# Gitmore vs Scale

Gitmore records 1,021 monthly visits with -62.4% month-over-month growth and Freemium pricing. Scale records 616,915 monthly visits with +12.5% growth and Contact for Pricing pricing.

| Metric | [Gitmore](https://openaitools.app/tool/gitmore) | [Scale](https://openaitools.app/tool/scale) |
| :--- | :--- | :--- |
| Website URL | https://gitmore.io | https://scale.com |
| Categories | AI Report Generator, AI GitHub, AI Developer Tools, AI Agent | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Agent |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 1,021 | 616,915 |
| MoM Growth Rate | -62.4% | +12.5% |
| Primary Market | United States | United States |
| Direct Visits | 401 | 191,408 |
| Review Score | 4.1 | 4.7 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## Gitmore

Gitmore connects to your repositories through OAuth or a manual webhook and only reads event metadata such as titles, messages, authors and timestamps — it never reads your source code. From that stream it assembles reports: what merged, who contributed, which pull requests are stalling, and which tasks from Notion, Linear, Jira, Asana, ClickUp or Trello actually shipped. Reports can be delivered on a schedule to Slack or email in 11 languages.

A distinctive feature is the AI agent ledger. Every pull request and commit is attributed to a human or to an agent such as Claude Code, GitHub Copilot, Cursor, Codex, Devin or Jules, using co-authored-by trailers, bot accounts and branch prefixes. Teams can then see AI share of merged work, merge rates per agent, and pull requests generated by agents that are still waiting on a human reviewer.

Beyond reports, Gitmore offers password-protected client portals with your branding, public changelogs and embeddable widgets, proactive alerts for stale or unreviewed pull requests, plus an MCP server and API. Setup takes a couple of minutes, no YAML or configuration files required, and connecting a new repository imports the previous 30 days of activity so the first report is not empty.

**Pricing & Plans:** Freemium with no per-seat pricing. The Free plan is free forever and includes unlimited repositories, unlimited automations and 2 AI credits per month. Pro is $8.99/month with 100 AI credits, a Slack AI agent and priority support; Enterprise is $30/month with 500 AI credits, custom-prompt reports and custom integrations. Annual billing saves roughly 17%. One AI credit equals one chat answer, scheduled report, report preview or client-portal summary.

- **Scheduled AI reports:** Daily, weekly or monthly summaries of merged work and in-flight pull requests, generated by AI and delivered to Slack or email on your schedule and in your timezone.
- **AI agent ledger:** Attributes each commit and pull request to a person or to an agent like Claude Code, Copilot, Cursor or Codex, so you can track AI share, merge rates and agent PRs waiting on review.
- **Client portals and changelogs:** Branded, password-protected update pages written in plain language for clients, investors and executives, plus a public changelog and embeddable widget built from merged work.
- **Planned vs shipped task matching:** Links tasks from Notion, Linear, Jira, Asana, ClickUp and Trello to the pull requests that implement them, flagging work that merged while the task stayed open.
- **Proactive alerts and metadata-only access:** Notifies you about stale PRs, merges without reviewers, AI-share spikes and dormant repositories, while reading only event metadata rather than your source code.

**Strengths:** Setup takes about two minutes with OAuth or a manual webhook, and no configuration files; Reads only event metadata, never source code, and supports self-hosted GitLab; Aggregates GitHub, GitLab and Bitbucket into one consistent reporting view; No per-seat pricing, so reporting cost does not grow with team size

**Limitations:** AI-dependent features are metered by credits, which can run out on busy teams; Some advanced capabilities such as custom-prompt reports are locked to the Enterprise tier; Best value requires adopting Slack or email delivery as part of your workflow

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

## Scale

Scale positions itself as the infrastructure layer for reliable AI - the data, evaluations and deployment support that sit underneath working systems rather than a single consumer-facing model. Founded in 2016, it built its reputation supplying training data and labelling capacity to the autonomous vehicle and then the generative AI wave, and now describes itself as powering a large share of the world's leading generative AI model builders.

The offering spans three broad areas. Data covers sourcing and expert annotation for training and fine-tuning, including RLHF-style preference data. Evaluation covers public leaderboards and private benchmarks used to measure model capability and safety. Applications covers the build-and-deploy side: identifying a viable use case, constructing the system and owning the outcome, with agentic AI for enterprise operations.

Scale's customer list leans heavily enterprise and public sector, including large technology companies, healthcare providers, media organisations, energy companies and government defence programmes. Because engagements are scoped rather than self-serve, pricing is arranged through a sales process, and the platform is aimed at organisations with serious data volume, strict compliance needs and a mandate to put AI into production.

**Pricing & Plans:** Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity.

- **Data Engine:** Human-sourced and expert-annotated training data, including specialist contributors with advanced degrees, at the quality bar frontier labs require.
- **Model evaluation & benchmarks:** Public leaderboards plus private benchmarks that test model capability and safety for the most ambitious AI developers.
- **Enterprise AI applications:** Platform and services teams that pick the right use case, build the system and take ownership of the outcome.
- **Agentic deployment:** Agentic systems designed around measurable business outcomes across private equity portfolios, energy and healthcare operations.
- **Domain expertise:** Programmes spanning healthcare, robotics, defence, public sector and enterprise with human oversight built in.

**Strengths:** Backed by a long track record in training data and model evaluation.; End-to-end coverage from data sourcing through to deployed systems.; Experienced with regulated, classified and safety-critical environments.; Benchmarks and leaderboards give an independent view of model capability.

**Limitations:** No public pricing or self-serve signup - you need a sales conversation to get started.; Enterprise-scale contracting and data operations require real internal commitment.; Overkill for small teams with modest data or evaluation needs.; Some projects involve sensitive government and defence work with strict compliance overhead.

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

## Frequently Asked Questions

### How do Gitmore and Scale compare in monthly traffic?

Gitmore has 1,021 monthly visits, while Scale has 616,915. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Gitmore has a month-over-month growth rate of -62.4%; Scale has +12.5%.

### How does pricing compare?

Gitmore: Freemium. Freemium with no per-seat pricing. The Free plan is free forever and includes unlimited repositories, unlimited automations and 2 AI credits per month. Pro is $8.99/month with 100 AI credits, a Slack AI agent and priority support; Enterprise is $30/month with 500 AI credits, custom-prompt reports and custom integrations. Annual billing saves roughly 17%. One AI credit equals one chat answer, scheduled report, report preview or client-portal summary. Scale: Contact for Pricing. Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Streamlines computer vision dataset annotation and model training, but is less suited for general LLM text tasks. | [.md](https://openaitools.app/tool/roboflow.md) |
| 2 | [DeepWiki by Cognition](https://openaitools.app/tool/deepwiki-by-congnition) | 879,539 | -3.1% | Free | AI-generated wikis and chat for any public GitHub repository | [.md](https://openaitools.app/tool/deepwiki-by-congnition.md) |
| 3 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Focuses heavily on enterprise data labeling and training data curation, similar to Scale's core annotation business. | [.md](https://openaitools.app/tool/labelbox.md) |
| 4 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Provides enterprise multimodal annotation and data management tools, but with less emphasis on managed labeling workforces. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 5 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 6 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Focuses on post-deployment LLM observability and evaluation rather than training data generation or annotation. | [.md](https://openaitools.app/tool/arize-com.md) |
| 7 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | Specializes in data infrastructure and annotation specifically tailored for computer vision and physical AI workloads. | [.md](https://openaitools.app/tool/encord-com.md) |
| 8 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Offers a flexible open-source alternative for custom data labeling and human-in-the-loop annotation pipelines. | [.md](https://openaitools.app/tool/label-studio.md) |
| 9 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |
| 10 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Provides LLM evaluation and testing workflows, but lacks Scale's massive human-in-the-loop data labeling services. | [.md](https://openaitools.app/tool/maxim-ai.md) |

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Source: https://openaitools.app/compare/gitmore-vs-scale

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