# Cursor vs Modal

Cursor records 23,297,751 monthly visits with +8.4% month-over-month growth and Freemium pricing. Modal records 1,196,021 monthly visits with +30.2% growth and Freemium, Contact for Pricing pricing.

| Metric | [Cursor](https://openaitools.app/tool/cursor) | [Modal](https://openaitools.app/tool/modal-com) |
| :--- | :--- | :--- |
| Website URL | https://www.cursor.com | https://modal.com |
| Categories | AI Code Assistant, AI Developer Tools, AI Agent | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models |
| Pricing Model | Freemium | Freemium, Contact for Pricing |
| Monthly Visits | 23,297,751 | 1,196,021 |
| MoM Growth Rate | +8.4% | +30.2% |
| Primary Market | United States | United States |
| Direct Visits | 14,683,770 | 845,636 |
| Review Score | 4.7 | 4.8 |
| Saved Count | 29 | 3 |
| Supported Platforms | Website | Website |

## Cursor

Cursor's editor looks familiar to anyone who has used a modern IDE, but the center of gravity is the agent. Instead of only suggesting the next line, it can read a task description, inspect the repository, produce an implementation plan, modify many files, run commands, and report back with a summary of what changed. Developers keep control by approving plans, reviewing diffs, and stepping in wherever judgment is needed.

The product has expanded well beyond a single application. Cloud agents can run on their own machines for extended periods, Cursor ships a CLI, and integrations bring the same agent into Slack and GitHub so work can be handed off without switching tools. A model picker lets you use frontier models from several providers, or rely on automatic selection per task.

This makes Cursor a good fit for teams that want measurable velocity gains on large codebases while keeping human review in the loop. The trade-offs are mostly about cost and process: capable agents consume quota, remote indexing raises questions for security-conscious organizations, and teams need light conventions for when to delegate versus write code directly.

**Pricing & Plans:** Cursor uses a freemium model: you can start for free with limited usage, then move to paid Pro, team, and enterprise tiers that unlock higher model limits, cloud agents, and administrative controls. Exact seats and credit allowances change over time, so check cursor.com/pricing for current numbers.

- **Agent-driven code changes:** An agentic mode that plans changes, edits several files at once, runs tests, and iterates until the task is complete.
- **Cloud and parallel agents:** Background agents spin up their own environments to build, test, and demo features in parallel, then hand back results for review.
- **Tab completion and codebase context:** Fast inline suggestions plus codebase-wide semantic search and indexing so answers reflect your actual repository.
- **Multi-model flexibility:** Switch between leading frontier models from multiple providers, or let Cursor select a model automatically for each task.
- **CLI, Slack and GitHub workflows:** Run Cursor from the terminal, chat with it in Slack, and let it review or open pull requests in GitHub without leaving your workflow.

**Strengths:** Strong repository-wide context and multi-file edits; Agent runs in the editor, cloud, CLI, Slack, and GitHub; Choose from multiple frontier models in one place; Familiar IDE experience for developers coming from other editors

**Limitations:** Requires a paid plan for the most capable models and higher usage; Learning curve to use agentic workflows effectively; Heavy cloud agent usage can consume plan quota quickly; Codebase indexing sends code to remote services, which some compliance teams must review

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

Cursor has 23,297,751 monthly visits, while Modal has 1,196,021. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Cursor has a month-over-month growth rate of +8.4%; Modal has +30.2%.

### How does pricing compare?

Cursor: Freemium. Cursor uses a freemium model: you can start for free with limited usage, then move to paid Pro, team, and enterprise tiers that unlock higher model limits, cloud agents, and administrative controls. Exact seats and credit allowances change over time, so check cursor.com/pricing for current numbers. 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 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Provides powerful parallel AI coding agents via a desktop command center, but is less integrated into a traditional editor. | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 2 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Enables instant cloud-based full-stack app building from natural language, but is less suited for local offline repositories. | [.md](https://openaitools.app/tool/replit.md) |
| 3 | [VS Code](https://openaitools.app/tool/vs-code) | 7,397,370 | -3.8% | Freemium | Offers unmatched extension support and official Copilot integration, but lacks Cursor's out-of-the-box deep codebase indexing and agentic editing. | [.md](https://openaitools.app/tool/vs-code.md) |
| 4 | [Qoder](https://openaitools.app/tool/qoder) | 5,145,040 | +81.8% | Freemium, Free Trial, Free | Features advanced multi-file planning and codebase reading, but lacks Cursor's smooth chat-to-edit inline workflows. | [.md](https://openaitools.app/tool/qoder.md) |
| 5 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | Focuses on spec-driven engineering and structured agent planning, but offers less fluid ad-hoc inline code generation than Cursor. | [.md](https://openaitools.app/tool/kiro.md) |
| 6 | [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) |
| 7 | [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) |
| 8 | [Devin](https://openaitools.app/tool/devin) | 2,549,346 | +1% | Paid | Operates as a fully autonomous software engineer for complex tasks, but lacks the snappy interactive inline editing of Cursor. | [.md](https://openaitools.app/tool/devin.md) |
| 9 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | The coding agent that learns your coding taste | [.md](https://openaitools.app/tool/command-code.md) |
| 10 | [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) |

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

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