Best AI Agent Sandbox 2026

E2B is the security-first answer. Every sandbox is a Firecracker microVM with its own Linux kernel, which is the same primitive AWS Lambda uses to separate tenants. That closes the class of cross-tenant escapes that container-based isolation leaves open, at the cost of a ~150ms boot instead of a container's few tens of milliseconds.

Pricing is granular and cheap: the default 2 vCPU configuration runs $0.000028/second with memory at $0.0000045 per GiB-second, and storage is included (10 GiB on Hobby, 20 GiB on Pro). Hobby ships $100 in one-time credits, which is a lot of sandbox-hours for a prototype. The Pro plan at $150/month lifts sessions from 1 hour to 24 and concurrency from 20 to 100, extendable to 1,100.

Best for: Agents executing genuinely untrusted code, or anything where you'll be asked to describe the isolation boundary in a security review. Skip if: you need a GPU inside the sandbox, or 60ms of extra cold start is user-visible in your loop.

Daytona competes on the two things E2B trades away: speed and persistence. Sandbox creation lands around 90ms — roughly 40% faster than E2B at essentially identical pricing — and tuned configurations have been measured near 27ms. When an agent creates a fresh sandbox on every tool call, that difference compounds across a session.

The bigger architectural difference is that Daytona workspaces are persistent by default. E2B and Modal sandboxes are ephemeral things you snapshot to preserve; a Daytona workspace exists until you delete it, more like a Codespace. For long-running agents that revisit the same project across hours or days, that removes an entire category of state-management code. It runs standard Docker/OCI images, so your existing Dockerfile works, and signup includes $200 in compute with up to $50k in credits available to startups.

Best for: High-frequency agent loops and long-lived coding sessions. Skip if: you need GPUs, or your compliance story specifically requires Firecracker.

Modal is the outlier, and the comparison mostly collapses to one fact: it is the only platform where a sandbox can hold a GPU. If your agent needs to run inference, fine-tune a model, or process images inside the same isolated process that handles its tool calls, Modal is the option. That capability comes from its isolation choice — gVisor is a software kernel, so GPU passthrough stays straightforward in a way hardware virtualization complicates.

CPU runs $0.0000131 per core-second (minimum 0.125 cores) and memory $0.00000222 per GiB-second, with GPUs billed per second: T4 at $0.000164, L4 at $0.000222, H100 at $0.001097, B300 at $0.001972. The Starter plan is $0 with $30/month in credits and 100 containers; Team is $250/month with $100/month in credits and 5,000 containers. Cold starts are sub-second — slower than Daytona, irrelevant next to the GPU capability.

Best for: ML-adjacent agents, code interpreters that handle media, anything needing accelerated compute in-sandbox. Skip if: you're only running CPU code — you'll pay roughly 40% more per hour than E2B or Daytona for capability you don't use.

Vercel Sandbox wins on two things that don't show up in a cold-start benchmark. First, active-CPU billing: time your sandbox spends waiting on I/O — an LLM call, a package install, a database query — isn't billed. For agent workloads, which are overwhelmingly I/O-bound, that can be the single biggest cost lever available. Vercel's own example puts a 5-minute, 2 vCPU AI code-validation run at about $0.03.

Second, it's one less vendor. If you're already deploying on Vercel, sandboxes are one API and one bill. The Hobby tier includes 5 CPU-hours, 420 GB-hours of memory, and 5,000 creations per month free. The constraints to know: maximum runtime is 45 minutes on Hobby (24 hours on Pro), concurrency is 10 on Hobby versus 10,000 on Pro, and — the real limitation — it currently runs only in the iad1 region.

Best for: Teams already on Vercel, and I/O-heavy agents where idle time dominates. Skip if: you need multi-region execution or GPU access.

Cloudflare's pitch is locality: the sandbox runs on the same edge network as your Workers, near the user, with no egress penalty for traffic that stays inside Cloudflare. If your agent already lives in a Worker and coordinates through Durable Objects, keeping execution in the same platform avoids a round trip to a distant region on every tool call.

The cost is startup time. A 2–3 second cold start is an order of magnitude worse than Daytona or E2B, and in an interactive agent loop that is plainly noticeable. At roughly $0.09/hour plus the $5/month Workers base, pricing sits mid-pack — you're paying for distribution, not compute economics.

Best for: Edge-first apps already built on Workers where geographic locality is a product requirement. Skip if: cold start is in your critical path — which, for most agent loops, it is.