# Boxes.dev vs Modal

Boxes.dev records 552 monthly visits with 0% month-over-month growth and Freemium, Contact for Pricing pricing. Modal records 1,196,021 monthly visits with +30.2% growth and Freemium, Contact for Pricing pricing.

| Metric | [Boxes.dev](https://openaitools.app/tool/boxes-dev) | [Modal](https://openaitools.app/tool/modal-com) |
| :--- | :--- | :--- |
| Website URL | https://boxes.dev | https://modal.com |
| Categories | AI Agent, AI Developer Tools, AI Code Assistant, AI Copilot | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models |
| Pricing Model | Freemium, Contact for Pricing | Freemium, Contact for Pricing |
| Monthly Visits | 552 | 1,196,021 |
| MoM Growth Rate | 0% | +30.2% |
| Primary Market | Global | United States |
| Direct Visits | 159 | 845,636 |
| Review Score | 4.6 | 4.8 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## Boxes.dev

Boxes.dev solves a problem that appears the moment you start running more than one AI coding agent at a time: a single laptop cannot give several agents isolated filesystems, isolated ports and enough CPU at once. The product's answer is to hand each meaningful body of work its own cloud machine, called a devbox. A devbox is a real Linux computer with its own filesystem, its own processes, port forwarding back to localhost, and your development setup already installed, so several tasks can run in parallel without port collisions or resource contention.

What makes Boxes.dev distinctive is that it does not introduce a new agent harness. It runs the native Claude Code and Codex CLI you already pay for, using your existing subscription or API key — the company explicitly does not sell tokens. Onboarding is designed to be handed to the agent itself: you paste a prompt into Claude Code or Codex inside your project folder, the agent inspects your environment (env files, local databases, tooling outside the repo), drafts a setup plan, and after your approval a second agent executes the plan on a remote box. The resulting filesystem-and-memory snapshot becomes the template that all future devboxes boot from in seconds.

Operationally, agents can be started from Slack by mentioning the bot, by assigning a Linear issue, by a cron schedule, or by a single API call, and results come back where you asked for them. Devboxes sleep automatically when idle and sleeping boxes are free, which keeps parallelism from turning into runaway cost. The company positions it against Devin, Cursor and bare Claude Code/Codex setups, emphasising full cloud computers per agent, a native iOS and Android app, live session sharing for teammates, and preview URLs for running apps.

**Pricing & Plans:** Freemium with a usage-based model. A free trial includes 20 box-hours with a template box plus 3 awake devboxes and unlimited sleeping boxes. Paid tiers per user per month: Starter $19 (50 box-hours, 5 awake devboxes), Pro $99 (300 box-hours, 12 awake devboxes) and Max $249 (750 box-hours, 25 awake devboxes). Overage is $0.40 per box-hour with prepaid discounts up to 10%. Teams plans with SSO, SCIM, RBAC and audit logs are custom-priced. A box-hour equals one hour awake on a standard 4 vCPU / 8 GiB / 100 GB machine; larger sizes consume proportionally more.

- **One cloud computer per agent:** Every meaningful task gets a fresh Linux devbox with its own filesystem, processes and ports, so parallel agents never fight over resources or collide on ports.
- **Bring your own Claude Code or Codex:** Boxes.dev runs the native harness and your existing subscription or API key — no new agent framework, and no token resale.
- **Start agents from anywhere:** Kick off work by mentioning @boxes.dev in Slack, assigning a Linear issue, scheduling a cron job, or calling the REST API, with results returned in the same channel.
- **Desktop, mobile, terminal and web clients:** Read threads, approve requests, review diffs or open a terminal on any surface, including full-featured native iOS and Android apps.
- **Sleeping devboxes are free:** Idle boxes suspend automatically and are billed by the second only while awake, keeping the cost of running many agents under control.

**Strengths:** Each agent gets a genuine isolated cloud computer rather than a worktree or sandbox on your laptop; Works with your existing Claude Code or Codex subscription, so there is no separate token bill; Agents can be triggered from Slack, Linear, cron or API, which fits existing team workflows; Idle devboxes sleep for free, and billing is per second, so parallel experiments stay affordable

**Limitations:** Usage is metered in box-hours, so heavy or long-running workloads can exceed included allowances; The free trial's 20 box-hours is enough for evaluation but not sustained daily work; Most powerful features such as SSO, audit logs and dedicated deployment sit behind custom-priced Teams plans

[Profile (Markdown)](https://openaitools.app/tool/boxes-dev.md)

## Modal

Modal provides cloud infrastructure aimed squarely at AI and data teams. The core idea is that developers write ordinary Python, decorate functions or define container images, and Modal takes care of the underlying compute: scheduling containers, attaching GPUs, autoscaling from zero and back, and exposing endpoints over HTTPS. Infrastructure concerns like networking, storage, and capacity management are abstracted away, so a workload that runs on a laptop can run the same way across a fleet.

The platform is organised around a few primitives. Functions handle serverless compute and can fan out across thousands of GPUs for parallel or batch work. Sandboxes provide isolated environments for coding agents, reinforcement learning rollouts, and other untrusted code, with sub-second start times and support for very high concurrency. Training covers fine-tuning, reinforcement learning, and evaluation on single or multi-node GPU capacity. On the inference side, Modal offers OpenAI-compatible endpoints for open-weight models and lets teams deploy custom stacks and tune endpoints to match their traffic shape.

Underneath, Modal runs its own container runtime and image system designed for large images and fast GPU scheduling, along with a distributed filesystem for fast access to model weights and datasets. Capacity is drawn from a multi-cloud pool spanning many regions, with automatic routing to available hardware and continuous fleet health checks. Billing is usage-based, charged by the second while code runs, with granular cost controls. The company offers a free starting allowance and scalable paid usage, and markets to enterprises with SOC 2 Type II compliance and HIPAA readiness.

**Pricing & Plans:** Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales.

- **Serverless Python functions:** Write plain Python, attach GPUs or containers, and deploy HTTPS endpoints that autoscale with traffic and scale to zero when idle.
- **Sandboxes for agents:** Launch isolated, secure environments for coding agents, RL rollouts, and untrusted code, with sub-second starts and support for over a million concurrent sandboxes.
- **Training and fine-tuning on demand:** Run fine-tunes, LoRA, full training, and reinforcement learning on elastic single or multi-node GPU capacity with no quota requests.
- **Model inference and endpoints:** Serve LLMs and image, video, or audio models through OpenAI-compatible endpoints, or bring your own inference stack and let Modal handle the infrastructure.
- **Fast storage and multi-cloud capacity:** A distributed filesystem loads large images and model weights at full bandwidth, while a pool spanning 20+ clouds routes workloads to the right hardware and region.

**Strengths:** Python-native developer experience with almost no infrastructure boilerplate; Scales from zero to thousands of GPUs with per-second, usage-based billing; Sandboxes are well suited to agentic and untrusted-code workloads; Multi-cloud capacity spans many regions with out-of-the-box observability and enterprise compliance

**Limitations:** Billed by real usage, so heavy GPU workloads can get expensive quickly without cost controls; Some advanced inference and enterprise features require working with the team rather than self-serve signup; The serverless model can feel less familiar to teams used to managing their own VMs or Kubernetes clusters

[Profile (Markdown)](https://openaitools.app/tool/modal-com.md)

## Frequently Asked Questions

### How do Boxes.dev and Modal compare in monthly traffic?

Boxes.dev has 552 monthly visits, while Modal has 1,196,021. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Boxes.dev has a month-over-month growth rate of 0%; Modal has +30.2%.

### How does pricing compare?

Boxes.dev: Freemium, Contact for Pricing. Freemium with a usage-based model. A free trial includes 20 box-hours with a template box plus 3 awake devboxes and unlimited sleeping boxes. Paid tiers per user per month: Starter $19 (50 box-hours, 5 awake devboxes), Pro $99 (300 box-hours, 12 awake devboxes) and Max $249 (750 box-hours, 25 awake devboxes). Overage is $0.40 per box-hour with prepaid discounts up to 10%. Teams plans with SSO, SCIM, RBAC and audit logs are custom-priced. A box-hour equals one hour awake on a standard 4 vCPU / 8 GiB / 100 GB machine; larger sizes consume proportionally more. Modal: Freemium, Contact for Pricing. Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Extremely fast serverless execution optimized specifically for generative media models rather than general Python code. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 2 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | Provides flexible GPU infrastructure and serverless endpoints, though less focused on pure Python function decorators. | [.md](https://openaitools.app/tool/runpod.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Focuses heavily on managed open-source model inference APIs rather than arbitrary custom container execution. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) |
| 5 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Delivers robust enterprise GPU clusters and managed inference, requiring more infrastructure setup than Modal's serverless approach. | [.md](https://openaitools.app/tool/nebius.md) |
| 6 | [MyClaw](https://openaitools.app/tool/myclaw) | 990,387 | -23.8% | Paid | Your always-on AI teammate, hosted and managed for you | [.md](https://openaitools.app/tool/myclaw.md) |
| 7 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Easier model packaging via Cog, but offers less arbitrary serverless Python container execution than Modal. | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 8 | [BLACKBOX.AI](https://openaitools.app/tool/blackbox-ai-1) | 622,975 | -27.2% | Free Trial, Paid | High-trust frontier inference, encrypted end to end | [.md](https://openaitools.app/tool/blackbox-ai-1.md) |
| 9 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Low-cost serverless inference APIs for 200+ open models | [.md](https://openaitools.app/tool/deep-infra.md) |
| 10 | [Lightning AI](https://openaitools.app/tool/lightning-ai) | 272,562 | -12.3% | Freemium, Paid, Contact for Pricing | Provides integrated development studios and training workflows, but with less granular serverless function orchestration. | [.md](https://openaitools.app/tool/lightning-ai.md) |

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Source: https://openaitools.app/compare/boxes-dev-vs-modal-com

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