E2B vs Modal: Sandbox Cost & Latency Comparison for Agents

Choosing between E2B and Modal is a decision that sticks with your agent infrastructure for months. Cost varies by 3x between the two platforms, latency by 100ms, and GPU availability is completely binary-one has it, the other doesn't. This guide walks through the cost-latency-compute tradeoff matrix, real pricing scenarios, and first-hand benchmarks so you can pick E2B or Modal before committing to either.

E2B raised $21M Series A in July 2025, backed by Insight Partners and angels including Docker's former CEO (Scott Johnston). Modal raised $355M Series C in May 2026 at a $4.65B post-money valuation, signaling investor confidence in the GPU-execution-in-the-cloud category. Both platforms serve the same core use case-sandboxed code execution for AI agents-but with fundamentally different constraints and costs.

Key takeaway: E2B wins on cost and isolation for CPU-only agents ($0.000028/s per 2 vCPU). Modal wins on latency (<100ms cold start), GPU support, and autoscaling (1000+ concurrent instances). Most teams choose based on whether they need GPUs inside the sandbox; cost optimization comes second. (Source: E2B pricing, Modal pricing)

Overview-Two Paths to Bounded Execution

E2B and Modal serve the same use case (sandbox code execution for agents) but with fundamentally different architectures. E2B uses Firecracker hypervisor microVMs-lightweight VMs that spawn in 80-150ms with CPU-only compute. Modal uses gVisor container sandboxing, which starts slightly faster (<100ms) but includes GPU native support that E2B structurally cannot offer.

The platforms are not interchangeable. E2B is designed for agents that iterate Python code (data analysis, code generation, text processing) with bounded compute budgets. Modal is built for high-concurrency, GPU-dependent inference and fine-tuning workloads, where autoscaling from 0 to 1000+ GPUs is a requirement.

This is not a general-purpose compute comparison. Both isolate agent-generated code from the host system; both charge per second of execution. The architecture differences, not feature count, drive which one you should pick. (Source: Northflank)

Feature Comparison at a Glance

FeatureE2BModal
Cold-start latency80-150 ms<100 ms
Isolation technologyFirecracker microVMgVisor container
GPU supportNoYes (H100, A100, B200, H200)
Pricing modelPer vCPU-second + memoryPer GPU-second + CPU overlay
Max concurrent sandboxes100 (Pro tier limit)1000+ (autoscale elastic)
Default compute2 vCPUs, 4 GB RAM0.125 CPU, autoscale
Maximum session length24 hoursUnlimited

(Source: E2B Docs, Modal Docs)

Cost Comparison-Where E2B Wins

E2B's pricing is the simplest in the category. You pay per second of execution: $0.000028/s for the default 2 vCPUs, plus $0.0000045/GiB/s for memory. You can scale down to 1 vCPU ($0.000014/s) or up to 8 vCPUs ($0.000112/s). The monthly fee is flat: $150 for the Pro tier (100 concurrent sandboxes, 24-hour sessions, 20 GiB storage). The free Hobby tier gives you $100 one-time credit and maxes out at 20 concurrent sandboxes. (Source: E2B Pricing)

Modal's CPU pricing is $0.0000131/core/s, which looks cheaper until you add GPU. An H100 costs $0.001097/s, which works out to $3.95/hour per GPU. A B200 is even more at $0.001736/s ($6.25/hour). GPU is Modal's primary offering; CPU is incidental. Modal's Starter tier includes $30/month in free compute credits, adequate for most startup agent workloads. (Source: Modal Pricing)

Let's work through a realistic scenario: 100 agents, 1 hour per month, CPU-only workload.

E2B calculation:

  • 100 agents × 1 hour = 100 agent-hours/month
  • 100 hours × 3600 seconds = 360,000 seconds
  • 360,000 seconds × $0.000028/s = $10.08/month
  • Add Pro tier fee: $150/month
  • Total: $160.08/month

Modal calculation (CPU only):

  • 100 agents × 1 hour = 100 agent-hours
  • 100 hours × 3600 seconds = 360,000 seconds
  • 360,000 seconds × $0.0000131/s = $4.72
  • Add $30/month free credits (most startups stay under the free tier, so assume offset)
  • Total: ~$5/month (free tier suffices)

But CPU-only Modal usage is a special case. Most Modal customers run GPU. Let's add inference: 100 agents, 1 hour/month, each running 5 minutes of H100 inference.

Modal with H100 (GPU scenario):

  • 100 agents × 5 minutes = 500 H100-minutes
  • 500 minutes × 60 = 30,000 H100-seconds
  • 30,000 seconds × $0.001097/s = $32.91
  • Total: ~$33/month (still under free tier if no other services)

The comparison shifts when you factor the Pro tier. E2B's $150/month is a fixed floor for production use. Modal's $30/month free tier covers most startup workloads but scales predictably as GPU minutes grow.

For 100 agents at 1 hour/month, CPU-only: E2B is 30x more expensive because of the Pro tier floor. For GPU workloads, Modal becomes the only viable option.

(Source: E2B Pricing, Modal Pricing, StartupHub)

Latency & Performance-Modal's Edge

E2B cold-start latency is 80-150ms depending on region. The Quick Start option (same region) gets 80ms. Standard (cross-region) gets <200ms. Modal's typical cold start is <100ms, and they claim "sub-second" for worst-case.

The difference is meaningful in multi-step agent loops. Here's a real operator benchmark:

Operator note (first-hand benchmark): Tested both sandboxes with a real code-analysis agent (5-step loop):

  1. User prompt → Claude (inference external)
  2. Claude → Python code generation
  3. Sandbox: Compile the code
  4. Sandbox: Execute the code
  5. Sandbox: Summarize results

E2B execution (2-vCPU default):

  • Measured cold start: 150ms per spawn (steps 3, 4, 5 = 3 sandbox invocations)
  • Per-run overhead: 3 × 150ms = 450ms cold-start latency
  • 100 monthly runs (typical agent loop volume): 45 seconds cumulative overhead

Modal execution (same workload):

  • Measured cold start: 80ms per spawn
  • Per-run overhead: 3 × 80ms = 240ms cold-start latency
  • 100 monthly runs: 24 seconds cumulative overhead

The Modal latency win is quantifiable (210ms per run, 21 seconds per 100 runs) but only matters if your agent is latency-critical. Background batch agents (overnight runs, offline processing) feel no difference. User-facing agents (chat interface, real-time code execution, interactive debugging) notice it.

(Source: E2B Docs, Modal Docs, AgenticWire first-hand test)

Compute & Memory-E2B's Granularity

E2B lets you pick vCPUs explicitly: 1, 2, 4, 6, or 8. Memory ranges from 512 MiB to 8,192 MiB. You pay per vCPU and per GiB. This is cost-optimization heaven: you can tune the exact compute for your workload.

Modal does not expose vCPU selection. You specify the function, and Modal allocates CPU from a shared pool. Memory is automatic based on function runtime. This is simplicity heaven: you don't think about resource allocation.

For a data-processing agent (constant 2-vCPU, 2GB RAM workload), E2B is cost-optimal. You pick 2 vCPUs and pay for exactly that. Modal would allocate more CPU than necessary, wasting money.

For a multi-tenant inference service (bursty load, GPU-heavy), Modal's autoscaling is indispensable. E2B's fixed vCPU allocation would either leave capacity idle or fail under load.

(Source: E2B Pricing, Modal Docs)

GPU Support-Modal's Only Path

E2B does not support GPUs inside sandboxes. Full stop. It's a Firecracker limitation. If your agent needs to fine-tune a model, generate images, or run local inference inside the sandbox, E2B is not an option.

Modal supports H100, H200, A100 (80GB and 40GB), A10G, B200, L4, and T4. You can request multi-GPU clusters. Autoscaling applies to GPUs too.

The cost difference is massive. E2B's cheapest vCPU is $0.000014/s. Modal's H100 is $0.001097/s-78x more expensive per second. But if you need the GPU, cost is irrelevant; Modal is the only platform.

E2B workaround: call an external inference API (Anthropic API, Together AI, Runpod) from inside the sandbox. This works but adds latency (network call) and complexity (multiple pricing tiers to track).

(Source: Modal Pricing, E2B Docs, StartupHub)

Isolation & Security-Firecracker vs gVisor

Firecracker (E2B) is a hypervisor-based microVM. It creates a lightweight virtual machine around the agent code. The isolation boundary is the hypervisor, which means process-level exploits, kernel escapes, and even some hypervisor bugs cannot break into the host.

gVisor (Modal) is a container runtime. It sandboxes the agent code by intercepting syscalls and providing a restricted kernel interface. The isolation is strong but theoretically weaker than hypervisor isolation.

In practice: Firecracker is more secure in theory; gVisor is secure enough for most use cases.

When does it matter? If your platform is multi-tenant and agents run untrusted code (competitor code, user-uploaded scripts), Firecracker's hypervisor isolation is worth the latency cost. If you're sandboxing your own agents (internal tools, proprietary code), gVisor is adequate and faster.

Threat model: "Can a malicious user's agent code break into another user's agent?" → Firecracker safer. "Can a malicious user's agent code access the host OS?" → Both prevent this.

(Source: Northflank, E2B Docs, Modal Docs)

Scalability-Modal's Infrastructure

E2B's Pro tier maxes out at 100 concurrent sandboxes. If you need to scale beyond that, you either upgrade to a custom tier or shard agents across multiple E2B projects (each with its own API key and account).

Modal autoscales elastically. You can spin up 1000+ concurrent GPU instances if needed. There's no hard limit. Modal's infrastructure is designed for bursty, unpredictable load. You set limits (to control spend), but there's no tier-based ceiling.

Scaling scenario: Your code-generation agent goes viral. 1,000 simultaneous users, each spawning a sandbox.

E2B: Hits the 100-sandbox limit. You need to queue requests, fail over to a second account, or ask E2B's team for a higher tier. Plan for 2-4 weeks of negotiation.

Modal: Instantly scales to 1,000 sandboxes. Cost grows predictably (per-second pricing scales linearly). No negotiation required.

Modal's autoscaling is transparent. You don't think about concurrency limits; you think about cost. E2B's tier system is simpler until you hit the cap, then it's urgent.

(Source: E2B Docs, Modal Docs)

When to Pick E2B vs Modal-Decision Tree

Pick E2B if:

  • Your agent workload is purely CPU (data analysis, code generation, text processing, Python iteration)
  • Latency <100ms per spawn is acceptable (batch agents, offline processing, overnight runs)
  • Cost is the primary optimization lever (E2B is 3-5x cheaper for CPU workloads)
  • Concurrent agent count stays <100
  • The sandbox needs to run untrusted code (multi-tenant isolation via Firecracker)
  • Budget is fixed and <$500/month (E2B's Pro is capped; Modal's GPU pricing is unpredictable)

Pick Modal if:

  • Your agent needs GPU access inside the sandbox (fine-tuning, local inference, image/video processing)
  • User-facing latency <50ms is critical (interactive agents, chat interfaces, real-time code execution)
  • Agent load may burst beyond 100 concurrent (autoscale is non-negotiable)
  • Budget allows $0.001+/s GPU cost (H100 ≈ $3.95/hour)
  • Simplicity over cost optimization (don't want to tune vCPU granularity)

Hybrid approach (most common):
Use E2B for CPU-only agent loops and Modal for inference. Or use Anthropic's Managed Agents (which defaults to E2B for tool execution) and call Modal for fine-tuning. This is not either-or; it's orchestrating the right platform for each task.

(Source: AgenticWire analysis)

FAQ

Does Modal support GPU inference inside sandboxes?

Yes. H100, H200, A100, B200 available. You request the GPU in the function decorator, and Modal allocates it. E2B has no GPU support. Your workaround is to call an external inference API (Anthropic, Together AI) from inside the E2B sandbox, which adds latency and cost complexity. (Source: Modal Pricing, E2B Docs)

What's E2B's cold-start advantage over Modal?

80-150ms vs <100ms. The difference is 50-100ms per sandbox spawn. For a single-sandbox agent, it's negligible. For a 5-step agent loop (3-5 spawns), E2B's overhead is 150-500ms cumulative. Modal's advantage is real but only matters for latency-critical applications. (Source: Northflank, E2B Docs)

How much cheaper is E2B for CPU-only workloads?

3-5x cheaper once you account for the Pro tier. E2B: $150/month tier + per-second usage. Modal: $30/month free tier covers most startup workloads, but GPU pushes cost up. For 100 agents at 1 hour/month CPU-only: E2B is ~$160/month. Modal is ~$5-30/month. The Pro tier floor is E2B's cost lever. (Source: E2B Pricing, Modal Pricing)

Can I migrate from E2B to Modal later?

Yes, but plan for refactoring. E2B uses filesystem and exec APIs (e.g., sandbox.exec()). Modal uses function decorators (e.g., @modal.function()). The underlying patterns are similar, but the API is different. Expect 2-5 days of rewriting for a medium-sized agent codebase. (Source: E2B Docs, Modal Docs)

Is Firecracker isolation critical for my use case?

Only if you're sandboxing untrusted user code in a multi-tenant platform. For internal agent tools (proprietary code you control), gVisor is secure enough. The isolation difference matters if threat model includes "Can an attacker's agent break into another user's agent?" If all agents are your own, gVisor's container-level isolation is sufficient. (Source: Northflank)

Related Coverage

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