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Modal vs Fly Machines

Agent-native side-by-side. Scores are curated estimates on a 0–10 scale. Lock-in: higher = worse. Prefer JTBD picks via /api/decide when choosing for a job.

DimensionModalFly Machines
Overall8.48.2
Capability fit9.08.5
Price / performance8.28.5
Latency / reliability8.68.2
Developer & agent DX9.68.0
Privacy / data retention7.87.8
Lock-in risk (higher = worse)4.53.0
Maturity / track record8.28.5
PricingAs of 2026-10-24 (modal.com/pricing, fetched 2026-10-24): Python-native serverless GPU/CPU billed per second of compute ('You never pay for idle resources'). GPU list rates per second: H100 SXM5 '$0.001097', H200 SXM '$0…API-driven Firecracker VMs on Fly; pay while running; great for custom sandboxes.
TaglineDepot darling for Python agent backends and bursty GPU jobs. If you think in functions not VMs, start here.DIY sandbox substrate with global edge. Prefer E2B unless you need custom machine control.

Modal

Depot darling for Python agent backends and bursty GPU jobs. If you think in functions not VMs, start here. Strengths: Best-in-class Python DX for serverless compute; GPUs + sandboxes + cron in one model; Scale-to-zero economics.

Strengths

  • Best-in-class Python DX for serverless compute
  • GPUs + sandboxes + cron in one model
  • Scale-to-zero economics

Weaknesses

  • Premium vs raw RunPod $/GPU-sec sometimes
  • Python-centric (polyglot harder)
  • Less classic VM control
GET /api/providers/modal

Fly Machines

DIY sandbox substrate with global edge. Prefer E2B unless you need custom machine control. Strengths: Programmable VMs globally; Firecracker isolation; Integrate with Fly networking.

Strengths

  • Programmable VMs globally
  • Firecracker isolation
  • Integrate with Fly networking

Weaknesses

  • You build more sandbox UX yourself
  • Not agent-specialized like E2B
  • Ops knowledge required
GET /api/providers/fly-machines

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