# Modal vs Step1

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. Step1 records 0 monthly visits with — growth and Free pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [Step1](https://openaitools.app/tool/step1) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://chromewebstore.google.com/detail/step1/aghjfakpkmlhmppddoingmepnlignjgd |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | Web3, Graph AI, AI For Data Analytics |
| Pricing Model | Freemium, Contact for Pricing | Free |
| Monthly Visits | 1,196,021 | 0 |
| MoM Growth Rate | +30.2% | — |
| Primary Market | United States | Global |
| Direct Visits | 845,636 | 0 |
| Review Score | 4.8 | 1.8 |
| Saved Count | 3 | 1 |
| Supported Platforms | Website | Browser Extension |

## 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)

## Step1

Step1 is a Chrome extension that describes itself as an AI-based Web3 user graph. Rather than judging accounts on raw activity alone, it analyses behaviour and reputation signals to build a profile of each participant, then estimates how much that user deserves from reward and incentive programs.

The problem it targets is a familiar one in Web3: distinguishing genuine contributors from freeloaders and automated farming accounts. Incentive campaigns are frequently drained by bots and low-effort wallets, while real users are under-rewarded. Step1's stated goal is to shift that balance by scoring quality of engagement and proposing economic models that reward it.

It is a lightweight extension installed from the Chrome Web Store, offered by Step1 official. It collects web history to perform its analysis and declares that this data is not sold to third parties or used for creditworthiness decisions. Note that the listing has not been updated since mid-2023 and has a small user base, so it is best treated as an early-stage Web3 experiment rather than a mainstream productivity tool.

**Pricing & Plans:** Free to install from the Chrome Web Store. No paid tier is listed.

- **AI-driven user graph:** AI and big-data methods combine on-chain signals into a picture of who a Web3 user is and how they behave.
- **Reward optimisation:** Scores and compares participants so that genuine contributors stand out from freeloaders and farm accounts.
- **Fair incentive models:** Aimed at making incentive programs fairer by rewarding high-quality participation rather than volume.
- **Simple Chrome extension:** Installs from the Chrome Web Store and runs alongside your normal Web3 browsing.
- **Cross-platform analysis:** Analyses user activity and reputation across Web3 rather than relying on a single protocol's data.

**Strengths:** Free Chrome extension with no paid tier; Targets a real Web3 problem: separating real users from freeloaders; Runs in the browser with minimal setup; Declares data is not sold to third parties

**Limitations:** Very small install base (roughly 73 users), so there is little community or documentation support; Extension has not been updated since July 2023, which is a long gap for a Web3 product; Requires a Chrome-based browser and an active Web3 wallet to be useful; Claims and metrics around reward optimization are hard to verify independently

[Profile (Markdown)](https://openaitools.app/tool/step1.md)

## Frequently Asked Questions

### How do Modal and Step1 compare in monthly traffic?

Modal has 1,196,021 monthly visits, while Step1 has 0. 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%; Step1 has —.

### 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. Step1: Free. Free to install from the Chrome Web Store. No paid tier is listed.

## 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 | [Thunderbit](https://openaitools.app/tool/thunderbit) | 391,726 | -21.9% | Freemium, Contact for Pricing | Agentic web scraper that turns any page into ready-to-use data | [.md](https://openaitools.app/tool/thunderbit.md) |
| 9 | [BlockSurvey](https://openaitools.app/tool/block-survey) | 302,480 | -14.9% | Freemium, Paid | Privacy-first AI surveys with end-to-end encrypted responses | [.md](https://openaitools.app/tool/block-survey.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-step1

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