Quick Verdict: E2B vs Modal
Bottom Line
E2B and Modal solve different problems. E2B gives AI agents a sandboxed execution environment (Firecracker microVMs, sub-200ms cold starts, persistent filesystems). Modal gives developers serverless GPU/CPU compute for ML workloads (elastic scaling, per-second billing, zero infrastructure). Pick based on what you are building, not which is "better."
Feature Comparison: E2B vs Modal
| Feature | E2B | Modal |
|---|---|---|
| Primary purpose | AI agent sandboxes | Serverless compute (GPU/CPU) |
| Isolation technology | Firecracker microVMs | gVisor-sandboxed OCI containers |
| GPU support | No | Yes (A10G, A100, H100, L40S) |
| Cold start | <200ms (standard sandbox) | ~300ms-2s (varies by image size) |
| SDK languages | Python, TypeScript/JS | Python only |
| Persistent filesystem | Yes, per sandbox | Volumes (network-attached) |
| Internet access per instance | Yes, each sandbox gets outbound access | Yes, configurable |
| Custom templates/images | Sandbox templates (Dockerfile-based) | Container images (Dockerfile-based) |
| Scheduling / cron | No | Yes, built-in cron scheduling |
| Web endpoints | No (sandboxes are ephemeral compute) | Yes, ASGI/WSGI web serving |
| Concurrency model | One sandbox per agent session | Parallel containers with auto-scaling |
| Multi-tenancy isolation | Hardware-level (microVM) | OS-level (gVisor) |
| Designed for | LLM tool-use, code interpreters, agent loops | ML training, batch inference, data pipelines |
| Free tier | 100 sandbox hrs/month | $30/month credits |
Architecture: MicroVMs vs Containers
The core technical difference between E2B and Modal is the isolation boundary. This choice cascades into everything else: security model, cold start time, resource overhead, and what workloads each platform handles well.
E2B: Firecracker MicroVMs
Each E2B sandbox is a Firecracker microVM, the same technology AWS Lambda uses. Every sandbox gets its own Linux kernel, filesystem, and network stack. This provides hardware-level isolation: a sandbox running untrusted LLM-generated code cannot escape to the host or affect other sandboxes. The tradeoff is no GPU passthrough, which is why E2B focuses on CPU-bound code execution rather than ML inference.
Modal: gVisor Containers
Modal runs workloads in OCI containers with gVisor sandboxing. Containers share the host kernel through gVisor's application kernel layer, which intercepts syscalls for security. This approach enables GPU passthrough (containers can access host GPUs directly), faster startup for large images, and efficient resource sharing. The tradeoff is weaker isolation than a full VM boundary.
Why the Isolation Model Matters
When an AI agent generates and executes code, you are running untrusted input. The agent might write import os; os.system('rm -rf /') because the LLM hallucinated, or because a prompt injection attack directed it to. E2B's microVM isolation means this destroys the sandbox and nothing else. The blast radius is one disposable VM.
Modal's container isolation is sufficient for first-party code (your own ML pipelines, your own functions) where the threat model is bugs, not adversarial input. For agent sandboxing where the code source is an LLM, the stronger isolation boundary of a microVM is the safer default.
Use Case Fit
The cleanest way to think about E2B vs Modal: what is the source of the code being executed?
E2B: Agent-Generated Code
Code interpreters for LLM assistants. Agent tool-use (file operations, shell commands, package installs). Multi-step agent loops where each step may run arbitrary code. Jupyter-style notebook execution in the cloud. Any workflow where an AI model generates code that needs to run safely.
Modal: Developer-Written Code
ML model training and fine-tuning on GPUs. Batch inference pipelines (process 1M images, transcribe 10K audio files). Data processing and ETL. Web API serving with auto-scaling. Scheduled jobs and cron tasks. Any workload where a developer writes the code and needs elastic cloud compute.
They Complement, Not Compete
A common production pattern: Modal runs your model inference endpoint. Your agent framework calls the model via Modal, receives generated code, then executes that code in an E2B sandbox. Modal handles the GPU-heavy inference. E2B handles the untrusted execution. Different layers, same pipeline.
SDK and Developer Experience
Both platforms invest heavily in developer experience, but their SDKs reflect their different target workflows.
E2B: SDK-First for Agent Frameworks
E2B's SDK is designed to be called from inside an LLM tool-use loop. The core primitives are Sandbox.create(), sandbox.run_code(), sandbox.files, and sandbox.commands. You create a sandbox, hand it to your agent as a tool, and the agent uses it to execute code, read/write files, and run shell commands. First-class integrations exist for LangChain, CrewAI, OpenAI Assistants, and Vercel AI SDK.
The TypeScript SDK matters here. Most agent frameworks (Vercel AI SDK, LangGraph.js) run in Node.js/TypeScript. E2B supports both Python and TypeScript natively, so you do not need a Python wrapper to give your TypeScript agent a sandbox.
Modal: Decorator-Driven Python
Modal's SDK uses Python decorators. You write @app.function(gpu="A100") on a regular Python function and Modal handles containerization, deployment, scaling, and GPU provisioning. The local development experience is strong: modal serve gives you a hot-reloading development server. modal deploy ships to production. No Dockerfiles, no Kubernetes, no infrastructure config.
The Python-only constraint is Modal's main DX limitation. If your stack is TypeScript, you either wrap Modal calls in HTTP endpoints or use a different platform. E2B's TypeScript SDK has no equivalent for Modal's Python-decorator workflow.
Pricing
Both platforms use per-second billing. The cost structures differ because the underlying resources differ.
| Item | E2B | Modal |
|---|---|---|
| Billing model | Per sandbox-second | Per container-second |
| Free tier | 100 sandbox hrs/month | $30 credits/month |
| CPU compute | Included in sandbox pricing | ~$0.000018/core-second |
| GPU compute | Not available | A10G ~$1.10/hr, A100 ~$5.92/hr, H100 ~$10/hr |
| Paid plans | From $45/month + usage | Pay-as-you-go (no minimum) |
| Idle cost | Zero (sandboxes are ephemeral) | Zero (containers scale to zero) |
| Storage | Included per sandbox | Volumes: ~$0.63/GiB-month |
Direct price comparison is not meaningful because the workloads are different. Running an agent code execution sandbox on E2B costs a few cents per session. Running GPU inference on Modal costs dollars per hour of GPU time. The question is not which is cheaper but which resource you need.
Cold Start and Performance
Cold start matters differently for each platform. For E2B, every agent tool call might spin up a new sandbox, so sub-second startup is critical for keeping agent loops responsive. For Modal, cold starts affect the latency of the first request to a scaled-to-zero function.
E2B achieves sub-200ms cold starts because Firecracker microVMs are designed for exactly this. They boot a minimal Linux kernel with a pre-built filesystem snapshot. Custom templates with pre-installed packages add some overhead but typically stay under 500ms.
Modal's cold start depends on image size. A minimal Python image starts in ~300ms. A large ML image with PyTorch and model weights can take 1-2 seconds. Modal mitigates this with container keep-alive (warm containers respond in ~5ms) and predictive pre-warming. For long-running inference jobs, cold start is amortized over minutes or hours of compute and barely matters.
When E2B Wins
AI Agent Sandboxing
The core use case E2B was built for. Give an LLM a sandbox, let it execute arbitrary code, tear it down. Firecracker isolation means LLM-generated code cannot escape. No other platform provides this level of agent-specific sandboxing with sub-200ms cold starts.
Code Interpreters
Building a ChatGPT-style code interpreter? E2B's sandbox.run_code() handles Python, JavaScript, R, and any language you install. Persistent filesystem means the agent can write a file in step 1 and read it in step 5. Built-in Jupyter kernel support for notebook-style execution.
Multi-Tenant Agent Platforms
If you are building a platform where multiple users run AI agents simultaneously, each agent needs its own isolated environment. E2B's microVM isolation guarantees that User A's agent cannot read User B's files or processes. Container isolation does not provide the same guarantee against determined adversaries.
TypeScript Agent Stacks
E2B's TypeScript SDK is a first-class citizen, not a wrapper. If your agent framework runs in Node.js (Vercel AI SDK, LangGraph.js, custom TypeScript agents), E2B integrates natively. Modal requires Python.
When Modal Wins
GPU Compute and ML Pipelines
Modal gives you A10G, A100, H100, and L40S GPUs with a Python decorator. No CUDA driver management, no Kubernetes, no cloud console. Fine-tune a model, run batch inference on 100K inputs, or serve a real-time prediction endpoint. E2B has no GPU support at all.
Production Web Services
Modal can serve ASGI/WSGI web applications with auto-scaling, custom domains, and health checks. Deploy a FastAPI service that scales from zero to hundreds of containers based on traffic. E2B sandboxes are ephemeral compute, not web servers.
Batch Processing at Scale
Process millions of items in parallel with Modal's map() primitive. Each item gets its own container. Modal handles the scheduling, retries, and resource allocation. Built-in support for distributed queues, priority scheduling, and progress tracking.
Developer Experience for Python Teams
Modal's decorator-based SDK is the simplest path from local Python function to cloud deployment. No Dockerfiles, no YAML, no infrastructure config. Hot-reloading dev server, integrated secrets management, environment snapshots. If your team writes Python and needs elastic compute, Modal's DX is hard to beat.
The Third Option: Morph
E2B gives agents a sandbox. Modal gives developers serverless compute. Morph approaches the problem from the agent tooling layer: what capabilities does a coding agent need beyond raw execution?
Morph provides three primitives that sit alongside sandbox and compute platforms:
Sandbox SDK
Cloud sandboxes for agent code execution with persistent filesystems and sub-second startup. Similar to E2B's approach but integrated with Morph's apply and search capabilities for end-to-end agent workflows.
Fast Apply
Morph's apply engine takes LLM-generated diffs and applies them to files at 10,500 tok/s, 5x faster than re-generating full files. Purpose-built for the edit step in coding agent loops where the bottleneck is turning LLM output into actual file changes.
WarpGrep Search
Agentic code search via MCP. 8 parallel tool calls per turn across 4 turns, delivering precise code context in under 6 seconds. Reduces the 60% of agent time typically spent searching to a fraction, leaving more context budget for reasoning and editing.
If you are building a coding agent, the stack is: Modal or cloud GPUs for inference, E2B or Morph Sandbox for execution, and Morph Apply + WarpGrep for the edit-and-search loop that connects them. The tools are complementary, not competing.
Frequently Asked Questions
Is E2B or Modal better for AI agents?
E2B is purpose-built for AI agent sandboxing with Firecracker microVMs, sub-200ms cold starts, and an SDK designed for LLM tool-use patterns. Modal is general serverless compute optimized for GPU workloads. If your agent needs to execute arbitrary code safely, E2B. If your agent needs GPU inference, Modal. Many production agent architectures use both.
Can I use E2B and Modal together?
Yes. A common pattern: Modal runs model inference (GPU-heavy), and E2B provides the sandboxed execution environment where the agent runs generated code (CPU, untrusted). They solve different layers of the stack. Your agent framework orchestrates both.
How much does E2B cost?
E2B's free tier includes 100 sandbox hours per month. Paid plans start at $45/month with per-second billing on additional usage. A typical agent session (create sandbox, run some code, tear down) costs a few cents. There are no GPU costs because E2B sandboxes are CPU-only.
How much does Modal cost?
Modal charges per second with no minimum commitment. CPU compute starts around $0.000018/core-second. GPU pricing: A10G ~$1.10/hr, A100 ~$5.92/hr, H100 ~$10/hr. New accounts get $30 in free monthly credits. Containers scale to zero, so you only pay for active compute.
What is E2B's cold start time?
Sub-200ms for standard sandboxes. Custom templates with pre-installed dependencies typically stay under 500ms. This is critical for agent workflows where each tool call might create a fresh sandbox. Firecracker microVMs are specifically designed for fast boot.
Does Modal support Firecracker microVMs?
No. Modal uses gVisor-sandboxed OCI containers. Containers share the host kernel (through gVisor's application kernel), which enables GPU passthrough but provides weaker isolation than a full VM boundary. For running your own trusted code, this is fine. For running untrusted LLM-generated code, microVM isolation is the safer choice.
Can E2B run GPU workloads?
No. E2B sandboxes are CPU-only Firecracker microVMs. Firecracker does not support GPU passthrough. If your agent needs GPU compute (for inference, training, or image generation), use Modal, Replicate, or a cloud GPU provider alongside E2B for the code execution layer.
What languages do E2B and Modal support?
E2B sandboxes are full Linux microVMs, so any language that runs on Linux works. The SDK has first-class support for Python and TypeScript/JavaScript. Modal is Python-native: you decorate Python functions with @app.function() and Modal handles deployment. Non-Python workloads on Modal require wrapping in a Python entry point or using custom container images.
Related Comparisons
Try Morph Sandbox SDK
Cloud sandboxes for AI agents with fast apply and agentic code search. Give your agent a safe execution environment, precise file editing at 10,500 tok/s, and code search that cuts context-gathering from minutes to seconds.