Code Execution Sandbox Pricing 2026: Cost, Cold Starts, and Persistence for AI Agents

If you’re building AI agents that execute code — whether it’s a coding agent running tests, a data analysis pipeline spinning up Python interpreters, or a multi-agent system that needs isolated environments per task — the sandbox you pick directly determines your cost structure, your latency profile, and how much state management code you have to write yourself. Three providers dominate this space in 2026: E2B, Modal, and Fly.io. Each one has a fundamentally different pricing model, cold start story, and persistence strategy. I’ve been running production agent workloads on all three for the past six months, and the differences are bigger than the marketing suggests.

The Pricing Models at a Glance

The first thing to understand is that these three providers don’t just charge different rates — they charge in fundamentally different ways.

E2B uses a hybrid model: a monthly subscription tier ($150/month for Pro) plus per-second compute on top. You pay the subscription for the right to run sandboxes at scale, then you pay per-second for actual CPU and RAM usage. The Hobby tier is free with $100 one-time credits, but you’re limited to 1-hour sandbox sessions and 20 concurrent sandboxes.

Modal is pure per-second billing with no subscription fee. You get $30/month in free compute credits on the Starter plan, and after that you pay only for what you use. There’s no upfront commitment, no minimum spend. This makes Modal the most flexible option for variable or bursty workloads.

Fly.io is also per-second billing but with a suspend/resume model that changes the economics significantly. Their smallest shared-CPU VM (256MB) costs $0.00000078/second — roughly $2.02/month if it runs continuously. But the real trick is that you can suspend VMs when they’re idle and pay only $0.15/month for metadata retention.

Here’s the raw per-second pricing for CPU-only workloads:

E2B and Modal are within 7% of each other on raw CPU pricing. Fly.io is 3-4x cheaper on paper, but that comparison is misleading — Fly.io’s shared-CPU VMs are not equivalent to dedicated vCPUs, and you’re sharing the physical core with other tenants. Their dedicated performance instances close the gap: a performance-1x 2GB VM costs $0.00001056/s, which is about 75% of E2B’s 1 vCPU rate.

Cold Starts: The Hidden Cost That Wastes Your Budget

Cold starts matter more for AI agents than for traditional serverless workloads, and here’s why: an interactive agent that calls a sandbox, runs a 200ms Python snippet, and waits for the result feels sluggish if the sandbox takes 2 seconds to boot. For batch processing, cold starts are a throughput problem. For interactive agents, they’re a user experience problem.

E2B claims sub-second cold starts for their lightweight Firecracker microVMs. In my testing, I consistently saw 400-800ms from request to first byte of execution. This is good enough for most interactive agent use cases. The sandboxes are purpose-built for AI agent code execution, so the boot path is optimized for exactly this scenario — spin up a minimal VM, run the code, return the result.

Modal has a split story. CPU functions cold start in 200-500ms, which is actually faster than E2B for simple workloads. GPU functions are a different story: 5-15 seconds for model loading. If your agent needs GPU inference (say, running a vision model on a screenshot), you’ll want to use Modal’s container pooling or scheduled functions to keep a warm instance ready. The $30/month free credits help offset the cost of keeping a GPU warm, but it’s something you have to design for.

Fly.io has the most interesting cold start story because of their suspend/resume model. A suspended VM resumes in 100-500ms — faster than either E2B or Modal for CPU workloads. A true cold start (first boot) takes 1-3 seconds. The key insight is that if you keep a VM suspended rather than destroyed, the resume is nearly instant. This makes Fly.io uniquely suited for agent workloads where the same sandbox is used repeatedly with gaps between executions.

The practical takeaway: if your agents make frequent, short-duration calls to the same sandbox, Fly.io’s suspend/resume model saves you both latency and money. If your agents are stateless and ephemeral — spin up, run, destroy — E2B or Modal are better fits.

Persistence: Session-Based vs. Volume-Based vs. Suspend/Resume

How your sandbox handles state across executions is the single most underestimated cost factor in agent infrastructure. I’ve seen teams burn through thousands of dollars because they designed their agents around persistent state on a platform that charges per-execution.

E2B is session-based. A sandbox session can last up to 1 hour on the Hobby plan or 24 hours on Pro. Within a session, the filesystem persists. Between sessions, everything is gone unless you explicitly export data. This works well for agents that complete their work within a single session — a coding agent that clones a repo, runs tests, and reports results in under an hour, for example. But if your agent needs to maintain state across days or weeks, you’ll need to build your own persistence layer on top.

Modal is stateless by default. Functions don’t retain filesystem state between invocations. If you need persistence, you pay $0.09/GiB/month for Modal Volumes, with the first 1 TiB free. This is cheap for long-term storage but adds latency for frequent reads and writes. Modal’s model works best for agents that process data in discrete chunks — pull input from a volume, process, write results back — rather than agents that maintain a long-running interactive session.

Fly.io supports both ephemeral storage (6GB included per VM) and persistent volumes. The suspend/resume model means a VM’s in-memory state is preserved across suspensions. This is the closest you’ll get to a “persistent agent” without building custom infrastructure. I’ve run agents that maintain database connections, in-memory caches, and accumulated state across hours of wall-clock time with no additional cost beyond the $0.15/month suspension fee.

Total Cost for Real Workloads

Let me give you three concrete scenarios based on workloads I’ve actually run.

Scenario 1: Interactive coding agent (CPU-only, 500 executions/day, 30 seconds each)

• E2B Pro ($150/month) + usage: ~$0.000028/s × 30s × 500 = $0.42/day = ~$12.60/month. Total: ~$163/month.
• Modal (no subscription): ~$0.0000131/core/s × 30s × 500 = $0.20/day = ~$6/month. Total: ~$6/month (under $30 free tier).
• Fly.io (performance-1x 2GB): ~$0.00001056/s × 30s × 500 = $0.16/day = ~$4.80/month. Total: ~$5/month.

For this workload, Modal and Fly.io are dramatically cheaper because there’s no subscription overhead. E2B’s $150/month Pro tier only makes sense if you need the 24-hour session limit or 100+ concurrent sandboxes.

Scenario 2: Batch data processing (CPU-only, 10,000 executions/day, 5 seconds each)

• E2B Pro ($150/month) + usage: ~$0.000028/s × 5s × 10,000 = $1.40/day = ~$42/month. Total: ~$192/month.
• Modal: ~$0.0000131/core/s × 5s × 10,000 = $0.66/day = ~$20/month. Total: ~$20/month.
• Fly.io (performance-2x 4GB): ~$0.00002111/s × 5s × 10,000 = $1.06/day = ~$32/month. Total: ~$32/month.

Modal wins on raw volume because there’s no subscription floor. E2B’s per-second rate is competitive, but the $150/month subscription dominates the total cost.

Scenario 3: GPU inference agent (H100, 1,000 executions/day, 10 seconds each)

• Modal H100: $0.001097/s × 10s × 1,000 = $10.97/day = ~$329/month.
• E2B: No GPU tier publicly listed.
• Fly.io: GPU available but pricing not public.

If your agent needs GPU, Modal is essentially the only option with transparent pricing. E2B and Fly.io either don’t offer GPU or don’t publish rates.

When to Pick Each One

After running production workloads on all three, here’s my honest assessment of where each provider fits.

Choose E2B when you need purpose-built sandbox isolation for untrusted code execution, you’re running long-lived agent sessions (up to 24 hours), and your workload justifies the $150/month Pro subscription. E2B’s sandboxes are the most secure out of the box for running untrusted AI-generated code. If you’re building a platform where users can submit arbitrary code, E2B should be your first look.

Choose Modal when you need GPU compute, your workloads are bursty or variable, and you don’t want to commit to a monthly subscription. Modal’s GPU selection is unmatched — they offer everything from T4s to the latest B300s — and the pure per-second billing means you never pay for idle capacity. The $30/month free tier is generous enough for prototyping and light production use.

Choose Fly.io when you’re optimizing for cost at low utilization, you need suspend/resume for persistent agent state, and your workloads are CPU-only. Fly.io’s free tier (3 always-free VMs) and absurdly cheap shared-CPU pricing make it the best choice for small-scale deployments and personal projects. The suspend/resume model is genuinely innovative for agent workloads.

The Bottom Line

The sandbox pricing landscape in 2026 has settled into three distinct models, and the right choice depends more on your workload pattern than on raw per-second rates. E2B and Modal are within spitting distance on CPU pricing, but E2B’s subscription model changes the economics at scale. Fly.io is the cost leader for low-utilization CPU workloads but falls short on GPU and sandbox isolation.

If I were starting a new agent project today, I’d default to Modal for anything involving GPU, Fly.io for CPU-only personal projects or low-traffic services, and E2B for production platforms that need to run untrusted code at scale. And I’d keep an eye on Cloudflare Sandboxes — they reached GA in April 2026 and their zero-trust egress model is a compelling addition to the space, especially for teams that already run on Cloudflare.

For a deeper look at how E2B compares with other purpose-built sandbox providers, check out my E2B vs Daytona vs Blaxel comparison. And if sandbox security is your primary concern — especially for AI-generated code — the Crawl4AI RCE sandbox escape is a sobering case study in why isolation matters.