CodeSandbox vs AgentHost
Detailed side-by-side comparison to help you choose the right tool
CodeSandbox
🟡Low CodeApp Deployment
CodeSandbox is a cloud development and code-execution platform — now part of Together AI — built around the Sandbox SDK and Firecracker microVMs with 2-second startup for AI agents and dev environments.
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Starting Price
FreeAgentHost
🔴DeveloperApp Deployment
Serverless hosting platform specifically designed for deploying and scaling AI agents.
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Starting Price
$49/monthFeature Comparison
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CodeSandbox - Pros & Cons
Pros
- ✓2-second VM startup on Firecracker microVMs is best-in-class for many AI agent workloads
- ✓Sandpack and Storybook integrations have unmatched distribution across JS/React docs and learning sites
- ✓Together AI ownership ties the SDK to a clear model/inference + agent infrastructure path
Cons
- ✗Pricing page now blocks crawlers (HTTP 403) — pricing transparency dropped after the Together AI acquisition
- ✗No public MCP server yet — agent integrations go through the CodeSandbox SDK directly
- ✗Modal Labs and E2B can be cheaper per second for pure Python eval workloads without browser IDE needs
AgentHost - Pros & Cons
Pros
- ✓Purpose-built persistent memory layer that the company claims delivers up to 40% faster context retrieval than standard database-backed solutions
- ✓Kernel-level sandboxing with granular network egress controls lets agents safely execute untrusted code
- ✓NVIDIA H100 and A100 GPU clusters available for local inference on open-weight models (128 new H100 nodes added Feb 2026)
- ✓Pro plan at $99/month bundles 5 agent instances, 16GB RAM, and 100GB SSD — cheaper than equivalent AWS setup (~$93/month before memory/sandbox config)
- ✓Full SSH access and framework-agnostic deployment — not locked into a proprietary flow
- ✓Pre-built templates for AutoGPT, LangChain, CrewAI, and AutoGen speed up production deployment
Cons
- ✗No free tier — minimum commitment is $49/month, unlike Modal which starts at $0 pay-per-use
- ✗Starter plan's 8GB RAM and single instance is tight for agents running local models or large context windows
- ✗Relatively new platform means a thinner track record and smaller community than AWS, GCP, or Azure
- ✗Limited geographic regions compared to hyperscalers may affect global latency for some deployments
- ✗Specialized infrastructure creates vendor risk — migrating off agent-specific features requires reengineering
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