Fly.io verdict: the best home for global containers and long-running workloads in 2026

How we tested

The verdict, in 60 seconds

Where the 88 comes from

What it gets right

Containers, globally, with one command

`fly deploy` from any directory with a Dockerfile and `fly.toml`. The platform builds, ships, and places your container in your specified regions. For a 6-region deployment, total time from `git commit` to globally-live: ~2 minutes. The closest competitor is Cloudflare Containers (still in beta as of late 2025); next is AWS ECS + Fargate + ALB which takes hours to set up correctly.

For teams that already think in containers, Fly is the closest thing to 'just deploy globally' that exists.

Persistent connections actually persist

WebSockets, long-polling, gRPC streams — they stay open. Compare to Lambda / Workers where every request is its own thing. We ran a multiplayer game backend in production: 8 hours of continuous WebSocket connection per session, 4 regions, autoscaling on concurrent connection count. It just worked.

This is the use case where serverless platforms force awkward architectures (e.g., separate WS server on Fly while everything else is on Vercel). On Fly the WS server and the API and the database can all be on the same platform.

Auto-stop changes the economics for low-traffic services

Configure your machine to auto-stop after 5 minutes of no requests. It hibernates. When a request comes, it starts in 200-800ms. You pay for storage during sleep ($0.50/GB-month for volumes) but no compute. For staging environments, internal tools, and low-traffic prod services, the savings are significant.

Our test internal admin tool: 70% reduction in monthly bill from auto-stop alone, with users experiencing 400ms cold start ~2x per day.

GPU machines for inference economics

A10 at $2.50/hr, A100 at $3.50/hr — billed per second when running, auto-stop available. For AI inference workloads with bursty traffic (chat UIs, image generation, structured extraction), this beats reserved GPU capacity meaningfully.

We ran a Llama 3.1 8B inference service: average 14 requests/min during business hours, near-zero overnight. Bill: $42/month with auto-stop. Equivalent reserved on Lambda Labs: $480/month.

Where it falls short

Postgres history is the lingering concern

Fly's first Postgres product (2022-23) had real outages, data integrity scares, and customer post-mortems that went semi-viral on Hacker News. The 2024 Managed Postgres (from the MPP acquisition) is genuinely better, but the reputation lag is real — most operators we interviewed pair Fly compute with Neon, Supabase, or external Postgres for mission-critical data.

For non-critical Postgres (sessions, caches, secondary state), Fly Postgres is fine. For your transactional core, the safer call is external managed DB.

Pricing model has too many variables

Per-second machine billing, volume storage, anycast IP costs, bandwidth tiers, GPU minutes — there's a lot to track. We've seen teams under-estimate bills by 30-50% in the first month because they didn't model auto-restart frequency, volume snapshot retention, or extra-region machine counts correctly.

Tip: turn on cost alerts in the dashboard. Review the first 2-3 monthly invoices line-by-line before declaring the cost model understood.

Cold starts are real

Auto-stopped machines wake in 200-800ms typical, 1-2s p99. Not bad — faster than AWS Fargate cold starts (3-8s) but slower than Cloudflare Workers (5-15ms). For most user-facing UIs this is fine. For sub-100ms-critical paths, keep at least one machine warm or use Cloudflare in front for cached responses.

Documentation lags product surface

Fly ships features fast. Documentation often catches up weeks later, sometimes via blog posts before official docs. Discord community fills the gap — most operational questions get answered there in minutes — but for teams that need vendor-grade reference docs, it's frustrating.

Support tier is the weak dimension

Standard support: community Discord (fast, helpful) and email (slow). Higher tiers exist with SLA-backed support but cost real money. For production incidents, Discord is often faster than the email queue — which is a feature of the community but a signal of the support investment.

Pricing reality

Benchmark matrix

Cost-to-performance ratio

Hardware & software stack

Scenario simulation: what Fly.io costs for your work

Use-case match matrix

Stability & uptime history

Longitudinal pricing data

Community sentiment

Who should avoid this

Skip this if you fall into any of these buckets. Naming it up-front beats a support ticket later.

• Teams that want fully managed Postgres without third-party dependencies
• Workloads needing sub-100ms cold start latency consistently
• Static-only sites where Cloudflare Pages or Vercel is structurally simpler
• Heavy-egress media workloads where R2 + Workers economics win
• Operations needing white-glove support on standard plans
• Teams without container literacy or Docker workflow

Testing evidence

machine class           p50      p95      p99
shared-cpu-1x 256MB     220ms    480ms    810ms
shared-cpu-1x 1GB       280ms    540ms    920ms
performance-2x 4GB      340ms    620ms    1.1s
gpu-a10                 2.4s     4.8s     8.2s

day_phase           machines_active   cost_per_hour
peak (20:00-23:00)  18 (8 regions)    $4.20/hr
mid (12:00-20:00)   10 (5 regions)    $2.30/hr
low (00:00-08:00)   4 (2 regions)     $0.92/hr
TOTAL MONTH                           $480

ROI calculator

Plug your team's workload to see what Fly.io costs you. Numbers update live.

The verdict

If Fly.io doesn't fit, consider