E2B vs Modal vs Blaxel: sandbox for production AI agents

Your coding agent passed every demo. Then a real user pauses for ten minutes to review a diff. When they return, the sandbox is gone. A fresh one boots, and the repository clones again. Every file the agent wrote is lost. Meanwhile, the meter ran while the agent waited on the model.

That failure lives in the sandbox layer. E2B, Modal, and Blaxel all execute untrusted AI-generated code. They differ in handling idle sessions, preserved state, and outbound networking. Those differences determine whether returning users resume immediately or rebuild their environment. They also shape the invoice at month end.

This comparison covers lifecycle and state, production networking and isolation, and cost and portability. Those criteria matter most for production agents that execute code across multiple user turns.

TL;DR

The shortest comparison comes down to each platform's primary workload:

• E2B vs Blaxel: E2B is the open-source reference point for AI sandboxes. Its Pro tier has a continuous runtime cap and a monthly base fee before compute usage.
• Modal vs Blaxel: Modal is a Python-native serverless platform for GPU and CPU compute. Blaxel focuses on preserving agent state across multi-hour sessions without idle billing.
• Who each fits: E2B suits prototyping and teams wanting self-hosted infrastructure. Modal suits Python-native GPU functions and batch compute. Blaxel suits coding agents first, followed by PR review and data analysis agents in production.

These workload boundaries provide the context for the platform details below.

What is E2B?

E2B runs AI-generated code inside Firecracker microVMs. Its Firecracker architecture gives each sandbox a separate guest kernel. Developers create sandboxes from templates through supported SDKs for Node.js and Python.

Its Code Interpreter runs Python, JavaScript, TypeScript, R, Java, and Bash. The supported languages require no custom templates. E2B publishes its infrastructure code under Apache 2.0. It also offers Bring Your Own Cloud (BYOC) deployments inside customer AWS or Google Cloud accounts.

What is Modal?

Modal is a Python-native serverless platform for GPU and CPU compute. Its Sandboxes product became generally available in January 2025. Modal isolates untrusted code with gVisor by default. This user-space application kernel intercepts system calls. Teams can instead run each Sandbox on a full virtual machine.

Modal's flagship strengths are Python-native GPU functions and large-scale batch compute. These capabilities suit model inference, training jobs, and parallel data processing. Its client SDKs cover Python, plus beta JavaScript and Go support. The SDK documentation explains those maturity levels.

What is Blaxel?

Blaxel is the infrastructure foundation for autonomous agents. It provides the execution layer for AI agents that run code. Its compute primitives include Sandboxes for stateful agent code and Batch Jobs for parallel processing. Storage includes Volumes for durable block storage. Agent Drive, in private preview, provides shared filesystems. Networking includes custom domains, proxy secrets injection, and dedicated egress gateways in private preview. These primitives cover code execution, persistent data, and production networking from one provider.

Sandboxes run as Firecracker microVMs. Closed connections move them into standby with files, memory, and processes intact. Network inactivity triggers that transition after about 15 seconds. Sandboxes can remain in standby indefinitely with no compute charge while idle. They resume in under 25ms with their previous state restored. That speed avoids a visible rebuild when users return. Volumes remain the documented option for guaranteed long-term data retention.

Blaxel is a first-class sandbox provider in the OpenAI Agents SDK. The SDK builds on OpenAI's Codex harness. Blaxel Sandboxes handle the execution layer. Blaxel also provides enterprise compliance options for regulated workloads.

Head-to-head feature comparison

SOC 2 Type II evaluates security controls over time. ISO 27001 covers information security management systems. The Health Insurance Portability and Accountability Act (HIPAA) governs protected health information in the United States.

The isolation row is a tie between Blaxel and E2B. The remaining rows show how much lifecycle and networking work each platform handles.

When Blaxel is the better choice

Blaxel fits agents whose sessions outlive a single request. It also fits teams needing managed preview domains and enterprise compliance. The next scenarios map those requirements to each competitor's differences.

Over E2B: sessions that outlive a timeout, on your own domain

• E2B's sandbox timeout defaults to five minutes. The default timeout action is kill. Persistence requires setting onTimeout to "pause". E2B's persistence caps continuous runtime at 24 hours on Pro. Hobby sessions have a shorter cap.
• Repeated pause and resume cycles have a reported filesystem race condition. Aura traced a production bug to that issue. Its workaround pauses once per large language model turn.
• Blaxel's automatic standby model doesn't require that lifecycle workaround. This difference matters for large repositories. Delty reported that standby restoration replaced repeated repository cloning. Returning users can continue with memory, files, and processes available.
• E2B's custom-domain path requires a customer-operated proxy VM with Caddy and Cloudflare DNS. Blaxel manages custom domains. Its proxy secrets injection keeps raw API keys outside the sandbox. Dedicated egress gateways are in private preview.
• Blaxel also combines Volumes and Agent Drive, in private preview, with its networking layer. Start by measuring how often sessions exceed E2B's default timeout. That figure shows exposure to environment termination.

Over Modal: stateful sessions on hardware-isolated microVMs

• Modal excels when agent workloads become Python-native GPU execution or parallel batch processing. Its Functions scale compute around discrete jobs and model workloads.
• A multi-turn agent sandbox creates a different requirement. It must retain files, memory, and running processes during user absences. Modal's alpha standby retains filesystem and memory snapshots for seven days. The platform deletes those snapshots after that finite window. Blaxel's standby lifecycle preserves complete state indefinitely without active compute allocation.
• Modal Sandboxes use three times standard Function rates for CPU and memory. Regional placement can add further multipliers. Longer scaledown windows reduce cold starts but reserve resources during idle periods.
• Modal uses platform-specific Python decorators. Moving serving logic to another provider therefore requires code changes.
• Modal's default gVisor layer intercepts system calls before they reach the host kernel. That boundary improves isolation over shared-kernel containers. Firecracker microVMs add a hardware-enforced boundary and separate guest kernel. For AI-generated code, this separates each sandbox from the host and neighboring tenants.
• Start by listing agents that retain processes between user turns. Compare those workloads separately from GPU functions and batch jobs.

When E2B may fit better

• E2B fits teams that need greater infrastructure control. Teams can self-host its Apache 2.0 code on Google Cloud or beta AWS infrastructure. BYOC deploys E2B sandboxes inside the customer's virtual private cloud (VPC). Templates, snapshots, and runtime logs remain in that account.
• Blaxel doesn't provide a fully air-gapped installation. Bring Your Own Metal puts sandbox runtimes on customer bare metal. VPC interconnect keeps traffic off the public internet. However, the Blaxel control plane remains managed.
• E2B makes its code available, while most customers use the managed service. Blaxel provides closed-source managed infrastructure. This gap matters for teams that require ownership of the complete deployment stack.
• E2B also fits prototyping. Its Hobby tier includes initial credits, concurrent sandboxes, and no credit-card requirement. The Code Interpreter supports R and Java without custom templates. The Desktop Sandbox gives computer-use agents a Linux desktop.
• These conveniences support create-and-kill development workflows. Teams needing infrastructure ownership or broad interpreter support should include E2B in their evaluation.

When Modal may fit better

• Modal fits teams whose Python workloads already run on Modal Functions. It also excels at Python-native GPU functions and batch processing. Those strengths apply to model inference, training, and parallel compute.
• Sandboxes share the same account, secrets, and Volume allowances. Consolidating on one vendor reduces contracting and account management. The Team plan's high container ceiling also suits workloads already fanning out across Modal Functions.
• Language support is close to a tie. Blaxel's first-class SDKs cover Python, TypeScript, and Go. Other host languages can use its REST API. Modal's Python SDK is generally available. Its JavaScript and Go SDKs remain in beta.
• Modal's Team plan includes longer log retention and substantial concurrency. A HIPAA Business Associate Agreement (BAA) requires its Enterprise plan.
• Teams already using Modal should compare consolidation benefits against multi-turn lifecycle requirements.

Pricing comparison

Every compute figure below uses 1 vCPU and 2 GB of RAM. That matches the size of a Blaxel XS sandbox. Vendor pricing pages were current as of May 2026.

Pricing

• Free: Up to $200 in free credits plus usage costs.
• Pre-configured sandbox tiers and usage-based pricing: See Blaxel's pricing page for the most up-to-date pricing information.
• Available add-ons: Available add-ons include email support, live Slack support, and HIPAA compliance.

This format separates introductory credits from ongoing usage and optional support services.

Exceeding Hobby concurrency on E2B requires the Pro tier. Usage charges then sit above the base subscription. Blaxel instead publishes usage-based tiers without a base subscription. Modal's published Sandbox rates sit above its Function rates. Pinning a region can add another multiplier. The E2B cost calculator provides the comparable compute formula.

The practical comparison depends on active time, idle behavior, concurrency, and regional placement. Model a representative session instead of comparing headline hourly rates alone.

Key differentiators and objection handling

"E2B is the category standard. Why not default to it?" E2B's mindshare is real. Its Firecracker isolation also matches Blaxel's isolation model. The differences appear in lifecycle defaults, managed networking, and compliance coverage.

E2B terminates idle sandboxes unless teams configure pausing. Custom domains and static IPs require customer-operated infrastructure. Blaxel automates standby and manages custom domains. It also provides enterprise compliance options.

For prototypes, those differences may carry little weight. Products with returning users should test lifecycle behavior directly.

"Modal has substantial funding. Isn't Blaxel the vendor risk?" Vendor stability belongs in any infrastructure review. Modal has substantial funding and a broad compute business. Blaxel supports production coding and review workloads.

Customer references provide another validation path. Engineering leaders should request references that match their workload and scale.

The OpenAI Agents SDK provides another technical signal. Its Python package includes BlaxelSandboxClient. TypeScript examples import the same client from @openai/agents-extensions/sandbox/blaxel.

"Won't we be locked in either way?" Every managed execution platform creates some dependency. A Hacker News discussion highlights this broader platform-lock-in concern. It applies across all three providers.

Blaxel reduces image-level dependency by building sandbox images from standard Dockerfiles. The same image definition can run locally. Blaxel then converts it into a microVM filesystem. Modal's decorator model creates deeper serving-logic dependencies. E2B's open-source infrastructure provides the broadest ownership option.

How to run your E2B vs Modal vs Blaxel evaluation this week

A useful evaluation should reproduce the lifecycle of a real production agent. Use the same workload, image, state, and timing across all three providers.

1. Choose a representative agent

Pick one production agent with meaningful local state. A coding agent with a large repository works well. A data analysis agent with a loaded dataset also exposes lifecycle differences. Define what must survive between turns. Include modified files, in-memory objects, and background processes. Also record required environment variables and open services.

Record the initial creation time and setup work. This baseline shows the cost of rebuilding the environment. Document networking and security requirements separately. Note any custom domain, static outbound address, or injected-secret requirements. Record compliance requirements before testing. Otherwise, a fast prototype can hide production blockers.

Choose a workload with realistic pauses between user turns. Don't test only continuous execution. Record how often users return after the default timeout. Measure the setup each repository or dataset rebuild requires. These observations connect lifecycle behavior to delays and engineering cost.

2. Standardize the sandbox image

Use the same image and dependency versions on all three platforms. Keep CPU, memory, repository size, and test commands consistent. Different images make timing results difficult to compare. Run one complete user interaction before measuring idle behavior. Confirm that each agent writes identical files and starts equivalent processes.

Capture compute usage during active execution. For Modal, record the selected region and scaledown settings. Both choices can affect the bill. Standardization separates platform behavior from application differences. It also exposes portability work across SDKs, decorators, networking, and image configuration.

Store the exact build instructions and dependency lockfiles with the results. Repeat setup from a clean environment. This check identifies distortion from cached dependencies or prebuilt images. Record platform-specific decorators or lifecycle calls as separate implementation work. Don't fold that effort into runtime performance.

3. Measure idle resume and state

Let each session idle for an hour. Reconnect and measure the time until useful work can continue. Check every file, process, and in-memory object that should remain available. Repeat the cycle several times. E2B's reported filesystem issue appears after repeated pause and resume operations.

For Blaxel, verify that automatic standby requires no lifecycle call. For Modal, evaluate the seven-day alpha retention window. Also compare the idle-resource tradeoff from your selected scaledown setting. Record failures as user-visible outcomes. Examples include fresh clones, restarted processes, missing files, or unavailable preview URLs.

These outcomes matter more than an isolated startup benchmark. Run the checks after both short and long pauses. Timeout behavior can change across plan boundaries. Log whether reconnection restores the existing environment or creates a replacement. Verify background services by sending a real request. Don't rely only on a process listing.

4. Compare state, networking, and cost

Calculate active compute, idle charges, storage, base subscriptions, and regional multipliers. Then add engineering work for proxies, domains, and lifecycle code. Include migration-specific serving logic as a separate cost.

Review the results against the original requirements. Modal may lead when GPU functions or batch compute dominate. E2B may lead when infrastructure ownership matters most. Blaxel may lead when stateful sessions and managed networking drive the workload. Its automatic standby also reduces lifecycle management.

Run this test before negotiating annual terms. A small production-shaped benchmark reveals more than feature checklists or headline rates. Present the final comparison as total session cost. Include active minutes, idle duration, rebuild frequency, and required engineering tasks. This format exposes subscriptions, idle reservations, and maintenance work outside each platform.

FAQs

Do E2B, Modal, and Blaxel all use microVMs?

No. E2B and Blaxel run each sandbox inside a Firecracker microVM. Each environment has its own guest kernel. A compromise must cross a hardware-enforced boundary before reaching the host.

Modal's standard Sandboxes use gVisor. This user-space application kernel intercepts system calls before they reach the host kernel. It improves isolation over containers but remains a software boundary. Modal also has a VM Sandbox in alpha for existing platform users.

The difference matters when agents execute arbitrary or AI-generated code. A separate guest kernel strengthens isolation from the host and neighboring tenants. Test the default mode and any optional VM configuration planned for production. Separate this security decision from lifecycle behavior. Similar isolation models can still handle standby and state differently.

How long can a sandbox stay idle on each platform?

On E2B, a paused sandbox remains available until the client calls sandbox.kill(). Teams must configure pausing first. The default timeout action terminates the environment.

Modal's alpha standby preserves filesystem and memory snapshots for seven days. It deletes those snapshots after that window. Modal also manages idle capacity through serverless lifecycle and scaledown settings. Extending the scaledown window reduces cold starts but increases reserved-resource costs. Teams should tune that window around their workload.

On Blaxel, a sandbox can remain in standby indefinitely. Volumes remain the documented option for data requiring guaranteed long-term durability. Test idle duration with the exact plan and production configuration. Check files, memory, processes, and preview services after reconnecting. An addressable sandbox without application state still forces rebuild work.

Which platform costs least for agents that idle most of the time?

E2B and Blaxel have similar active compute costs at the comparison configuration. Idle behavior determines the larger difference. E2B stops billing while paused if the application configures pausing. Blaxel stops compute billing automatically when connections close. Snapshot storage still applies.

The Build0 case study reported a 70–80% infrastructure cost reduction. Build0 moved from CodeSandbox and Vercel. Modal's cost depends on Sandbox rates, region selection, and scaledown settings.

Your ratio depends on active execution and waiting time between commands. Calculate a representative session using active minutes and idle duration. Add storage, concurrency, base subscriptions, and regional multipliers. Include cloning or dataset reloads when termination forces a rebuild. The least expensive provider depends on the complete session pattern.

Does Blaxel work with the OpenAI Agents SDK?

Yes. The OpenAI Agents SDK treats Blaxel as a first-class sandbox provider. E2B support is also available through the SDK. The Python package installs as openai-agents[blaxel]. The JavaScript client lives at @openai/agents-extensions/sandbox/blaxel.

The SDK builds on OpenAI's Codex harness. Blaxel Sandboxes provide the execution layer beneath it. The Python documentation also covers cloud bucket mounts for S3, R2, and Google Cloud Storage. Agent Drive, in private preview, provides shared storage for sandboxes and agent sessions.

During evaluation, confirm that the client can create a sandbox and execute commands. Test whether it preserves state across a user pause. Then reconnect without rebuilding the environment. Use the same repository, environment variables, background processes, and storage mounts as production. This validates the integration path and underlying lifecycle together.