Daytona's 90ms vs E2B's 150ms: What a 40% Cold-Start Gap at Price Parity Actually Means at 10,000 Sandboxes a Day

Two numbers dominate every 2026 comparison of AI-agent sandbox providers: Daytona spins up a sandbox in roughly 90 milliseconds, E2B in roughly 150. And because both platforms meter compute at almost exactly the same rate — about $0.0504 per vCPU-hour — the entire buying decision appears to collapse into that 60ms gap. A 40% cold-start advantage at identical unit price sounds decisive.

Here is what that gap actually amounts to, up front. At 10,000 sandbox creations a day, 60ms per start adds up to 10 minutes of aggregate wall-clock time — spread invisibly across 10,000 independent executions. And if that startup time were even billed as compute (it mostly isn't), 60ms at $0.0504/vCPU-hour costs $0.00000084 per start — about $0.0084 per day at that volume. The 60ms gap is not a cost question at any realistic scale. It is a latency question, and only in one specific shape of workload does it matter at all. This post works through the math at 1,000, 10,000, and 100,000 sandboxes a day, shows the one workload shape where the gap genuinely bites, and then looks at the limits that actually gate an agent platform at volume — because they are not the headline number.

Where the Numbers Come From — and How Much to Trust Them​

The 90ms and 150ms figures come from 2026 head-to-head benchmark posts, but the vendors' own claims already tell you these are moving targets:

• Daytona advertises "sub-90ms sandbox creation from code to execution" on its pricing page, and its docs state <90ms startup for default Linux sandboxes.
• E2B currently claims sandboxes "start in less than 200ms" in the same region as the client — and advertises 80ms starts on its Pro tier.

So depending on which tier, region, and template you measure, the "60ms gap" can widen, shrink, or even invert. That is the first honest takeaway: cold-start benchmarks for sandbox platforms are configuration-dependent snapshots, not physical constants. The math below uses the benchmarked 60ms delta, but it holds at any plausible value — double it to 120ms and every figure in the next section simply doubles, which, as you'll see, still doesn't change the conclusion.

The more durable difference is architectural. E2B runs every sandbox as a Firecracker microVM — the same hardware-virtualization technology AWS built for Lambda — giving each sandbox its own kernel and a hypervisor boundary against the host. Daytona's default sandbox is a Linux container (with VM and GPU variants available), which shares the host kernel. Booting a microVM does strictly more work than starting a container, so some of E2B's extra milliseconds are buying a stronger isolation boundary, not being wasted. If your agents execute genuinely untrusted, model-generated code from many tenants, that distinction can matter more than the latency it costs.

Both companies are well-capitalized and racing to close their respective gaps: E2B announced a $21M Series A and reports over 1 billion sandboxes started and 94% of Fortune 100 companies touching its platform; Daytona raised a $24M Series A led by FirstMark in February 2026 (with strategic checks from Datadog and Figma Ventures) after hitting a $1M forward revenue run rate in under three months, with LangChain, Turing, Writer, and SambaNova as customers.

The Worked Math: 60ms at 1,000 / 10,000 / 100,000 Sandboxes a Day​

Take the benchmark numbers at face value and run them against real volumes. Assume 1 vCPU per sandbox and the shared $0.0504/vCPU-hour rate (E2B meters per second at $0.000014/vCPU-second; Daytona's Linux rate lands at parity, which is exactly why the benchmark posts frame it as a latency-only race).

Two things jump out.

First, the cost column is noise. Even at 100,000 starts a day — far beyond what most agent platforms run — the 60ms gap is worth about $2.52 a month in compute. Nobody's infrastructure bill has ever been rescued by 8 cents a day. At price parity, cold start is simply not a cost lever.

Second, the wall-clock column is misleading unless you know the workload shape. "100 minutes of added latency" sounds alarming, but it never appears anywhere as 100 contiguous minutes. Whether it matters depends entirely on how those starts are arranged:

• Parallel fan-out (one agent forks 200 sandboxes to explore 200 branches at once): all 200 sandboxes start concurrently, so the entire fan-out absorbs the gap once — the batch completes ~60ms later. At 100,000 starts a day arranged as fan-outs, the experienced added latency rounds to zero. The gap amortizes away completely.
• Serial agent chains (a coding agent runs a 12-step loop — generate, execute, read the error, regenerate — with a fresh sandbox per step): the gap compounds per step. Twelve steps × 60ms = 720ms added to the p50 of every user-facing request. Stack a few of those chains inside one interactive session and the "trivial" 60ms is now a visible second of hang time. This — and only this — is the workload where the benchmark headline translates into something a user feels.

There's a third framing worth checking: cold start as a fraction of total execution time. For genuinely short-lived executions — say a 2-second code run, the typical unit of work when an agent is iterating on a script — a 90ms start is 4.3% of wall clock and a 150ms start is 7%. Real but small. Stretch the task to 60 seconds (a test suite, a build) and even the slower start is 0.25% — pure noise. The shorter and more serial your executions, the more the gap matters; the longer or more parallel, the faster it vanishes.

That is the honest reading of "90ms beats 150ms at price parity": it is a p50-latency argument for interactive, serial, short-task agents — and close to irrelevant for everything else.

What Actually Gates You at Thousands of Sandboxes a Day​

Here is what the cold-start headline hides: at real agent volume, you will hit a structural ceiling long before a 60ms delta shows up in any dashboard.

Concurrency caps. E2B's Hobby tier allows 20 concurrent sandboxes; Pro allows 100, expandable to 1,100 as a paid add-on. Now replay the fan-out scenario above: an agent forks 200 branches at once. On Hobby, 180 of them queue. On Pro, 100 queue. Queueing delay at a hard cap is measured in seconds to minutes — three orders of magnitude larger than the cold-start gap the benchmark posts obsess over. A platform whose agents fan out aggressively should be comparing concurrency ceilings and their overage pricing, not shaving milliseconds.

Session length limits. E2B caps sandbox sessions at 1 hour on Hobby and 24 hours on Pro. Short-lived agent executions fit comfortably; a long-running agent that keeps a stateful environment warm across a workday does not, and needs explicit checkpoint/resume logic either way.

Lifecycle semantics. Daytona's sandboxes carry native lifecycle policies — auto-stop after idle, auto-archive to object storage, auto-delete (set the interval to zero and sandboxes are ephemeral by construction) — plus snapshot-based creation and pause/resume with memory snapshots on VM sandboxes. At thousands of sandboxes a day, what happens to a sandbox after the agent stops caring about it is an operational cost center: orphaned sandboxes are the agent-infra version of zombie EC2 instances, and they cost real money in a way 60ms never will (a single forgotten 2-vCPU sandbox left running for a month is ~$73 — roughly 240× what the cold-start gap costs in a full year at 1,000 starts/day).

Isolation blast radius. The Firecracker-vs-container difference from earlier returns as a throughput question. A shared-kernel container escape exposes every tenant on the node; a microVM escape has to get through a hypervisor boundary first. If sandbox contents are single-tenant and low-stakes, containers' speed wins cleanly. If you're running arbitrary code on behalf of many customers, the slower microVM may be the only defensible default — and you should read the 60ms not as overhead but as the price of the boundary.

Ranked honestly for a team choosing a backend at thousands-of-executions-a-day scale: concurrency ceiling first, isolation model second, lifecycle automation third, cold start fourth — and cold start climbs that ranking only if your agents run serial, interactive, seconds-long tasks.

If You're Building the Backend Instead of Buying It​

For platform teams building their own sandbox layer — because agent execution has to run on owned hardware, inside a compliance boundary, or simply below hyperscaler margins — the benchmark race is less a purchasing guide than a bill of materials. The 90ms-class numbers on both sides come from the same three levers, all reproducible on your own machines:

1. Pre-warmed pools. Nobody boots from zero on the request path. Keep N sandboxes booted and paused; "creation" becomes handing one out and re-warming in the background. This one lever turns any backend into a <100ms backend at the cost of idle-pool RAM — which, on hardware you own, is capacity you've already paid for.
2. Snapshot/restore. Firecracker's snapshot support and container checkpointing (CRIU) both let you resume a pre-initialized environment — runtime loaded, dependencies imported — instead of cold-booting. This is also what Daytona's fork-and-branch semantics and E2B's template system are built on.
3. Minimal images. Every megabyte in the rootfs is milliseconds on the clock. Purpose-built sandbox images, not general-purpose base images.

The isolation decision is the same one the vendors made, and pricing it honestly matters: Firecracker microVMs if tenants are mutually untrusted; gVisor as a middle path (syscall interception, weaker than a hypervisor, stronger than raw runc); plain containers only when the code's provenance is trusted. Your workload's position on the serial-vs-parallel spectrum — the same variable that decided the buy-side math above — decides how much pool warmth you need and how much isolation overhead you can afford.

This is the direction agent-native platforms are converging on: the sandbox pool stops being a product you subscribe to and becomes a primitive your platform operates — sized, warmed, and audited like any other node pool. That's the model we're building toward at Bex.co: an open-source, AI-native deploy platform where agents are first-class operators, pushing code and getting running services on machines you own — and where the execution environments agents use are part of the platform's own machine-readable state, not a third-party black box.

The Benchmark Is Real. It's Just Not the Decision.​

None of this says the 2026 benchmarks are wrong — 90ms genuinely beats 150ms, and for an interactive coding agent chaining a dozen short executions per request, the compounding 720ms is a real UX difference worth switching for. But at price parity, cold start is a proxy metric: it predicts almost nothing about your bill, and it predicts user-visible latency only for serial, short-task workloads. The axes that decide whether a sandbox backend survives contact with thousands of agent executions a day are concurrency ceilings, isolation blast radius, and lifecycle hygiene — and those don't fit in a headline.

Expect the headline race to end in a tie anyway. With E2B already claiming 80ms on Pro and Daytona holding sub-90ms, sub-100ms starts are becoming table stakes, and both $20M+ war chests are being spent on the same next frontier: snapshot, fork, and pause/resume semantics — turning a sandbox from "a fast-booting box" into "a process you can branch." When every provider starts in double-digit milliseconds, the benchmark posts will need a new number. Choose — or build — on the axis your workload actually stresses, and treat any single-number comparison as the beginning of the evaluation, not the end of it.

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