# Modal vs Surf

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. Surf records 116,252 monthly visits with -18.9% growth and Free, Pro, Max, API credits pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [Surf](https://openaitools.app/tool/surf) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://asksurf.ai |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | AI Crypto, AI Copywriting, AI Creative Writing, AI For Finance |
| Pricing Model | Freemium, Contact for Pricing | Free, Pro, Max, API credits |
| Monthly Visits | 1,196,021 | 116,252 |
| MoM Growth Rate | +30.2% | -18.9% |
| Primary Market | United States | South Korea |
| Direct Visits | 845,636 | 88,813 |
| Review Score | 4.8 | 4.2 |
| 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)

## Surf

Surf positions itself as the intelligence layer for open financial markets. The core experience is conversational: you ask a question about a token, a protocol, a company, or a market move, and the system returns an answer grounded in live market feeds, onchain events, SEC filings, and news, with the underlying sources cited so you can verify the reasoning. Around that chat experience, Surf layers a set of named products such as CryptoPulse and Cryptopedia, plus an in-browser Studio for building analysis.

What separates Surf from a generic chatbot is the data plumbing behind it. The company says its datasets are kept current, structured, and unified across fragmented sources before any model touches them, which matters because much of crypto data is scattered across exchanges, chains, and regional venues. Surf also highlights its coverage of hard-to-source markets, including multilingual Asian centralized exchange data, as a differentiator for institutional research.

For builders, Surf exposes the same intelligence through an API, an MCP server, a CLI, and reusable agent skills, so a research desk can embed crypto and equities context directly into its own agents and internal tools. Surf states it is used by more than 500,000 users across 700-plus organizations, and its developer layer is documented separately for teams that want to integrate rather than click. Surf is operated by Cybertino Inc. and is not affiliated with any exchange or broker; outputs are research assistance, not investment advice.

**Pricing & Plans:** Surf publishes a tiered pricing page covering Free, Pro, and Max plans alongside API credit packages, with annual billing options and Studio credits bundled at higher tiers. Exact figures change over time, so check the official pricing page before purchasing.

- **Cited conversational analyst:** Ask questions about tokens, protocols, or equities in plain language and receive answers backed by visible sources rather than unsupported assertions.
- **Unified multi-source data:** Market prices, onchain events, SEC filings, and news are normalized into one structured layer so the model reasons over consistent inputs.
- **Developer integration layer:** API, MCP server, CLI, and agent skills let teams wire Surf intelligence into their own agents, dashboards, and internal research workflows.
- **CryptoPulse and Cryptopedia products:** Purpose-built modules for tracking market-moving developments and for looking up structured context on projects and assets.
- **Coverage of hard-to-source markets:** Surf emphasizes structured multilingual data from Asian centralized exchanges, a category many Western data vendors handle poorly.

**Strengths:** Answers come with citations, which makes verification practical rather than a leap of faith.; Covers both onchain crypto data and US equities in a single interface, useful for cross-asset research.; Genuine developer surface area: API, MCP, CLI, and agent skills, not just a chat box.; Institutional framing and published case studies signal real deployments with funds and exchanges.

**Limitations:** It is a research and intelligence tool, not a trading or execution venue, so it will not place orders for you.; Deeper data access and higher usage tiers sit behind paid plans and API credits.; Coverage quality varies by asset and venue; niche or thinly traded markets may return thinner answers.

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

## Frequently Asked Questions

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

Modal has 1,196,021 monthly visits, while Surf has 116,252. 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%; Surf has -18.9%.

### 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. Surf: Free, Pro, Max, API credits. Surf publishes a tiered pricing page covering Free, Pro, and Max plans alongside API credit packages, with annual billing options and Studio credits bundled at higher tiers. Exact figures change over time, so check the official pricing page before purchasing.

## 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 | [LuxAlgo](https://openaitools.app/tool/luxalgo) | 985,376 | +9.8% | Freemium, Free Trial, Paid | AI trading indicators, real-trade orderflow and charts in one platform | [.md](https://openaitools.app/tool/luxalgo.md) |
| 7 | [MacroMicro](https://openaitools.app/tool/macromicro) | 928,258 | +22.5% | Freemium, Paid | Global macro data and AI research platform for smarter economic decisions | [.md](https://openaitools.app/tool/macromicro.md) |
| 8 | [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) |
| 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/modal-com-vs-surf

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