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Fly.io vs Vercel

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Fly.io
cloudFree (3 shared VMs)
Vercel
cloudFree (Hobby)
Pricing
Free (3 shared VMs)Pay-as-you-go ($0.0015/hr per shared CPU)$29/mo LaunchCustom Enterprise
Free (Hobby)$20/user/mo ProCustom Enterprise
Open Source
Self-Hosted
SDK Languages
pythonjavascripttypescriptgorustrubyelixir
javascripttypescriptpythongoruby
Frameworks
None listed
vercel-ai
Compliance
soc2gdpr
soc2gdprhipaa
Best For
Run containers close to users globally — best for latency-sensitive agent APIs and real-time agent interactions
Best-in-class Next.js hosting with Vercel AI SDK — the default for deploying AI-powered frontend apps
Limitations
Steeper learning curve than Railway; pricing can be unpredictable with auto-scaling; smaller community; documentation is sometimes sparse
Vendor lock-in on serverless primitives; cold starts on serverless functions; expensive at scale for compute-heavy AI workloads; free tier has bandwidth limits

Supported Not supported Unverified

Fly.io and Vercel occupy opposite ends of the hosting spectrum for agent deployments. Fly.io provides Firecracker microVMs with persistent processes, private networking, and infrastructure-level control. Vercel provides serverless functions with Fluid Compute concurrency, an AI SDK, and a model provider marketplace. Fly.io wins on execution model, persistent compute, and multi-service networking. Vercel wins on AI developer experience for stateless LLM-calling agents.

Where Fly.io wins

  • Persistent microVMs with no execution timeout. Fly Machines run any Docker image as a full microVM with no execution limit. Machines run agent orchestrators, MCP tool servers, or background workers indefinitely while maintaining in-memory state, WebSocket connections, and long-running processes. Machines start and stop in subseconds and can auto-start on incoming requests. Vercel Functions terminate after responding—max 800 seconds (13 minutes) on Pro. No persistent processes, no Docker support, no always-on compute. Agents needing state across requests, planning loops, or MCP servers cannot run on Vercel without external infrastructure.

  • Private 6PN networking for multi-service agent architectures. Fly.io provides a private IPv6 network across all Machines in an organization with .internal DNS, TCP/UDP support, and WireGuard tunnels. Agent architectures—orchestrator, MCP tool servers, Postgres, Redis—communicate over the private network using standard DNS names with no public exposure. Vercel has no private networking between functions. Each function invocation is isolated: no service discovery, no internal DNS, no function-to-function communication without going through the public internet or external services.

  • Persistent volumes and managed Postgres co-located with compute. Fly.io persistent volumes provide NVMe storage attached to Machines for vector indexes, model weights, or agent artifacts. Managed Postgres runs as Fly Machines on the same private network. Vercel's storage is Blob (file storage), Edge Config (KV for feature flags), and marketplace integrations with external providers (Neon, Upstash, Supabase). None colocate with function compute—every database access routes to an external provider.

Where Vercel wins

  • AI SDK with unified streaming, tool calls, and model provider marketplace. Vercel's AI SDK offers a unified TypeScript interface for streaming chat completions, tool-call handling, and structured output across providers: OpenAI, Anthropic, Google, xAI, Groq, Together AI. The Vercel Marketplace provisions provider accounts from the dashboard and injects API keys as environment variables. For stateless agents calling external LLMs, this reduces integration work. Fly.io has no AI-specific SDK, marketplace, or managed model provider integrations—developers install SDKs, manage API keys, and configure streaming manually.

  • Fluid Compute concurrency for I/O-bound agent API calls. Vercel's Fluid Compute lets multiple concurrent invocations share one function instance with automatic bytecode caching to reduce cold starts. For I/O-bound agent workloads—parallel LLM API calls, embedding lookups, tool-call fan-out—this reduces resource usage and cold start frequency. Fly.io achieves concurrency by running multiple requests on the same Machine, but developers manage this in application code.

The agentic difference

The fundamental question is whether the agent needs a persistent process or fits the request-response model. Fly.io provides the infrastructure agent architectures need: long-lived processes maintaining state, a private network connecting agent services, and persistent storage co-located with compute. Vercel optimizes serverless functions for stateless agents calling external LLMs.

Most production agent systems—multi-step reasoning, MCP tool servers, conversation memory, background planning—need processes outliving a single HTTP request. Vercel's 800-second ceiling and lack of persistent compute make it unsuitable without external infrastructure. Fly.io's microVM model supports them natively.

When to pick which

  • Pick Fly.io when the agent needs persistent processes (MCP servers, orchestrators, background workers), private networking between services, persistent volumes, managed Postgres, or any compute outliving a single HTTP request—and AI SDK convenience is not a priority.

  • Pick Vercel when the agent is stateless, calls external LLM APIs behind a frontend, the team benefits from the AI SDK and model provider marketplace, every operation completes within 800 seconds, and needs no persistent state, inter-service networking, or always-on compute.

Last verified: 2026-05-09