# NOAN vs Scale

NOAN records 3,663 monthly visits with -15.7% month-over-month growth and Paid pricing. Scale records 616,915 monthly visits with +12.5% growth and Contact for Pricing pricing.

| Metric | [NOAN](https://openaitools.app/tool/noan) | [Scale](https://openaitools.app/tool/scale) |
| :--- | :--- | :--- |
| Website URL | https://getnoan.com | https://scale.com |
| Categories | AI Assistant, AI Knowledge Management, Large Language Models (LLMs), AI Marketing | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Agent |
| Pricing Model | Paid | Contact for Pricing |
| Monthly Visits | 3,663 | 616,915 |
| MoM Growth Rate | -15.7% | +12.5% |
| Primary Market | United States | United States |
| Direct Visits | 1,450 | 191,408 |
| Review Score | 4.4 | 4.7 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## NOAN

NOAN describes itself as fact infrastructure for agentic companies. The problem it addresses is a familiar one for teams running AI agents: the price lives in fourteen system prompts, three of which are wrong; retrieval surfaces last year's deck next to this year's; memory recalls what someone said on a call rather than what was decided. NOAN's answer is to keep your company's verified facts in one place and have every agent, app, and site read them live from a single API.

Technically, NOAN exposes a plain REST API over api.getnoan.com/v1 and an MCP server at mcp.getnoan.com/mcp. You create a project, then issue a key for each agent, app, or site, scoped to the stacks it needs. A single GET returns the latest verified version of a fact block, along with who verified it and its sources. Agents can write back, posting new versions that supersede the previous ones while keeping the lineage — and if review-on-write is enabled, those writes wait for a person to approve them before anything can read them. Around that core, NOAN handles the surrounding mechanics: classification, permissions, partner sharing, and per-key activity auditing.

The platform also ships the workspace pieces agents need day to day — a shared task board for people and agents, contacts with history and open work, notes linked to facts, and reusable assets and templates. It integrates with Claude, ChatGPT, Cursor, and other MCP clients, and supports multiple model providers because the model is a configuration choice. Beyond the tooling, NOAN publishes a startup program and grant, plus material for agencies that want to run a separate fact base per client.

**Pricing & Plans:** Paid, with a free starting point: you can start free and scale when you ship, and pricing is presented as live/usage-based in the app. API access, MCP, versioned facts, review-on-write, and scoped keys are part of the platform, with higher tiers aimed at teams and agencies running the facts layer in production. A startup program and grant are also offered.

- **Verified, versioned facts:** Each fact keeps its date, sources, and the person who verified it, so nothing is silently overwritten and agents can show the receipt behind an answer.
- **Scoped keys and permissions:** Scope one key per agent, app, or site and limit it to the stacks it needs, so the right keys read the right facts and nothing else.
- **Review on write:** Turn review on for a project or a single key so any write an agent proposes waits for a human to verify it before it can be read.
- **Time Travel:** See your entire fact base as it stood on any past date, so you can answer what was true when a decision was made.
- **Activity on the record:** Every agent action lands on one timeline, marked by the key that made it, giving you auditability across agents, apps, and sites.

**Strengths:** Solves a real and specific agent problem — grounding — instead of adding another generic chatbot; Version history, sources, and named verification make outputs auditable and defensible; Model-agnostic and accessible over both REST and MCP, so it fits many stacks; Review-on-write lets agents propose updates without letting them silently change pricing or policy

**Limitations:** The product is a new category (fact infrastructure), so new users need to absorb unfamiliar concepts before they see value; Value depends on teams actually maintaining facts, so adoption requires some process discipline; Pricing is presented as dynamic and depends on the scale you ship at; It complements rather than replaces memory or RAG layers, so it is another integration to wire up

[Profile (Markdown)](https://openaitools.app/tool/noan.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 NOAN and Scale compare in monthly traffic?

NOAN has 3,663 monthly visits, while Scale has 616,915. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

NOAN has a month-over-month growth rate of -15.7%; Scale has +12.5%.

### How does pricing compare?

NOAN: Paid. Paid, with a free starting point: you can start free and scale when you ship, and pricing is presented as live/usage-based in the app. API access, MCP, versioned facts, review-on-write, and scoped keys are part of the platform, with higher tiers aimed at teams and agencies running the facts layer in production. A startup program and grant are also offered. 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 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | The AI workspace where teams and agents get work done together | [.md](https://openaitools.app/tool/t.md) |
| 2 | [Glean](https://openaitools.app/tool/glean) | 3,301,875 | +0.1% | Contact for Pricing | Enterprise AI grounded in your company's own knowledge | [.md](https://openaitools.app/tool/glean.md) |
| 3 | [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) |
| 4 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Web search API and crawler built for AI agents | [.md](https://openaitools.app/tool/exa-ai.md) |
| 5 | [You.com](https://openaitools.app/tool/you) | 948,349 | +7% | Freemium | Search and inference APIs that give AI models real-time web data | [.md](https://openaitools.app/tool/you.md) |
| 6 | [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) |
| 7 | [Dust](https://openaitools.app/tool/dust) | 547,217 | +1.8% | Contact for Pricing | Multiplayer AI where people and agents work side by side | [.md](https://openaitools.app/tool/dust.md) |
| 8 | [LlamaIndex](https://openaitools.app/tool/llamaindex) | 387,910 | +19.9% | Freemium, Contact for Pricing | Turn any document into AI-ready context with agentic parsing and extraction | [.md](https://openaitools.app/tool/llamaindex.md) |
| 9 | [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) |
| 10 | [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) |

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

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