# Agent 37 vs Modal

Agent 37 records 180,030 monthly visits with +24.2% month-over-month growth and Free Trial, Contact for Pricing pricing. Modal records 1,196,021 monthly visits with +30.2% growth and Freemium, Contact for Pricing pricing.

| Metric | [Agent 37](https://openaitools.app/tool/agent-37) | [Modal](https://openaitools.app/tool/modal-com) |
| :--- | :--- | :--- |
| Website URL | https://www.agent37.com | https://modal.com |
| Categories | AI Coaching, AI Agent, AI Developer Tools, Large Language Models (LLMs) | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models |
| Pricing Model | Free Trial, Contact for Pricing | Freemium, Contact for Pricing |
| Monthly Visits | 180,030 | 1,196,021 |
| MoM Growth Rate | +24.2% | +30.2% |
| Primary Market | Paraguay | United States |
| Direct Visits | 72,672 | 845,636 |
| Review Score | 4.3 | 4.8 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## Agent 37

Agent 37 is a cloud infrastructure product aimed at people who need somewhere real for AI agents to run. Instead of a chat window, every customer gets a sandbox: an isolated Linux container with CPU, memory, persistent disk, an optional public HTTPS URL, and outbound network access. The company positions itself primarily on price, claiming its sandboxes cost roughly 94% less than most competing providers and publishing a comparison table against AWS, Daytona, E2B, Modal, Vercel, and others. Billing is prepaid and metered by the minute — vCPU, memory, and disk each have their own unit rate — with no subscription fee.

Beyond raw compute, Agent 37 ships a layer of agent-specific conveniences. You can start from a ready-made harness such as Claude Code, Codex, OpenClaw, OpenCode, or Hermes, or bring your own Docker image. Agents keep their files across sleep, stop and restart cycles, and can be given cron schedules so they run unattended and simply report results. Model access can come from the platform gateway (Claude, OpenAI, Gemini, Grok) or from your own API keys, and integrations cover services such as Gmail, Slack, Notion, and GitHub through a Composio-based connector catalog, plus metered access to paid tools like web scraping and people/company data.

Security is a major part of the pitch: sandboxes are isolated with gVisor, hosts have no public hostname, ingress goes through Cloudflare with API-key authentication, and outbound traffic is restricted to the public internet. The company states it does not collect prompts, files, or terminal sessions, holds SOC 2 Type I, and is working toward Type II. It also offers startup credits and a free personal agent tier for individuals who just want an agent of their own.

**Pricing & Plans:** Prepaid, pay-as-you-go compute metered per minute with no subscription or minimum. New accounts get a $5 starter credit once a card is on file, and the company advertises up to $10,000 in credits for startups. Exact unit rates are published on the pricing page and change over time, so confirm current figures there.

- **Per-minute sandbox billing:** Pay for vCPU, memory and disk separately while the sandbox runs; when it sleeps or is deleted the compute meter stops and you are left with disk cost only.
- **Ready-made agent harnesses:** Launch Claude Code, Codex, OpenClaw, OpenCode, Hermes, Grok or your own Docker image, each in its own tenant-isolated sandbox.
- **Persistent workspace and scheduling:** Files survive sleep, restarts and weeks of inactivity, and agents can be attached to cron-style schedules so work runs unattended.
- **Networking and custom domains:** Expose individual ports publicly, attach your own domain, or keep everything behind an API key on an authenticated URL.
- **Integrations and metered tools:** Connect 1,000+ apps such as Gmail, Slack, Notion and GitHub, and buy one-off capabilities like scraping or enrichment from the same balance.

**Strengths:** Aggressively low per-minute pricing with an explicit public comparison against major sandbox providers; Persistent disk and scheduling make it suitable for real agent workloads rather than one-shot tasks; Strong isolation story (gVisor, firewalled egress, no public hostnames) plus SOC 2 Type I; Flexible model and tool sourcing: platform gateway, bring-your-own keys, or pay-per-use tools

**Limitations:** Prepaid balance model means you must top up in advance and monitor spend across many sandboxes; Persona pricing and plan details are only available after contacting the team for the white-label and enterprise tracks; Documentation and benchmark claims are largely self-published, so independent verification is limited

[Profile (Markdown)](https://openaitools.app/tool/agent-37.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 Agent 37 and Modal compare in monthly traffic?

Agent 37 has 180,030 monthly visits, while Modal has 1,196,021. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Agent 37 has a month-over-month growth rate of +24.2%; Modal has +30.2%.

### How does pricing compare?

Agent 37: Free Trial, Contact for Pricing. Prepaid, pay-as-you-go compute metered per minute with no subscription or minimum. New accounts get a $5 starter credit once a card is on file, and the company advertises up to $10,000 in credits for startups. Exact unit rates are published on the pricing page and change over time, so confirm current figures there. 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 | [Browserbase](https://openaitools.app/tool/browserbase) | 283,597 | -8% | Freemium | Cloud browsers and web APIs for AI agents | [.md](https://openaitools.app/tool/browserbase.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/agent-37-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)