Introducing Anannas AI: Your Gateway to 500+ AI Models Through One API

The state of LLM infrastructure is a mess. You want to use Claude for reasoning, GPT-5 for general tasks, and Gemini for multimodal/long-context work; that means juggling four different APIs, four billing dashboards, four sets of rate limits, and four different ways things can break at 3 AM.

We built Anannas AI because we got tired of this. It's a single API that gives you access to 500+ models across OpenAI, Anthropic, Mistral, Gemini, DeepSeek, Nebius, and more. Think of it as your control panel for the entire AI ecosystem.

What Makes Anannas Different

You've probably heard of OpenRouter. Anannas is similar in concept but fundamentally better where it matters. We're up to 80% faster and 9% cheaper on average, with observability that actually helps you understand what's happening with your AI workloads.

The speed difference comes from our infrastructure design; we've obsessed over every millisecond. Our overhead is around 0.48ms, which means your requests get to the model and back without the latency bloat you see elsewhere. When you're running thousands of requests, this compounds fast.

The pricing is straightforward: we charge a 5% markup compared to OpenRouter's 5.5%, and our smart routing can direct requests to more cost-effective models when it makes sense. You can also bring your own keys if you prefer that model.

Observability That Actually Matters

Most API gateways give you basic request logs and call it a day. We built Anannas with observability as a first-class feature because production AI workloads need more than "request succeeded."

Cache performance metrics show you exactly how much you're saving through prompt caching; we track cache hit rates, cost savings, and token-level analytics so you can optimize your prompts for maximum efficiency. Tool and function call analytics help you understand how your agents are actually behaving in production, which calls are expensive, which are slow, which are failing. Model efficiency scoring gives you a holistic view across cost, latency, cache utilization, and reliability so you can make informed decisions about which models to use where.

Provider health monitoring and fallback routing mean your application keeps working even when a provider has issues. We track provider performance in real-time and automatically route around problems; you get the reliability of multiple providers without writing any of that logic yourself.

Production-Ready From Day One

Anannas is already powering production workloads at Bhindi, Scira AI, and more. We've handled over 100,000 requests with zero fallbacks required and stable latency throughout, processing over 1B+ tokens. That's not a beta disclaimer; it's production infrastructure that just works.

We maintain 99.999% uptime because our architecture is designed for reliability from the ground up. Multi-region deployments, intelligent failover, comprehensive monitoring. The infrastructure pieces that take months to build are already there.

The Vision: An Intelligent Inference Layer

Right now, Anannas is an incredibly fast and reliable way to access the AI ecosystem. But we're building toward something more ambitious: an intelligent inference layer that makes decisions for you.

Imagine describing your requirements (cost budget, latency constraints, quality needs) and having Anannas automatically route to the optimal model for each request. Or having it detect when a cheaper model would produce identical results and switch automatically. Or learning from your usage patterns to prefetch and cache the right things.

That's where we're headed; some of this already works today, more is coming soon.

Get Started

Register for an API key, make your first request, and see the difference. Our documentation has everything you need to get running, and we offer demo keys for teams that want to test against their actual workloads.

If you're currently using multiple LLM providers directly or through another gateway, we'd love to show you the performance and cost difference. Reach out at info@anannas.ai and we'll set up a demo environment.

We built Anannas because LLM infrastructure shouldn't be this complicated. You should be able to use the best model for each task without infrastructure becoming the bottleneck. That's what we've shipped, and we're just getting started.