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E2B vs Modal

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.

DimensionE2BModal
Overall8.68.4
Capability fit9.49.0
Price / performance8.08.2
Latency / reliability8.68.6
Developer & agent DX9.49.6
Privacy / data retention8.57.8
Lock-in risk (higher = worse)4.04.5
Maturity / track record8.08.2
PricingFirecracker microVM sandboxes billed per second; templates; code interpreter for agents.As 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…
TaglineDepot default code sandbox for agents. Use for untrusted code; Daytona when you need persistent…Depot darling for Python agent backends and bursty GPU jobs. If you think in functions not VMs, start here.

E2B

Depot default code sandbox for agents. Use for untrusted code; Daytona when you need persistent devworkspaces. Also the depot default Linux desktop sandbox for computer-use (e2b-desktop + VNC stream) when you already standardize on E2B. Strengths: Purpose-built for LLM code execution; Strong isolation (microVMs); Excel

Strengths

  • Purpose-built for LLM code execution
  • Strong isolation (microVMs)
  • Excellent agent SDK fit

Weaknesses

  • Per-sandbox-second costs add up
  • Less persistent than Daytona workspaces
  • Younger than hyperscalers
GET /api/providers/e2b

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

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