Ironlane provides a platform that helps developers select, test, and deploy open‑source AI models while accurately forecasting per‑token costs. By offering a unified API and cost‑prediction engine, it lets teams compare performance and pricing, enforce budget controls, and switch models without rewriting integrations, supporting deployment on any major cloud provider.
Funding
Funding not disclosed
Founders
Product
Problem
AI production teams struggle to predict inference costs, select defensible open-source models, and move from prototype to production without rebuilding infrastructure, leading to unpredictable spend and potential outages.
Solution
Ironlane offers an OpenAI‑compatible inference platform that automatically recommends the most cost‑effective open-source model for a given task, providing per‑token pricing and budget caps to forecast spend. Users can test models instantly in a playground, add retrieval‑augmented generation (RAG) context, and then deploy the same API to their own cloud environments (AWS, GCP, Azure) for security and compliance. The platform maintains a consistent request shape from development through production, eliminating the need to rewrite integrations or manage separate model stacks. Built‑in fallback routing ensures continuity if a primary provider fails, while usage‑based billing and transparent cost estimates keep budgets under control.
Target Audience
Primary customers are mid‑market product and engineering teams building AI‑driven applications that require scalable, cost‑predictable inference, as well as enterprises needing secure, cloud‑native deployment of open-source LLMs.
Features
- Intelligent model discovery engine that matches job requirements to the optimal open-source or frontier model for performance and price
- OpenAI‑compatible API allowing drop‑in replacement of existing client libraries with a single endpoint change
- Per‑token pricing, budget controls, and spend estimation tools for predictable cost management
- Playground for rapid testing of model output, latency, and cost before committing to production
- RAG‑ready context injection with knowledge‑base IDs and managed prompt templates, eliminating separate retrieval services
- Bring‑your‑own‑cloud deployment to AWS, Google Cloud, or Azure for data residency, security, and compliance
- Automatic provider fallback and load balancing to maintain service availability during outages