E2B vs Modal
Compare E2B and Modal by workflow, pricing, privacy, model support, and best use cases.
E2B
Choose E2B when you are building AI agents, code interpreters, browser/computer-use agents, or LLM applications that need secure, fast, isolated code execution. Choose GitHub Codespaces, Gitpod/Ona, or Coder for full developer workspaces, CodeSandbox for collaborative cloud sandboxes, or Daytona/Vercel Sandbox when their pricing, lifecycle, or platform fit is better for your agent runtime.
Modal
Choose Modal when you need Python-first serverless compute for AI, data, GPU, inference, batch jobs, queues, notebooks, or backend services. Choose E2B or Daytona for dedicated AI sandbox infrastructure, Vercel Sandbox for Vercel-native code execution, RunPod or Baseten for alternative GPU hosting, and GitHub Codespaces or Coder for full developer workspaces.
Key Differences
Workflow
E2B is an open-source AI sandbox cloud for giving agents secure, isolated computers where they can execute code, use tools, and run workflows.
Modal is a serverless compute platform for AI, data, Python, GPU, batch, sandbox, notebook, and inference workloads that need elastic cloud execution without infrastructure management.
compare.fields.openSource
Yes
No
Feature Comparison
| Feature | E2B | Modal |
|---|---|---|
| Primary workflow | E2B is an open-source AI sandbox cloud for giving agents secure, isolated computers where they can execute code, use tools, and run workflows. | Modal is a serverless compute platform for AI, data, Python, GPU, batch, sandbox, notebook, and inference workloads that need elastic cloud execution without infrastructure management. |
| Type | resource | resource |
| Editor base | CLI | CLI |
| Pricing model | freemium | freemium |
| Starting price | $0 | $0 |
| Free plan | Yes | Yes |
| Open source | Yes | No |
| Local models | No | No |
| BYOK | No | No |
| Platforms | Cloud sandboxes, JavaScript SDK, TypeScript SDK, Python SDK, REST API, CLI, GitHub Actions, MCP Gateway, Docker MCP Catalog, Cloud browser, Desktop sandbox, Linux microVMs, Custom sandbox templates | Python SDK, CLI, Web dashboard, Serverless functions, GPU containers, Web endpoints, Cron jobs, Job queues, Modal Sandboxes, Modal Notebooks, Persistent volumes, Cloud-hosted Linux containers |
| Models | Unknown | Unknown |
| Enterprise features | Custom pricing, Custom concurrency above 1,100 sandboxes, Custom CPU and memory ceilings, Higher disk limits, Custom continuous runtime, Enterprise support, Bring Your Own Cloud documentation path, Security review, Trust Center, Lifecycle webhooks, Metrics, OpenTelemetry export, Custom templates, Private registries, Custom MCP servers, MCP Gateway, Sandbox public URL controls, Proxy tunneling, Custom domains, Secured access | Team workspace, Enterprise contracts, Custom support, Production workload governance, Usage visibility, Secrets management, Environment separation, Persistent volumes, Web endpoints, Custom containers and images, Autoscaling controls, GPU access, Modal Sandboxes, Modal Notebooks, Dashboard observability, Security and compliance review through enterprise sales |
| Best for | AI agent code execution, Code interpreter products, Running untrusted AI-generated code, LLM tool execution, Data analysis agents, Coding agents that need terminal and filesystem access, Computer-use agents, Cloud browser workflows, MCP-enabled agent tools, GitHub Actions validation, Sandboxed test runners, Products that need fast isolated execution environments | Serverless GPU inference, LLM serving, Image and video generation workloads, Speech and audio processing, Batch data processing, Fine-tuning jobs, Parallel Python jobs, Scheduled compute, AI backend services, Model APIs, Data science workloads, Code execution backends, Agent infrastructure that needs scalable compute, Teams that want cloud GPUs without managing Kubernetes |
| Not best for | Developers looking for a complete cloud IDE, Non-technical users looking for prompt-to-app builders, Teams that only need simple frontend playgrounds, Projects that require fixed monthly pricing with no usage metering, Workflows where agents do not need to execute code, Organizations unwilling to manage sandbox permissions, secrets, and network policies | Developers looking for a browser IDE, Users looking for AI autocomplete or code chat, Non-technical users looking for prompt-to-app builders, Teams needing a GitHub-native cloud development environment, Workloads that require fully self-hosted or on-prem execution, Projects needing fixed monthly compute pricing with no usage variability, Simple frontend demos better served by StackBlitz or CodeSandbox |
Use Case Winners
Modal is built around a CLI editor workflow.
Both E2B and Modal have comparable signals here.
E2B lists more team or enterprise controls.
E2B has stronger frontend or web workflow signals.
Neither tool shows a strong signal for this use case in the current structured data.
E2B is marked as open source.
Pricing Comparison
E2B
- Hobby$0 / month
Free plan with one-time $100 usage credits, community support, up to 1-hour sandbox sessions, and up to 20 concurrent sandboxes.
- Pro$150 / month
Adds higher limits, custom sandbox CPU and RAM, up to 24-hour sessions, up to 100 concurrent sandboxes, and optional extra concurrency up to 1,100.
- EnterpriseCustom
Custom pricing, higher limits, custom compute, 1,100+ concurrent sandboxes, and enterprise deployment or support discussions.
- Compute usageFrom $0.000014 / vCPU-second
Usage-based compute billing while sandboxes are running. Default 2 vCPU costs $0.000028/second.
- Memory usage$0.0000045 / GiB-second
Memory is billed per GiB-second while a sandbox is actively running.
Modal
- Starter$0 / month
Free workspace plan with usage-based compute billing for serverless CPU, memory, GPU, sandbox, storage, and related resources.
- Team$250 / month
Team workspace plan plus compute usage, designed for shared production workloads, collaboration, and higher team needs.
- EnterpriseCustom
Custom pricing and support for larger organizations with security, compliance, governance, scaling, and procurement needs.
- CPU and MemoryUsage-based / second
Serverless functions and workloads are billed by requested compute resources and execution time.
- GPUUsage-based / second
GPU instances such as T4, L4, A10G, L40S, A100, H100, H200, and B200 are priced by GPU type and runtime.
Privacy & Security
E2B
E2B sandboxes can contain AI-generated code, uploaded files, command output, environment variables, MCP tool results, browser activity, Git context, and persistent filesystem or memory state. Teams should avoid placing production secrets directly into generated code or broad sandbox environments, restrict network and MCP tool access, review custom templates, use lifecycle controls to pause or kill idle sandboxes, and define retention policies for snapshots, files, logs, and agent outputs.
Modal
Modal workloads can process application code, container images, environment variables, secrets, model files, datasets, logs, notebooks, sandbox contents, volumes, and runtime outputs. Teams should configure Modal Secrets, control data copied into images or volumes, limit public endpoints, review logs for sensitive output, and design retention, access, and network policies before running proprietary models, private data, or generated-code execution workloads.
Choose E2B if...
- AI agent code execution
- Code interpreter products
- Running untrusted AI-generated code
- LLM tool execution
- Data analysis agents
Choose Modal if...
- Serverless GPU inference
- LLM serving
- Image and video generation workloads
- Speech and audio processing
- Batch data processing
Avoid E2B if...
- Developers looking for a complete cloud IDE
- Non-technical users looking for prompt-to-app builders
- Teams that only need simple frontend playgrounds
- Projects that require fixed monthly pricing with no usage metering
- Workflows where agents do not need to execute code
Avoid Modal if...
- Developers looking for a browser IDE
- Users looking for AI autocomplete or code chat
- Non-technical users looking for prompt-to-app builders
- Teams needing a GitHub-native cloud development environment
- Workloads that require fully self-hosted or on-prem execution