Fly.io Performance Tuning

Overview

Optimize Fly.io performance: eliminate cold starts, right-size VMs, leverage multi-region for low latency, and tune concurrency settings.

Prerequisites

A redacted baseline for latency, error rate, saturation, cost, and region-level health.
A staging application, synthetic load, named change owner, and a tested rollback mechanism.

Output

Publish a tuning receipt with baseline/post-change aggregate metrics, VM/concurrency settings, regions affected, canary result, owner, and rollback outcome. Exclude runtime secrets, request bodies, and user data.

Error Handling

Stop rollout on health, latency, saturation, or cost threshold breaches and revert the canary.
Reduce concurrency or capacity changes before retrying a failed region promotion.
Keep diagnostics redacted and route deployment incidents to the on-call owner.

Examples

Run a synthetic load test against a staging region, adjust one VM setting, and compare aggregate p95 latency and error rate. Simulate a failed health check and confirm the release rolls back before traffic expands.

Instructions

Step 1: Eliminate Cold Starts

# fly.toml — suspend instead of stop for faster resume (~100ms vs ~5s)
[http_service]
  auto_stop_machines = "suspend"   # Suspend to RAM, not full stop
  auto_start_machines = true
  min_machines_running = 1          # Always-warm in primary region

# For latency-critical: keep machines running in all regions
# min_machines_running applies globally

Step 2: Right-Size VMs

# Check current allocation
fly scale show -a my-app

# Start small, scale up based on metrics
fly scale vm shared-cpu-1x --memory 256    # Start here
fly scale vm shared-cpu-1x --memory 512    # If memory-constrained
fly scale vm shared-cpu-2x --memory 1024   # If CPU-bound
fly scale vm performance-2x --memory 4096  # For compute-heavy workloads

Workload	VM	Memory	When
Static site / API proxy	shared-cpu-1x	256mb	Low traffic
Node.js API	shared-cpu-1x	512mb	Most apps
Heavy processing	shared-cpu-2x	1gb	Background jobs
Database / ML	performance-2x	4gb	Compute-intensive

Step 3: Multi-Region Latency Optimization

# Deploy close to your users
fly scale count 1 --region iad    # US East
fly scale count 1 --region lhr    # Europe
fly scale count 1 --region nrt    # Asia Pacific

# Fly automatically routes to nearest region via Anycast
# Verify: curl with timing
curl -w "DNS: %{time_namelookup}s, Connect: %{time_connect}s, Total: %{time_total}s\n" \
  -o /dev/null -s https://my-app.fly.dev/health

Step 4: Connection Pooling for Postgres

// Use connection pooling for Fly Postgres
// PgBouncer runs on port 5433 (pooled) vs 5432 (direct)
const pooledUrl = databaseConfig.pooledEndpoint;

// Enable PgBouncer mode through the client's typed configuration.

Step 5: Tune Concurrency

[http_service.concurrency]
  type = "requests"       # or "connections"
  hard_limit = 250        # Max before rejecting
  soft_limit = 200        # Start scaling at this point

Resources

Next Steps

For cost optimization, see flyio-cost-tuning.