E2B vs Daytona vs Blaxel: AI Agent Code Execution Sandbox Comparison 2026

On April 15, 2026, OpenAI shipped Agents SDK v2 with seven native sandbox providers baked directly into the framework — Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, and Vercel. That single release turned sandbox selection from a niche infrastructure decision into a routine engineering choice that every AI agent team now has to make. Three providers dominate early conversations: E2B, Daytona, and Blaxel. Each is production-ready, each has native SDK integration, and each is built around a fundamentally different runtime model. This article breaks down exactly where they diverge, which workload profile each one is optimized for, and how to pick the right one without running a month of expensive benchmarks.

E2B vs Daytona vs Blaxel 2026: The AI Agent Sandbox Comparison

The AI agent sandbox market now has more viable options than at any point in its short history, and the differences between providers matter more than they did two years ago. In 2026, more teams ran production autonomous coding agents in a single quarter than in all prior years combined, and the infrastructure those agents run on directly determines cost, latency, and reliability at scale. The three providers shaping this conversation — E2B, Daytona, and Blaxel — share a common premise (isolated, reproducible execution environments for agents) but differ sharply on architecture. E2B is built on Firecracker microVMs for lightweight stateless execution. Daytona uses pre-warmed Docker snapshots tuned for developer workflows and per-second billing. Blaxel maintains persistent environment state enabling a 25ms resume time for paused agents. These are not marketing variations on the same underlying approach — they are genuinely different architectural bets on what agentic workloads look like at scale. Understanding those bets is the fastest path to a defensible infrastructure decision.

E2B: Firecracker-Powered Lightweight Sandboxes with $100 Free Credits

E2B gives every new account $100 in free credits — enough compute to run thousands of sandbox sessions at evaluation scale without a credit card. That alone makes it the de facto first sandbox most agent developers reach for, and the sub-second cold start time (typically under one second for a fresh Firecracker microVM) means the onboarding experience is immediately satisfying. E2B is built on Firecracker, the open-source microVM technology developed by AWS for Lambda and Fargate. Firecracker boots a minimal Linux kernel in milliseconds, provides hardware-level isolation without the overhead of a full VM, and tears down equally fast. For agent workloads, this maps directly to ephemeral code execution: an agent calls a tool, E2B spins up a sandbox, the tool runs, the result is returned, and the sandbox is discarded. No state accumulates between calls unless you explicitly manage it. The developer API is one of the cleanest in the market — Python and TypeScript SDKs with intuitive Sandbox objects, streaming stdout/stderr, file upload/download, and port exposure for running web services. E2B’s documentation is thorough, the community is active, and the OpenAI Agents SDK v2 integration is first-class. For teams starting their agentic sandbox journey, E2B’s combination of free credits, developer ergonomics, and sub-second startup makes it the lowest-risk entry point.

from e2b_code_interpreter import Sandbox

with Sandbox() as sandbox:
    execution = sandbox.run_code("import pandas as pd; print(pd.__version__)")
    print(execution.text)

The stateless model means each sandbox run is independent by default. This is a feature for short-lived tasks and a limitation for long-running workflows that need persistent context. E2B does support longer-running sandboxes with timeouts up to 24 hours, but the underlying model is still closer to a function invocation than a persistent environment. For workloads that fit the ephemeral model — code interpretation, data analysis, single-session agent tasks — E2B is hard to beat on simplicity and cost efficiency.

Daytona: Developer-First Sandboxes with Integrated Workflows

Daytona’s headline number in 2026 is sub-90ms cold starts, with optimized production configurations reaching as low as 27ms — a meaningful improvement over E2B’s sub-second baseline for latency-critical agent orchestration loops. Founded by the team that built Codeanywhere and backed by a $24M Series A in February 2026, Daytona approaches sandboxes through a developer-environment lens rather than a pure compute lens. Where E2B asks you to think of sandboxes as function invocations, Daytona asks you to think of them as persistent development environments with a full lifecycle: Running, Stopped, Archived, Deleted. Sandboxes auto-stop after 15 minutes of inactivity, auto-archive after seven days, and can be forked into parallel branches — all behaviors that map naturally to how developer workflows actually operate. The per-second billing model is a meaningful differentiator for teams with bursty or unpredictable workloads: you pay only for active compute seconds, not for minimum session durations or idle time. Daytona publishes SDKs for Python, TypeScript, Ruby, and Go, with LangChain packages on both npm and PyPI, and native Language Server Protocol support inside sandboxes enables code analysis (completions, diagnostics, go-to-definition) without additional infrastructure. For teams running coding agents that need to understand code structure — not just execute it — that LSP integration eliminates a class of tooling problems. Daytona also supports forking sandbox state into parallel branches, enabling multi-agent exploration patterns where different reasoning paths need isolated but identically-initialized environments.

from daytona_sdk import Daytona

daytona = Daytona()  # reads DAYTONA_API_KEY from env
sandbox = daytona.create()
response = sandbox.process.code_run("print('hello from daytona')")
print(response.result)
daytona.remove(sandbox)

The developer experience is notably strong for teams managing multiple concurrent environments. Daytona’s sandbox lifecycle management, auto-cleanup behavior, and per-second billing combine into a model that works well for mid-length agent sessions — longer than a single function call but shorter than the multi-day workflows where Blaxel’s persistence advantages become decisive.

Blaxel: Persistent Sandboxes with 25ms Resume for Long-Running Agents

Blaxel’s defining capability is a 25ms resume time for paused environments — fast enough that pause-resume cycles in an agent orchestration loop become operationally invisible. That number represents something most sandbox providers do not offer: genuinely persistent environment state. With E2B or a basic container platform, resuming a “paused” agent typically means cold-booting a new sandbox and reconstructing context from an external store. With Blaxel, the environment itself holds the state — filesystem contents, in-memory data, running processes, and network configuration all persist between sessions. When an orchestrator resumes an agent, it picks up in under 30ms from exactly the state it left in. This architecture was built for a specific workload profile that is increasingly common in 2026: autonomous agents that operate in phases, check in for human review between phases, and need to resume into a deterministic environment without re-running expensive setup steps. Blaxel became one of the seven native providers in the OpenAI Agents SDK v2 on April 15, 2026, which eliminated the custom adapter work previously required to route SDK-managed agents to Blaxel sandboxes. For enterprise teams running production autonomous coding agents — multi-phase refactoring pipelines, multi-day research agents, browser automation with authenticated session state — Blaxel’s 25ms resume and native state persistence remove an entire category of infrastructure complexity that stateless sandbox users build manually. The platform exposes REST APIs and Python/TypeScript SDKs following the same ergonomic patterns as E2B and Daytona, making the migration surface manageable for teams switching from a stateless model.

# Blaxel sandbox with persistent state
import blaxel

env = blaxel.Environment.get("my-agent-env")
env.resume()  # 25ms resume from last checkpoint
result = env.run("python agent_phase_2.py")
env.pause()   # checkpoint state before waiting for review

The persistence model requires a mental shift: design your agent to treat the Blaxel environment like a container that will still be running when you come back, not like a clean-room execution context that gets wiped on each invocation. For teams whose agents genuinely need that persistence, Blaxel removes the need to build external state serialization infrastructure. For teams whose agents complete in a single session, Blaxel’s architectural advantages are irrelevant and the cost model is unfavorable compared to E2B or Daytona.

Performance Benchmarks: Cold Start, Throughput, and State Management

Performance comparison across these three providers requires separating two distinct latency concepts: cold start time (how long until a fresh sandbox accepts its first instruction) and resume time (how long until a paused sandbox is back to full operating state). These are measured differently and matter for different workloads. For cold starts, Daytona leads with sub-90ms measured p99 and 27ms in optimized configurations. E2B follows with sub-second cold starts using Firecracker microVMs — typically in the 150–500ms range depending on image complexity. Blaxel’s cold start for a brand-new environment is comparable to E2B, but its resume time of 25ms for paused environments is the fastest in the market for the warm-state scenario. For throughput on stateless workloads — running many independent sandbox sessions concurrently — all three providers scale horizontally without hard limits at the session count levels most teams operate at in 2026. E2B’s Firecracker architecture is particularly efficient here: microVMs have a smaller memory footprint than full containers, so E2B can pack more concurrent sandboxes onto the same underlying hardware. For state management overhead, the difference is stark. E2B and Daytona require external state stores (S3, Redis, a database) if you want context to persist across sandbox sessions. Blaxel’s persistence is native: no external store required, no serialization code to write, no deserialization logic to maintain. For workloads with complex state — large file trees, running databases, accumulated in-memory data — the engineering cost of external state management with E2B or Daytona is non-trivial and often underestimated at the outset of a project.

One performance dimension that benchmarks rarely surface is compound latency across multi-step agent workflows. A 15-tool-call agent loop with 500ms cold starts per call accumulates 7.5 seconds of pure infrastructure latency. The same loop with 27ms Daytona cold starts adds under half a second total. For production agents running thousands of sessions per day, this differential compounds into meaningful cost and response-time differences.

Pricing Comparison: Free Credits vs Per-Second vs Subscription

Pricing structures across the three providers reflect their different architectural models and target customers. E2B offers the most developer-friendly entry point: $100 in free credits for new accounts, with no credit card required to start evaluating. The free credits cover meaningful experimentation — thousands of sandbox-minutes at typical task lengths — and the pay-per-use model that follows is straightforward to reason about at scale. E2B charges by compute time, with pricing that varies based on sandbox size (CPU and memory configuration). For cost-sensitive workloads where sandboxes run for seconds to minutes per session and traffic is predictable, E2B’s per-usage model is easy to budget. Daytona’s per-second billing is the strongest fit for bursty workloads with irregular session lengths. Per-second billing eliminates the over-payment problem that minute-minimum billing creates: a 3-second sandbox session costs 3 seconds of compute, not 60. For teams running many short-lived sandboxes with unpredictable runtimes, the cost efficiency advantage over minimum-increment competitors is real and compounds at scale. Daytona also offers a free tier, making it accessible for evaluation without the upfront $100 commitment of E2B. Blaxel’s pricing is the least transparent of the three as of May 2026, reflecting the enterprise focus of its product positioning. Blaxel charges based on active persistent environments, compute resources allocated per environment, and resume frequency — a model that rewards teams whose agents actually use the persistence features. For teams with hundreds of concurrent long-running agents that pause and resume frequently, Blaxel’s infrastructure cost can be lower than repeatedly cold-starting equivalent state with E2B, but this calculation requires workload-specific benchmarking to validate. Teams evaluating Blaxel should request pricing directly and run a parallel cost benchmark using E2B’s $100 free credit as a baseline.

When to Use Each Sandbox Provider

The choice between E2B, Daytona, and Blaxel reduces to three questions: How long do your agents run? How much state do they accumulate? How frequently do they start fresh versus resume? If your agents complete work in a single session — a coding task, a data analysis job, a document review — and discard their environment afterward, E2B is the correct default. The $100 free credit, developer-friendly API, and sub-second cold starts make it the easiest sandbox to adopt, and the stateless model is not a limitation for workloads that have no state to preserve. E2B is also the right choice when cost predictability matters more than latency, and for teams building multi-tenant systems where tenant isolation at the VM level is a security requirement. Choose Daytona when your agents run in development-environment-style workflows: code environments that need to persist across tool calls within a session, teams managing many parallel sandboxes simultaneously, or workloads with bursty patterns where per-second billing delivers better unit economics than per-minute or per-session minimums. Daytona’s LSP integration also makes it the better choice for coding agents that need semantic code understanding rather than just execution. If you are building agents that clone repositories, make changes across multiple files, run test suites, and iterate — all within a single session — Daytona’s sandbox lifecycle model is more natural than E2B’s ephemeral approach. Choose Blaxel when your agents genuinely pause and resume across multiple sessions, when reconstructing state from scratch is expensive enough to matter, and when you want to eliminate custom state serialization infrastructure. The canonical Blaxel workload is a multi-phase autonomous agent: phase one completes and pauses for human review, phase two resumes into the exact same environment hours or days later. Without Blaxel, teams build serialization/deserialization logic, checkpoint storage, and restore validation. With Blaxel, the environment persists and the 25ms resume eliminates the cold-boot penalty entirely.

Summary decision matrix:

• Stateless, short-lived agent tasks → E2B
• Dev-environment workflows, per-second billing, semantic code tooling → Daytona
• Long-running agents with pause/resume cycles and complex state → Blaxel

Getting Started: OpenAI Agents SDK v2 Sandbox Integration

The OpenAI Agents SDK v2, released April 15, 2026, is the fastest path to integrating any of the three providers, because it handles sandbox lifecycle management (provisioning, health checks, cleanup, retry logic) automatically through its native provider adapters. Selecting a sandbox provider is a configuration decision rather than an architecture decision when you start from the SDK. All three providers — E2B, Daytona, and Blaxel — require an API key from their respective platforms and a one-line configuration change in the SDK to activate. Here is the pattern for E2B, which works identically for Daytona and Blaxel by substituting the provider name:

from openai_agents import Agent, SandboxConfig

# E2B configuration
agent = Agent(
    model="gpt-5",
    sandbox=SandboxConfig(provider="e2b", api_key="e2b_your_key_here"),
    tools=[code_interpreter],
)

# Daytona — swap provider name, same pattern
agent = Agent(
    model="gpt-5",
    sandbox=SandboxConfig(provider="daytona", api_key="your_daytona_key"),
    tools=[code_interpreter],
)

# Blaxel — persistent sandboxes via same interface
agent = Agent(
    model="gpt-5",
    sandbox=SandboxConfig(
        provider="blaxel",
        api_key="your_blaxel_key",
        persistent=True,
        environment_id="my-agent-env",
    ),
    tools=[code_interpreter],
)

For teams not using the OpenAI Agents SDK v2, each provider exposes a standalone Python and TypeScript SDK with similar ergonomics. Start with E2B if you have no existing sandbox infrastructure — the $100 free credit covers a full evaluation cycle, the documentation is the most complete of the three, and switching to Daytona or Blaxel later is a contained migration. If you already know your agents will be long-running with frequent checkpoints, start with Blaxel and avoid building state serialization infrastructure you will have to remove later. For Daytona, the free tier and per-second billing make it easy to run a parallel benchmark against E2B before committing to either. The SDK’s provider abstraction means you can benchmark all three on representative workloads by changing a single line of configuration, then pick the provider whose performance and pricing fit your production requirements.

FAQ

Q: Can I use E2B, Daytona, and Blaxel with the same agent code?

Yes. The OpenAI Agents SDK v2’s SandboxConfig abstraction treats all seven native providers through the same interface. Switching between E2B, Daytona, and Blaxel is a configuration change — you substitute the provider name and API key without touching agent logic. For teams using provider-specific SDKs directly (not through the OpenAI Agents SDK), the APIs differ in method naming and lifecycle model, but the core operations (create, run, read output, destroy) are present on all three platforms with comparable ergonomics.

Q: Is E2B’s $100 free credit enough to evaluate it properly?

Yes, for most teams. At E2B’s typical per-compute-unit pricing, $100 covers thousands of sandbox-minutes — sufficient to run representative agent workloads, measure cold start latency at your task complexity level, and estimate monthly production costs with reasonable accuracy. The credit requires no credit card to claim, which removes the friction of expensing an evaluation. For unusually large-scale evaluations (millions of short sandbox sessions or very long-running tasks), you may exhaust the credit faster, but standard evaluation use cases are well within the $100 ceiling.

Q: What happens to Blaxel state if my process crashes mid-run?

Blaxel’s persistent environment model checkpoints at the pause point — state that accumulated during an active session before a crash may not be fully preserved unless your agent explicitly checkpoints (pauses) at logical intervals. Blaxel recommends treating explicit pause() calls as checkpoints analogous to database commits: call them at the end of every completed logical phase, not just at the end of a full workflow. If a process crashes during an active phase, Blaxel can recover the last paused state, but work done after the last pause may be lost. This is a different failure model than stateless sandboxes (which lose everything on crash) but requires the same discipline around checkpoint placement.

Q: How does Daytona’s per-second billing compare to E2B’s per-usage pricing in practice?

The cost difference depends heavily on session length distribution. For very short sessions (under 10 seconds), Daytona’s per-second billing often delivers better unit economics because it charges only for active seconds without minimum increments. For longer sessions (minutes to hours), the difference narrows and depends on the per-second rates each provider charges for your compute configuration. The most reliable approach is to run a parallel benchmark using Daytona’s free tier and E2B’s $100 credit on your actual workload distribution, then extrapolate to your expected monthly volume. Do not rely on list prices alone — compute configuration (CPU cores, memory) affects the per-second rate significantly, and the configuration that matches your workload may price differently than the baseline example in each provider’s documentation.

Q: Will Blaxel, Daytona, and E2B remain supported in future OpenAI Agents SDK versions?

As of the April 2026 v2 release, all seven native providers — including all three covered in this article — are first-class supported providers in the SDK. OpenAI has not announced any plans to reduce the provider list in upcoming releases, and the modular provider architecture in SDK v2 is designed to add providers rather than remove them. That said, the sandbox infrastructure market is moving quickly: provider consolidation, acquisitions, or shifts in OpenAI’s partnership strategy could affect the provider list on timescales of months to a year. The safest architectural posture is to keep sandbox provider selection isolated behind the SandboxConfig abstraction, which makes switching providers a configuration change rather than a code rewrite if your preferred provider’s status changes.