain point
3. Build the smallest possible solution
4. Get them to pay for it

The Platform Trap

Platforms are seductive because they feel comprehensive and future-proof. But they're also:

• Harder to explain: "It does everything" isn't a value proposition
• Slower to ship: Every feature needs to work with every other feature
• Difficult to price: How do you price a Swiss Army knife?

The Silver Lining

While cubik.ai got zero users, it wasn't a complete loss:

• Technical skills: I learned Go, distributed systems, and GPU management
• Product intuition: I now viscerally understand product-market fit
• Network: The story itself became valuable for connecting with other builders

What's Next

I'm now building focused tools instead of platforms. Each one solves a specific problem for a specific audience. The first one already has paying customers.

The irony? Some of the code from cubik.ai is being repurposed into these focused products. The technical foundation wasn't wasted - it just needed the right product wrapper.

Key Takeaways

1. Platforms are hard: Start with a product, evolve to a platform
2. Users want solutions, not tools: Solve a specific problem completely
3. The last 10% matters most: A polished single feature beats 10 half-finished ones
4. Talk to users early: Build what they need, not what you think is cool

Building cubik.ai taught me that sometimes the most valuable lesson comes from spectacular failure. The next time you're tempted to build a platform, remember: start with a screwdriver, not a Swiss Army knife.

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Have you fallen into the platform trap? I'd love to hear your story. Connect with me on Twitter or LinkedIn.