The Problem: API Fragmentation
When we started MegaLLM, the goal was simple: developer experience. If you wanted to build an AI app in 2024/2025, you had to manage five different API keys (OpenAI, Anthropic, Google, Mistral, Meta).
We built a unified gateway to aggregate 70+ models under one standard OpenAI-compatible API. The idea exploded. In November alone, our infrastructure served massive traffic spikes, leading to a cloud bill exceeding $1,000,000 USD.
Scaling that fast broke things. We faced downtime, support backlogs, and valid frustration from our community. We also faced rumors about how our routing works.
Today, I want to peel back the curtain on our architecture, our pivot to paid plans, and how we actually route your prompts.
The Architecture of MegaLLM
We are not just a wrapper; we are a high-performance gateway. Our backend handles load balancing across multiple inference providers including AWS Bedrock, Open Router ,Microsoft Azure, Fireworks AI, and Baseten.
- The "Smart Router" (and the Spoofing Myth)
A common challenge and a source of recent rumors is how gateways handle specific model requests.
The Myth: "Gateways route expensive models (like Claude 3 Opus) to cheaper ones (like Sonnet) to save money."
The Reality: Our router is deterministic. If your API request specifies model: claude-3-opus, our gateway directs that traffic specifically to an Opus endpoint.
We aggregate supply. If AWS Bedrock is rate-limited on Opus, we might failover to another direct provider, but we do not silently downgrade the model class. You get the compute you pay for.
Caption: Internal cost breakdown showing massive consumption of genuine Claude Opus and Sonnet compute.
- Handling Scale & The $1M Bill
Scaling to millions of requests implies massive financial and technical weight.
Technical Debt: We are a lean team of 9. During our free tier explosion, we pushed code 24/7. We accumulated technical debt, which caused the recent instability.
The Pivot: We recently sunset our free tiers. Why? Because sustainable infrastructure requires sustainable economics. We cannot guarantee 99.9% uptime for enterprise clients while burning cash on valid but massive free usage.
Caption: Scaling isn't free. Our November infrastructure costs.
What We Are Building Now
We are moving from "growth at all costs" to "stability first."
Diversified Compute: We are integrating faster, specialized providers like Cerebras for near-instant inference and Baseten for custom fine-tunes.
Transparency: We are rolling out dashboards that give you granular visibility into exactly which provider handled your request.
Support 2.0: We are building a new support system with real-time screen sharing and voice agents to help developers debug integration issues live.
Final Thoughts
Building in public is painful. When you break, everyone sees it. But it’s also rewarding. MegaLLM is now stronger, more stable, and fully focused on our paid and enterprise partners who need a reliable pipe for AI intelligence.
To the developers sticking with us: We are just getting started.
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