# Modal vs TinyFish

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. TinyFish records 1,124,952 monthly visits with +199.7% growth and Freemium, Contact for Pricing pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [TinyFish](https://openaitools.app/tool/tinyfish) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://www.tinyfish.ai |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | AI API, AI Agent, AI Browsers, AI Web Scraping |
| Pricing Model | Freemium, Contact for Pricing | Freemium, Contact for Pricing |
| Monthly Visits | 1,196,021 | 1,124,952 |
| MoM Growth Rate | +30.2% | +199.7% |
| Primary Market | United States | United States |
| Direct Visits | 845,636 | 401,320 |
| Review Score | 4.8 | 4.4 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## 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)

## TinyFish

TinyFish describes itself as the web stack for AI agents: a serverless platform that lets agents search, extract, browse and act on the live web. Rather than a single point tool, it groups several products under one API key and one wallet — TinySearch for fresh, browser-rendered web search returned as structured JSON, TinyFetch for turning any URL into clean Markdown, JSON or HTML, TinyBrowser for real cloud browser sessions driven with Playwright, Puppeteer or CDP, TinyAgent for plain-English multi-step automation, and TinyMonitor for alerts when a page or topic changes.

The platform is aimed at production workloads rather than demos. It emphasises reliability and structure, real browser rendering instead of thin cached results, persistent session state, and the ability to handle authenticated flows. Supporting pieces include TinyVault for connecting 1Password or Bitwarden so credentials stay hidden from the model, TinyProfiles for saving signed-in sessions, and step-by-step run recordings so you can audit exactly what an agent did on a site.

TinyFish is designed to slot into existing developer stacks. It is promoted for use alongside Claude, ChatGPT, Cursor and other agent runtimes, offers an MCP connection, Python SDK and CLI, and is positioned for workloads such as price monitoring, record enrichment, deep research and supplier-portal automation. Entry-level search and fetch calls are free, while agent steps, browser minutes and monitoring checks are metered on a pay-as-you-go wallet basis, with an enterprise track for larger deployments.

**Pricing & Plans:** Freemium and usage-based. Search and basic fetch are free with no search fee, while TinyAgent is billed per step, TinyBrowser per minute and TinyMonitor per check from a prepaid wallet; new users can claim a small free credit. Enterprise plans are custom — confirm current rates on the pricing page.

- **TinySearch:** Real browser-rendered search that returns structured JSON with fresh, uncached results from the live web, with no search fee.
- **TinyFetch:** Convert any URL into token-efficient Markdown, JSON or HTML, stripping navigation bars, cookie banners and ads before they reach your model.
- **TinyBrowser:** Full cloud browser sessions with persistent state that you drive with Playwright, Puppeteer or the Chrome DevTools Protocol.
- **TinyAgent and TinyMonitor:** Give an agent a goal in plain English and it navigates, fills forms and returns structured results; monitoring fires alerts when a tracked page or topic changes.
- **One API key, one wallet:** Search, fetch, browser and agent products share single authentication and billing, plus an MCP connection, Python SDK and CLI.

**Strengths:** Consolidates search, fetch, browsing, agent automation and monitoring behind one API key and wallet; Browser-rendered, uncached results rather than thin cached snippets; Free search and fetch tiers lower the cost per task compared to several paid search APIs; Strong developer ergonomics: MCP, Python SDK, CLI and integrations with popular agent runtimes

**Limitations:** Agent steps, browser minutes and monitoring checks are metered, so heavy automation volumes add up; More complex auth flows still carry the usual fragility of driving third-party sites; It is infrastructure for builders — not aimed at non-technical end users

[Profile (Markdown)](https://openaitools.app/tool/tinyfish.md)

## Frequently Asked Questions

### How do Modal and TinyFish compare in monthly traffic?

Modal has 1,196,021 monthly visits, while TinyFish has 1,124,952. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Modal has a month-over-month growth rate of +30.2%; TinyFish has +199.7%.

### How does pricing compare?

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. TinyFish: Freemium, Contact for Pricing. Freemium and usage-based. Search and basic fetch are free with no search fee, while TinyAgent is billed per step, TinyBrowser per minute and TinyMonitor per check from a prepaid wallet; new users can claim a small free credit. Enterprise plans are custom — confirm current rates on the pricing page.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Apify](https://openaitools.app/tool/apify) | 4,147,762 | +3.9% | Freemium, Contact for Pricing | Features a massive marketplace of ready-to-run scrapers, though it operates more as an actor platform than a raw API. | [.md](https://openaitools.app/tool/apify.md) |
| 2 | [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) |
| 3 | [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) |
| 4 | [Firecrawl](https://openaitools.app/tool/firecrawl-dev) | 1,828,239 | -1.9% | Freemium, Contact for Pricing | Provides cleaner LLM-ready markdown conversion and crawling, but lacks TinyFish's interactive cloud browser automation capabilities. | [.md](https://openaitools.app/tool/firecrawl-dev.md) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Offers superior neural web search designed for AI agents, but provides less comprehensive raw browser automation tools. | [.md](https://openaitools.app/tool/exa-ai.md) |
| 9 | [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) |
| 10 | [Bright Data](https://openaitools.app/tool/bright-data) | 802,752 | -5.9% | Free Trial, Paid | Delivers enterprise-grade proxy networks and scraping infrastructure, but involves a steeper learning curve than TinyFish. | [.md](https://openaitools.app/tool/bright-data.md) |

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

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