a2a cloud vs Fly.io for deploying AI agents
Fly.io runs fast-booting VMs close to your users, and Fly Machines are a great primitive for latency-sensitive apps. But it gives you compute, not an agent — and with GPU machines deprecated after August 2026, GPU agent workloads need a new home. a2a cloud deploys the whole agent app and proves what it did.
Where Fly.io is genuinely strong — and where a2a cloud is different.
Fast VMs, run anywhere
Fly Machines boot in seconds and run close to your users, with a clean CLI and a genuinely good developer experience for latency-sensitive apps. If you want raw, global compute you fully control, Fly is excellent at it.
The whole agent app, not just the VM
a2a cloud gives each agent managed Postgres, MCP, an OpenAPI gateway, a frontend, and auth. Signed grants scope grant-backed operations. The runtime signs Agent API calls; the gateway signs public /invoke and MCP tools/call.
a2a cloud vs Fly.io, dimension by dimension.
A fair comparison. Both columns are accurate as we understand the products today — the difference is what the runtime owns by default.
Pick the tool that matches the job.
Reach for Fly.io when
- You want raw, globally-distributed VMs you fully control.
- Latency and edge placement are your primary concern.
- You're happy to build the DB, MCP, auth, and audit layers yourself.
Reach for a2a cloud when
- You need the full agent app — DB, MCP, API, frontend — not just compute.
- You're migrating GPU agent workloads off Fly before the Aug 2026 deprecation.
- You need scoped grants and signed receipts for governance and proof.
Trust the receipt.
a2a cloud deploys any agent — LangGraph, OpenAI Agents SDK, CrewAI, or custom — and ships it with a managed Postgres database, an MCP server, an API, a frontend, and an Ed25519-signed receipts on governed execution paths. Scoped grants, no ambient production access. One deploy, the whole agent app, with proof.