E2B vs Cua
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.
| Dimension | E2B | Cua |
|---|---|---|
| Overall | 8.6 | 8.0 |
| Capability fit | 9.4 | 8.8 |
| Price / performance | 8.0 | 8.0 |
| Latency / reliability | 8.6 | 7.6 |
| Developer & agent DX | 9.4 | 8.6 |
| Privacy / data retention | 8.5 | 8.2 |
| Lock-in risk (higher = worse) | 4.0 | 3.8 |
| Maturity / track record | 8.0 | 7.0 |
| Pricing | Firecracker microVM sandboxes billed per second; templates; code interpreter for agents. | As of 2026-09-14 (cua.ai): OSS MIT sandbox/driver (Docker/QEMU/Apple VZ local). Cua Fleet usage-based: CPU $0.044625/vCPU/hr, memory $0.0223125/GB/hr. Cloud Windows sandboxes GA (credits: Small 8/hr, Medium 15/hr, Large … |
| Tagline | Depot default code sandbox for agents. Use for untrusted code; Daytona when you need persistent… | Best Scrapybara/E2B Desktop peer when you need Windows+macOS+Android fleets or local OSS sandboxes. E2B… |
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
Cua
Best Scrapybara/E2B Desktop peer when you need Windows+macOS+Android fleets or local OSS sandboxes. E2B remains the depot Linux-desktop default; Cua when OS coverage is the job. Strengths: True multi-OS computer-use fleets (Linux/Windows/macOS/Android) plus OSS local runtime; Warm pools / fork-from-snapshot for eval an
Strengths
- True multi-OS computer-use fleets (Linux/Windows/macOS/Android) plus OSS local runtime
- Warm pools / fork-from-snapshot for eval and RL rollouts
- Background driver does not steal the user cursor
Weaknesses
- Younger hosted SLA story than E2B
- Credit vs $/vCPU packaging can confuse forecasting
- macOS still preview
More pairs: anthropic-vs-openai · openai-vs-google-ai · groq-vs-fireworks · fireworks-vs-together · qdrant-vs-pinecone · pinecone-vs-supabase-pgvector · e2b-vs-modal · browserbase-vs-firecrawl