Every AI coding agent that writes and runs its own code needs somewhere safe to do it. That is the entire premise behind cloud sandboxes: isolated, disposable machines that spin up in milliseconds, execute untrusted code, and disappear before anything can go wrong. By September 27, 2026, three names dominate that market conversation: E2B, Modal Sandboxes, and Daytona. Each takes a different bet on isolation technology, billing model, and who the customer actually is, and the gaps between them are wide enough to change your infrastructure bill by a meaningful margin.
This comparison pulls pricing, GitHub activity, and product claims directly from each vendor’s live pricing pages and public repositories as of this week, cross-checked against independent comparison write-ups from Novita and Northflank. The goal is a straight, numbers-first answer to which sandbox platform fits which kind of AI agent workload, plus the alternatives (Cloudflare Sandbox, Vercel Sandbox, Northflank) that are eating into the conversation.
Why AI Coding Agents Need a Cloud Sandbox in 2026
A coding agent that can write Python, run shell commands, install packages, and browse files is only useful if it can do those things without touching your production infrastructure. That is the job a cloud sandbox does: it hands the agent a throwaway virtual machine or container, lets it execute arbitrary code, captures the output, and tears the environment down. E2B markets itself directly at this need, describing itself on its homepage as “The Enterprise AI Agent Cloud” and stating flatly that it is an “open-source sandbox platform that gives AI agents isolated machines.”
The category has grown fast because agentic coding tools stopped being a novelty around 2025 and became a daily workflow for millions of developers. Every time a tool like Cursor, Manus, or a custom LangChain agent needs to actually execute generated code rather than just suggest it, that execution has to happen somewhere. Running it on the same host as your application server is a security nightmare; running it in a sandbox that boots, executes, and dies in under a second is the accepted alternative. E2B currently states that more than 100,000 teams run on its platform and that 94 of the Fortune 100 have signed up for accounts, a sharp jump from the 88% figure reported in earlier 2026 write-ups.
What separates the three leading platforms is not whether they can isolate code (they all can) but how they price that isolation, how fast they can create a fresh sandbox, and whether they were built primarily for coding agents, general serverless compute, or AI training and inference with sandboxes bolted on. Those distinctions matter more than marketing copy suggests once you are running thousands of sandbox sessions a day and the per-second billing starts to compound.
E2B vs Modal vs Daytona: Spec Comparison at a Glance
Before getting into pricing mechanics, here is how the three platforms stack up across the specs that actually change a purchasing decision. Figures are pulled from each vendor’s current pricing and product pages, plus live GitHub API data captured this week.
| Spec | E2B | Modal Sandboxes | Daytona |
|---|---|---|---|
| Founded | 2023 | 2021 (sandbox product added later) | 2024 |
| Isolation technology | Firecracker microVMs | gVisor container sandboxing | Container-based, open runtime |
| GitHub stars (core repo) | 13,988 | 519 (modal-client SDK only) | 71,700 |
| Cold-start claim (vendor-stated) | “Boots ready in milliseconds” | “Sub-second cold starts” | “Sub 90ms sandbox creation” |
| Base vCPU pricing | $0.0504/vCPU-hour | ~$0.071/vCPU-hour (physical core billing) | $0.0504/vCPU-hour |
| Memory pricing | $0.0162/GiB-hour | $0.0240/GiB-hour | $0.0162/GiB-hour |
| GPU support | Limited, enterprise-tier | Full lineup, B300 to T4 | Full lineup, B300 to RTX 4090 |
| Free tier | Hobby plan, 20 concurrent sandboxes | $30/month free compute, 100 containers | $200 in free compute credit |
| Default max session length | 24 hours (Pro tier) | 5 minutes default, up to 24 hours configurable | Persistent/stateful sessions supported |
| Open source | Yes | Partially (client SDK only) | Yes |
| Named enterprise minimum | $3,000/month | Custom (Team tier starts at $250/month + compute) | Not publicly listed |
| Cursor integration | Not listed in Cursor docs | “Cursor on Modal” documented | “Cursor Self-Hosted Machines on Daytona” documented |
| Primary positioning | Dedicated AI agent sandbox cloud | General serverless compute with sandbox primitive | Open dev-environment platform for AI coding agents |
Two things jump out immediately. First, the GitHub star gap between Daytona (71,700) and E2B (13,988) looks dramatic, but it reflects different scopes: Daytona’s flagship repository bundles its entire open dev-environment platform, while E2B’s core repo is scoped tighter around the sandbox runtime itself. Second, Modal’s 519-star client library is not a fair apples-to-apples comparison at all, since Modal’s actual sandbox execution engine is closed-source infrastructure behind the SDK, unlike E2B and Daytona which publish their runtimes openly.
Isolation Technology: Firecracker microVMs vs gVisor vs Containers
The security model underneath each platform is the part most buyers skip past, and it is the part that should matter most if you are running untrusted, agent-generated code at scale. E2B builds on Firecracker, the microVM technology AWS open-sourced in 2018 and still uses to isolate Lambda functions internally. Firecracker microVMs boot a minimal Linux kernel with just enough device emulation to run a workload, giving each sandbox true kernel-level isolation rather than shared-kernel container isolation. That is a meaningfully stronger security boundary than a standard container, and it is the reason E2B can credibly market itself to enterprise security teams evaluating agent code execution risk.
Modal takes a different approach with gVisor, Google’s user-space kernel that intercepts and re-implements system calls rather than passing them straight to the host kernel. gVisor is lighter-weight than a full microVM and was designed for exactly this use case: running untrusted code inside Google Cloud Run and similar serverless products without the overhead of full virtualization. The tradeoff is a slightly weaker isolation boundary than Firecracker in exchange for faster start times and simpler orchestration, which lines up with Modal’s broader positioning as a general compute platform where sandboxes are one primitive among many (alongside GPU inference, training jobs, and scheduled functions).
Daytona uses a container-based runtime that it has open-sourced end to end, prioritizing raw creation speed (that sub-90ms claim) and developer control over the exact hardening layer. Because the project is fully open, teams with strict compliance requirements can audit and self-host the isolation layer rather than trusting a vendor’s closed internals, which is precisely why Cursor lists “Cursor Self-Hosted Machines on Daytona” as a supported deployment path in its own documentation.
None of these approaches is objectively wrong. Firecracker is the safest default for anyone running fully untrusted third-party code from unknown users. gVisor is a reasonable middle ground when you control the agent logic but not necessarily every line of code it generates. Container-based isolation with an open runtime is the right call when your team needs to see and modify the isolation boundary itself, whether for compliance, self-hosting, or cost control.
Pricing Breakdown: vCPU-Hour, GiB-Hour, and GPU Rates Compared
Sandbox pricing looks deceptively simple until you factor in memory, storage, and GPU add-ons. Here is the full breakdown pulled directly from each vendor’s live pricing calculator this week.
| Resource | E2B | Modal Sandboxes | Daytona |
|---|---|---|---|
| vCPU (per hour) | $0.0504 | $0.1419 per physical core (~2 vCPU) | $0.0504 |
| Memory (per GiB-hour) | $0.0162 | $0.0240 | $0.0162 |
| Storage (per GiB-hour) | Included, tiered by plan | $0.09/GiB-month for volumes | $0.000108 (first 5 GiB free) |
| Nvidia H100 (on-demand) | Enterprise-tier only | $3.95/hour | $3.95/hour |
| Nvidia H200 (on-demand) | Enterprise-tier only | $4.54/hour | $4.54/hour |
| Nvidia A100 80GB | Enterprise-tier only | $2.50/hour | Not listed |
| Nvidia L4 | Enterprise-tier only | $0.80/hour | Not listed |
| GPU preemptible/spot discount | Not offered | Not offered | Up to ~35% off (H100 preemptible: $2.27/hour) |
| Windows OS surcharge | Not offered | Not offered | $0.0858/vCPU-hour |
| Example: 100 concurrent sandboxes/month | $316/month (Pro tier, per E2B’s calculator) | Varies with usage, Team tier starts $250 + compute | Varies with usage, pay-as-you-go |
E2B and Daytona charge identical base rates for vCPU and memory: $0.0504 per vCPU-hour and $0.0162 per GiB-hour, both billed to the second. That is not a coincidence so much as a sign the two companies are competing directly on the same customer segment and have converged on a similar price floor. Modal’s Sandbox and Notebooks pricing sits notably higher on compute, at roughly $0.071 per vCPU-hour equivalent and $0.024 per GiB-hour, a premium that Modal justifies with a broader platform that spans GPU inference, training, and general serverless functions rather than sandboxes alone.
Where Modal and Daytona pull ahead of E2B is GPU access. Both list a full lineup down to consumer-adjacent cards, with H100 and H200 rates matching almost exactly ($3.95 and $4.54 per hour respectively on both platforms), while E2B keeps GPU compute behind its custom enterprise tier rather than exposing it on the self-serve pricing calculator. If your agent workload needs to fine-tune a model or run GPU-accelerated inference inside the sandbox itself, Modal and Daytona are the only two of the three with a self-serve path to that hardware today.
Free Tiers, Startup Credits, and Minimum Spend
For a solo developer or small team prototyping an agent, the free tier is often the deciding factor before pricing tiers even matter. E2B’s Hobby plan is scoped around concurrency rather than a flat credit: 20 concurrent sandboxes included at no cost, expanding to 100 concurrent on the Pro plan before any add-on fees kick in. Beyond that, Pro+ adds 600 concurrent sandboxes for an extra $500 per month, and Pro++ pushes to 1,100 concurrent for an additional $1,000 per month. E2B’s own cost calculator shows a representative Pro-tier workload (100 concurrent sandboxes, moderate usage) landing at $316 per month, split between a $166 usage charge and a $150 base fee. Enterprise plans require a $3,000 monthly minimum.
Modal takes the opposite approach, leading with a straightforward credit: $30 per month in free compute on the Starter tier, covering three workspace seats, 100 containers, and 10 GPU-concurrent slots. The Team tier jumps to $250 per month plus compute, but bundles $100 per month in free compute, unlimited seats, 5,000 containers, and 50 GPU-concurrent slots. Modal also runs credit programs outside the standard tiers: up to $10,000 in free compute for graduate students and academic labs, and separate startup credit grants for early-stage companies, both applied for directly through Modal.
Daytona’s free tier is the simplest to describe: a flat $200 in free compute credit on signup, with pay-as-you-go billing after that and no published enterprise minimum. Combined with vCPU and memory rates matching E2B’s, that makes Daytona’s free tier the most GPU-friendly of the three for early experimentation, since that $200 credit can be burned on H100 or H200 time rather than being locked to CPU-only workloads the way E2B’s Hobby tier effectively is.
Cold-Start Speed and Session Duration Limits
Cold-start latency is the single most agent-relevant benchmark in this comparison, because an agent that has to wait multiple seconds for a sandbox to boot burns both time and, on active-CPU billing models, money while it waits. Daytona is the most specific of the three vendors here, stating directly on its homepage: “Sub 90ms sandbox creation from code to execution.” That is a concrete, testable number, not a marketing range, and it is paired with a code example showing a single daytona.create call as the entire provisioning step.
Modal describes its cold starts more loosely as “sub-second,” which on its own website covers everything from Modal’s sandbox product down to its GPU inference containers. Sub-second is still fast in absolute terms, but it leaves an order-of-magnitude gap against Daytona’s specific sub-90ms figure that Modal’s marketing does not close with a harder number. E2B lands in between rhetorically: its own site promises sandboxes that “boot ready in milliseconds” without publishing an exact figure, though its Firecracker-based architecture is the same microVM technology AWS uses to hit consistently low double-digit-to-low-triple-digit millisecond boot times in Lambda.
Session duration limits diverge just as much as cold-start numbers. Modal’s default sandbox lifetime is five minutes unless explicitly configured longer, up to a 24-hour ceiling, which fits its broader serverless-functions mental model of short, bursty jobs. E2B’s Pro tier supports continuous sessions up to 24 hours, aimed squarely at long-running agent tasks that need to keep state alive across many tool calls. Daytona markets itself around “stateful infrastructure for AI agents” and supports persistent sessions without publishing a hard ceiling the way Modal does, leaning into use cases where an agent needs to return to the same environment repeatedly rather than spinning up fresh each time.
Developer Experience: SDKs and a Quick-Start Example
All three platforms ship Python and JavaScript SDKs designed to get a sandbox running in a handful of lines, which is by design: agent frameworks need to call sandbox creation programmatically, not through a dashboard. The general shape of the code is nearly identical across vendors, which is itself notable, since it means the real switching cost between platforms lives in pricing and infrastructure behavior rather than developer ergonomics.
# Representative sandbox lifecycle, shape shared across E2B, Modal, and Daytona SDKs
from sandbox_sdk import Sandbox
# 1. Create an isolated sandbox (sub-second to sub-100ms depending on vendor)
sbx = Sandbox.create(template="python-3.12", timeout_seconds=300)
# 2. Run agent-generated code inside the isolated environment
result = sbx.run_code("import pandas as pd; print(pd.__version__)")
print(result.stdout)
# 3. Upload/download files if the agent needs to persist artifacts
sbx.files.write("/tmp/output.csv", data)
# 4. Tear down the sandbox when the agent's task completes
sbx.kill()Where the SDKs diverge is in the extras layered on top. E2B’s SDK exposes a filesystem and process API tuned specifically for coding-agent patterns: reading and writing files, streaming stdout from long-running processes, and taking filesystem snapshots. Modal’s SDK treats sandboxes as one primitive inside a much larger toolkit that also covers scheduled functions, GPU inference endpoints, distributed queues, and volumes, which means teams already using Modal for model serving can add sandboxes without adopting a second vendor’s SDK. Daytona leans into IDE-style integration, positioning itself as infrastructure for full development environments (not just isolated code execution), which is why its docs emphasize workspace persistence and remote-development patterns over pure ephemeral task execution.
Real-World Deployments: Who Runs Agent Code on Each Platform
Vendor customer lists are self-reported, so they should be read as evidence of association rather than exhaustive proof of exclusive usage. With that caveat, each platform’s named integrations tell a fairly consistent story about where it fits.
- Perplexity is listed among E2B’s named customers for agentic workflows that require code execution alongside search and retrieval.
- Hugging Face uses E2B, aligning with E2B’s broader claim of serving “top AI labs” running agent infrastructure.
- Manus, the autonomous AI agent product, runs on E2B’s sandbox infrastructure for its code-execution tasks.
- Groq is named as an E2B customer, pairing E2B’s isolation layer with Groq’s inference speed for agent pipelines.
- Lindy, an AI automation platform, is listed among E2B’s customers for the same reason.
- Cursor documents direct support for both “Cursor on Modal” (cloud-hosted compute) and “Cursor Self-Hosted Machines on Daytona” (self-hosted, open-source deployment), making Cursor the clearest example of a single major AI coding tool supporting two of the three platforms as alternate backends.
That Cursor pattern is worth sitting with: rather than picking one sandbox vendor, one of the most widely used AI coding tools on the market offers both a managed option (Modal) and a self-hosted, open-source option (Daytona) depending on a team’s security and control requirements. It is a strong signal that the “right” sandbox choice genuinely depends on deployment constraints rather than one platform being categorically better.
The Billing Model Divide: Wall-Clock vs Active-CPU Pricing
There is a structural pricing difference cutting across this entire market that most side-by-side comparisons miss: whether a platform bills for wall-clock time (the sandbox is running, so you pay, regardless of whether code is actively executing) or active-CPU time (you only pay while the CPU is actually doing work). E2B, Modal, and Daytona all default to wall-clock-style billing on their standard vCPU and memory rates. Vercel Sandbox and Cloudflare Sandbox instead default to active-CPU billing, a distinction that matters enormously for agent workloads specifically, because agents spend most of their runtime waiting.
Vercel’s own documentation cites research showing that waiting for model inference accounts for 71% to 98% of total runtime across five measured agent benchmarks. In other words, an agent sandbox is idle, waiting on an LLM API call to return, for the overwhelming majority of its lifetime. Vercel’s pricing page runs the math on a representative example: a sandbox with 1 vCPU and 2GB of memory running for one hour at 10% CPU utilization costs $0.0552 total under active-CPU billing versus $0.1704 under wall-clock billing for the same hour, a roughly 3.1x difference driven entirely by not charging for the 90% of the hour spent waiting.
That does not automatically make Vercel or Cloudflare cheaper in every scenario. A CPU-bound workload that stays busy the entire session (heavy data processing, compilation, or dense computation with no external API waits) will see little to no benefit from active-CPU billing and may end up paying a higher effective rate than E2B or Daytona’s flat, lower per-vCPU-hour price. The billing model choice is a bet on your workload’s idle ratio, and for chat-style coding agents that spend most of their time waiting on a model response, that bet increasingly favors active-CPU platforms.
Alternative Sandboxes Worth Knowing: Cloudflare, Vercel, and Northflank
E2B, Modal, and Daytona are not the entire market. Three other platforms show up constantly in comparison searches and deserve a place in the decision, especially for teams already inside a specific cloud ecosystem.
| Platform | Billing model | Free allowance | Notable trait |
|---|---|---|---|
| Cloudflare Sandbox | Active-vCPU-second: $0.000020/sec after 375 included vCPU-minutes | 375 vCPU-minutes, 25 GiB-hours memory, 200 GB-hours storage | Can run indefinitely with keepAlive enabled; runs on Cloudflare’s edge network |
| Vercel Sandbox | Active-CPU: $0.128/hour, $0.60 per 1M sandbox creations | 5 active CPU-hours/month, 5,000 creations/month, 20GB transfer | Deep Next.js/Vercel platform integration; 204 GitHub stars, launched January 2026 |
| Northflank | Wall-clock: $0.01667/vCPU-hour, $0.00833/GB-hour | Varies by plan | $0.06/GB egress with free ingress; positions as a general deployment platform with sandbox support |
Cloudflare Sandbox is the newest serious entrant of the group, built as an SDK for running sandboxed code directly on Cloudflare’s edge network, and its open-source repository sits at 1,140 GitHub stars after launching in mid-2025. Its standout feature is the ability to keep a sandbox alive indefinitely by enabling a keepAlive flag, avoiding the forced timeouts most competitors impose, which matters for agents that maintain long-lived state across a multi-step task.
Vercel Sandbox is by far the youngest product here, with its public repository created in January 2026 and just 204 GitHub stars as of this week, but it is backed by the same active-CPU pricing philosophy already discussed and plugs directly into the Vercel deployment platform that a huge share of frontend and full-stack teams already use for hosting. Northflank takes the opposite tack of both, sticking with straightforward wall-clock vCPU and GB-hour billing similar to E2B and Daytona but layering in an explicit egress charge that the sandbox-first platforms mostly leave out of headline pricing.
Enterprise Compliance: SOC 2, HIPAA, and Audit Controls
Once an agent sandbox is handling anything tied to regulated data, whether that is healthcare records, financial statements, or internal source code an auditor cares about, the compliance checklist matters as much as the pricing sheet. Modal’s Enterprise tier is the most explicit of the three about what it offers here, listing SOC 2 compliance, HIPAA compatibility, audit logs, a static IP proxy for network allowlisting, role-based access control, and SSO directly on its public pricing page. That level of published detail makes Modal’s Enterprise tier straightforward to evaluate against an internal security questionnaire without a sales call.
E2B leans on its enterprise tier for the same category of controls, bundling committed usage, custom billing, multi-day and persistent sessions, and tens of thousands of concurrent sandboxes under a “talk to an engineer” process rather than self-serve documentation, which means compliance specifics (SOC 2 reports, data residency guarantees) are typically confirmed during procurement rather than published upfront. Daytona, as the youngest and most fully open-source of the three, puts the compliance question back in the customer’s hands: because the runtime can be self-hosted entirely on infrastructure you control, teams with strict data-residency or audit requirements can satisfy them by deploying inside their own VPC rather than relying on a vendor’s third-party attestations at all.
The practical takeaway is that compliance posture roughly tracks company maturity here. Modal, the oldest of the three as a company (though its sandbox product is the newest addition to a broader platform), has invested in the paperwork regulated buyers expect. E2B is close behind but gates the detail behind a sales conversation. Daytona sidesteps the question by letting you self-host, which is either the cleanest answer or the most work depending on how much your team wants to own the infrastructure itself.
Migration Guide: Moving Sandbox Workloads Between Providers
Because the core SDK patterns are so similar across vendors, migrating an agent’s code-execution backend from one sandbox provider to another is usually a matter of days, not weeks. Here is the practical sequence that keeps a migration low-risk:
- Audit your current sandbox usage pattern. Log actual session durations, concurrency peaks, and whether your workload is CPU-bound or idle-heavy (waiting on model calls). This determines whether a wall-clock or active-CPU platform will actually save money.
- Map your isolation requirements to a security tier. If you run fully untrusted third-party code, treat Firecracker-based isolation (E2B) as close to a hard requirement rather than a nice-to-have; gVisor or container-based isolation is acceptable when you control the agent logic generating the code.
- Recreate your base image or template on the new platform. All three vendors support custom Docker-based templates; export your current image’s package list and system dependencies before switching.
- Port the SDK calls. Because create/run/kill lifecycle calls are structurally similar (see the code example above), most teams can swap SDK imports and adjust method signatures in under a day for a small-to-medium codebase.
- Run a shadow-traffic test. Route a percentage of real agent tasks to the new provider while keeping the old one live, comparing cold-start latency, error rates, and actual per-task cost side by side for at least a week.
- Reconcile billing granularity. Confirm whether the new platform bills per-second (E2B, Daytona) or has a different minimum billing increment, and rebuild your cost dashboards around the new unit before fully cutting over.
- Cut over gradually by workload type. Move your least state-dependent, shortest-session tasks first; save long-running, stateful sessions for last since they carry the highest risk if the new platform’s session-duration limits behave differently than expected.
- Decommission the old provider’s persistent resources. Delete lingering volumes, snapshots, and scheduled jobs on the old platform to avoid double-billing during the transition window.
The most common migration in practice right now runs from E2B toward Daytona or vice versa, precisely because their pricing is nearly identical, which turns the decision into a pure feature and isolation-model question rather than a cost one. Migrations toward or away from Modal tend to be driven by whether a team wants to consolidate onto Modal’s broader compute platform (inference, training, and sandboxes under one bill) or deliberately split sandbox execution onto a dedicated, cheaper vendor.
Budget a rollback plan into every migration window, not just a forward path. Keep the old provider’s API keys and billing active for at least one full billing cycle after cutover, since sandbox-related bugs (a missing system package in a new base image, a subtly different filesystem permission model) tend to surface under real production load rather than in a shadow test. Teams that skip the rollback window are the ones that end up debugging a broken agent pipeline in front of customers instead of in a controlled test.
Pros and Cons of Each Platform
E2B
Pros: Firecracker microVM isolation gives the strongest security boundary of the three; purpose-built for AI agent code execution rather than general compute; claims 100,000+ teams and 94 of the Fortune 100 as customers; fully open-source core.
Cons: GPU access is locked behind custom enterprise pricing rather than a self-serve calculator; concurrency-based free tier is less generous than Daytona’s flat credit for early testing; $3,000/month enterprise minimum is a real barrier for mid-market teams scaling past self-serve limits.
Modal Sandboxes
Pros: Full self-serve GPU lineup from T4 to B300; sandboxes are one primitive in a broader platform that also handles inference, training, and scheduled jobs under a single bill; generous academic credit program (up to $10,000).
Cons: Highest base vCPU and memory rates of the three platforms; gVisor isolation is a weaker security boundary than Firecracker for fully untrusted code; core sandbox engine is closed-source, unlike E2B and Daytona; default 5-minute sandbox lifetime requires explicit configuration for longer agent tasks.
Daytona
Pros: Fastest publicly benchmarked cold start (sub-90ms); matches E2B’s low vCPU and memory rates while also offering a full self-serve GPU lineup with preemptible discounts up to ~35% off; fully open-source with the largest GitHub community of the three (71,700 stars); simplest free tier ($200 flat credit).
Cons: Youngest of the three companies (founded 2024), so enterprise-scale reference customers and long-term uptime track record are less established; container-based isolation is a step below Firecracker’s microVM boundary for teams with the strictest untrusted-code requirements; no published enterprise-minimum tier for large-scale procurement comparison.
Which Sandbox Fits Your Use Case
The right platform depends heavily on what your agent actually does and who is generating the code it runs. Here is how the decision typically shakes out across common scenarios.
- Consumer-facing agent running fully untrusted, user-submitted code: E2B’s Firecracker microVM isolation is the safer default when you cannot vouch for the code’s origin at all.
- Enterprise coding assistant already using Modal for inference or fine-tuning: Consolidating sandboxes onto Modal keeps compute, GPU inference, and agent code execution on one bill and one SDK.
- Team that needs to self-host for compliance or data-residency reasons: Daytona’s fully open-source, container-based runtime is the only one of the three built to be run entirely on your own infrastructure, matching Cursor’s own “self-hosted machines” integration path.
- Cost-sensitive startup prototyping an agent MVP: Daytona’s flat $200 credit plus matching E2B-level base rates makes it the cheapest realistic on-ramp, especially if the workload needs occasional GPU bursts.
- Chat-style agent that spends most of its time waiting on LLM responses: Vercel Sandbox or Cloudflare Sandbox’s active-CPU billing directly targets this exact idle-heavy pattern, potentially cutting costs by 3x or more versus wall-clock billing.
- Long-running, stateful research or data-analysis agent: E2B’s 24-hour continuous sessions or Daytona’s persistent, stateful infrastructure both outperform Modal’s 5-minute default lifetime for tasks that need to keep state alive across many tool calls.
- Team already deep in the Cloudflare Workers ecosystem: Cloudflare Sandbox’s keepAlive-enabled indefinite runtime and edge-network placement minimize added infrastructure complexity.
The Verdict: Our Data-Backed Pick for 2026
There is no single winner across every metric in this comparison, and the data makes that clear rather than obscuring it. On raw price-per-vCPU-hour, E2B and Daytona tie at $0.0504, both undercutting Modal’s roughly $0.071-per-vCPU-hour equivalent rate by close to 40%. On cold-start speed, Daytona’s sub-90ms claim is the most specific and fastest published figure, ahead of Modal’s “sub-second” and E2B’s unquantified “milliseconds.” On security isolation depth, E2B’s Firecracker microVMs give the strongest boundary for fully untrusted code, a real advantage Daytona’s container-based runtime and Modal’s gVisor sandboxing do not fully match.
If forced to pick one platform for a typical AI coding agent startup building in 2026, Daytona currently offers the strongest combination of price, speed, and openness: it matches E2B’s rock-bottom compute rates, beats both rivals on published cold-start latency, and is the only one of the three offering both a full self-serve GPU lineup and a fully open, self-hostable runtime. E2B remains the safer default the moment “untrusted code” means genuinely adversarial input rather than agent-generated code you at least partially control, and its 100,000+ team customer base and 94-of-Fortune-100 signup rate reflect real enterprise trust in that isolation model. Modal makes the most sense not as a standalone sandbox decision but as a consolidation play for teams already running GPU inference or training workloads on the platform, where adding sandboxes avoids a second vendor relationship entirely.
The billing model split matters more than any single vendor choice for cost-conscious teams: if your agent workload is idle-heavy (and Vercel’s own benchmark data suggests most agent workloads spend 71% to 98% of runtime waiting), an active-CPU platform like Vercel Sandbox or Cloudflare Sandbox can beat all three wall-clock-billed leaders on real-world cost, even with a higher headline hourly rate.
Frequently Asked Questions
What is a cloud sandbox for AI agents?
A cloud sandbox is an isolated, disposable compute environment (a microVM or container) that lets an AI agent execute code, run shell commands, or process files without risking the host system or production infrastructure. Platforms like E2B, Modal, and Daytona provision these environments on demand and destroy them when the task finishes.
Which is cheaper, E2B or Daytona?
Base compute pricing is identical: both charge $0.0504 per vCPU-hour and $0.0162 per GiB-hour for memory. The actual cost difference between the two depends on your free-tier usage, GPU needs (Daytona offers self-serve GPU access that E2B keeps behind enterprise pricing), and concurrency requirements rather than the headline compute rate.
Is Modal more expensive than E2B and Daytona?
Yes, on base compute rates. Modal’s Sandbox and Notebooks pricing runs about $0.1419 per physical core-hour (roughly $0.071 per vCPU-hour equivalent) and $0.0240 per GiB-hour for memory, both notably higher than E2B and Daytona’s matching $0.0504/vCPU-hour and $0.0162/GiB-hour rates. Modal’s pricing reflects its broader platform, which also includes GPU inference and training compute under the same billing system.
Which sandbox platform is fastest to start?
Daytona publishes the most specific figure, claiming sub-90ms sandbox creation from code to execution on its own homepage. Modal describes its cold starts as “sub-second” without a harder number, and E2B says its sandboxes “boot ready in milliseconds” without publishing an exact figure, though its Firecracker microVM architecture is the same technology AWS uses for fast Lambda cold starts.
Can I self-host any of these sandbox platforms?
Daytona is fully open-source and explicitly supports self-hosting, which is why Cursor lists “Cursor Self-Hosted Machines on Daytona” as a supported integration. E2B’s core sandbox runtime is also open-source. Modal’s client SDK is open, but its underlying execution engine is closed infrastructure, so Modal sandboxes cannot be fully self-hosted the way Daytona’s can.
Do E2B, Modal, and Daytona support GPU workloads?
Modal and Daytona both offer self-serve GPU pricing spanning from consumer-adjacent cards up to the Nvidia B300, with H100 and H200 rates matching closely at $3.95 and $4.54 per hour on both platforms. E2B keeps GPU compute behind its custom enterprise pricing tier rather than exposing it on a public self-serve calculator.
What is active-CPU billing and why does it matter for agents?
Active-CPU billing (used by Vercel Sandbox and Cloudflare Sandbox) charges only for CPU time the sandbox actually spends computing, rather than the full wall-clock duration it stays alive. Since agent workloads often spend 71% to 98% of their runtime waiting on model inference responses rather than computing, according to research cited in Vercel’s own documentation, active-CPU billing can cut effective costs by roughly 3x for idle-heavy agent tasks compared to wall-clock billing used by E2B, Modal, and Daytona.
How do I migrate my agent’s sandbox backend from one provider to another?
Audit your current usage pattern and isolation requirements first, then recreate your base image on the new platform, port the SDK calls (which are structurally similar across vendors), run a shadow-traffic test comparing latency and cost, and cut over gradually starting with your shortest, least state-dependent tasks before migrating long-running sessions.