# exe.dev vs Modal

exe.dev records 60,103 monthly visits with -26.4% month-over-month growth and Freemium, Paid pricing. Modal records 1,196,021 monthly visits with +30.2% growth and Freemium, Contact for Pricing pricing.

| Metric | [exe.dev](https://openaitools.app/tool/exe-dev) | [Modal](https://openaitools.app/tool/modal-com) |
| :--- | :--- | :--- |
| Website URL | https://exe.dev | https://modal.com |
| Categories | AI Developer Tools, AI Agent, AI App Builder | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models |
| Pricing Model | Freemium, Paid | Freemium, Contact for Pricing |
| Monthly Visits | 60,103 | 1,196,021 |
| MoM Growth Rate | -26.4% | +30.2% |
| Primary Market | United States | United States |
| Direct Visits | 40,778 | 845,636 |
| Review Score | 4.3 | 4.8 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## exe.dev

exe.dev is a Bay Area-built cloud computing platform aimed at a specific modern problem: developers increasingly run coding agents that need their own machines, and those machines need to behave like real servers rather than ephemeral containers. The product lets you buy a pool of vCPU and memory and then spin up as many VMs as you want inside it, fork them quickly, and SSH straight in with root access on Ubuntu-compatible images with apt and systemd available.

The platform positions itself as production-ready infrastructure, not just a scratchpad. VMs get fast, persistent, replicated NVMe storage, can be exposed publicly over HTTPS on *.exe.xyz subdomains, and can also host consumer-facing apps with authentication and secret management handled by the platform. A built-in LLM gateway can attach provider keys (OpenAI, Anthropic, xAI, Fireworks, Deepgram and others) to your machines so agents have model access without extra plumbing.

Pricing is transparent and published: a Personal plan starts at $15 per month for a 2 vCPU / 4 GiB pool, larger Personal pools scale up to 16 vCPU / 32 GiB, a Work plan adds SSO, unlimited users, larger VM counts per pool and hourly or monthly scaling, and Enterprise covers dedicated hardware and VPC connectivity. A seven-day trial is offered, and you can get started free through ChatGPT.

**Pricing & Plans:** Personal plans start at $15/month for 2 vCPU and 4 GiB of pooled memory, scaling to $155/month for 16 vCPU and 32 GiB. The Work plan is $75/month regular (intro $37.50/month for the first three months) with SSO, unlimited users and extra pool resources billed hourly. Enterprise pricing is custom, and a 7-day free trial is available.

- **Real persistent VMs, not sandboxes:** SSH into Ubuntu-compatible machines with root access, apt and systemd, then keep them running as long-lived servers instead of short-lived containers.
- **Pooled CPU and memory:** Buy vCPU and RAM once, then create as many VMs as you need inside the pool and fork them quickly for parallel agent work.
- **HTTPS and public hosting built in:** Expose internal apps privately or publish them publicly on *.exe.xyz subdomains, with auth and secret management handled for you.
- **LLM gateway for agents:** Attach OpenAI, Anthropic, xAI, Fireworks or Deepgram keys so agents running on your VMs have model access configured automatically.
- **Global regions and durable storage:** Deploy VMs across regions with fast, persistent and replicated NVMe storage, plus team features like SSO on higher-tier plans.

**Strengths:** Real root-access Linux VMs with persistent storage rather than ephemeral sandboxes; Transparent published pricing with a free trial and hourly scaling option; Built-in HTTPS exposure, auth, secrets and an LLM gateway for agent workloads; Fast VM creation and forking, useful for parallel agent tasks and preview environments

**Limitations:** Priced per resource pool, so heavy multi-VM usage can add up quickly; No free tier beyond the trial and ChatGPT-based signup path; Enterprise features such as VPC and dedicated hardware require contacting sales

[Profile (Markdown)](https://openaitools.app/tool/exe-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 exe.dev and Modal compare in monthly traffic?

exe.dev has 60,103 monthly visits, while Modal has 1,196,021. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

exe.dev has a month-over-month growth rate of -26.4%; Modal has +30.2%.

### How does pricing compare?

exe.dev: Freemium, Paid. Personal plans start at $15/month for 2 vCPU and 4 GiB of pooled memory, scaling to $155/month for 16 vCPU and 32 GiB. The Work plan is $75/month regular (intro $37.50/month for the first three months) with SSO, unlimited users and extra pool resources billed hourly. Enterprise pricing is custom, and a 7-day free trial is available. 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 | [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) |
| 7 | [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) |
| 8 | [Zeabur](https://openaitools.app/tool/zeabur) | 275,642 | -15.9% | Free Trial, Paid | AI DevOps engineer that deploys and scales your apps | [.md](https://openaitools.app/tool/zeabur.md) |
| 9 | [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) |
| 10 | [Novita AI](https://openaitools.app/tool/novita-ai) | 271,371 | -10.4% | Paid | Combines ready-to-use model APIs and serverless GPU jobs, with simpler API consumption than custom Modal apps. | [.md](https://openaitools.app/tool/novita-ai.md) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)