Fly.io

Edge Compute Meets AI

Fly.io is legendary in the web development world for its ability to deploy Docker containers close to users globally using Anycast routing. Recently, Fly.io has aggressively entered the AI space by adding powerful GPUs (like the NVIDIA L40s and A100) to their edge locations. This allows developers to deploy their AI inference containers natively in Paris, Tokyo, or Chicago, ensuring that end-users experience ultra-low latency responses.

Raw Power, Maximum Freedom

Fly.io is not a managed MLOps platform; it is raw, unadulterated infrastructure. They do not offer a curated “Model Catalog” or a proprietary API. Instead, developers package their models using frameworks like vLLM, Ollama, or custom FastAPI wrappers into standard Docker containers. Fly.io then deploys and scales these containers globally. This appeals massively to engineers who want absolute control over their inference architecture without vendor lock-in.

Disruptive GPU Pricing

By leveraging their massive global footprint and utilizing efficient hardware like the L40s (which offers exceptional inference performance for LLMs), Fly.io provides highly disruptive hourly compute pricing. For startups that have outgrown “pay-per-token” managed services and want to host their own dedicated models, Fly.io offers an incredibly affordable, highly scalable middle ground.

Supported Workloads