# Scale vs Vana Scale records 616,915 monthly visits with +12.5% month-over-month growth and Contact for Pricing pricing. Vana records 5,701 monthly visits with -45.5% growth and Free pricing. | Metric | [Scale](https://openaitools.app/tool/scale) | [Vana](https://openaitools.app/tool/vana-com) | | :--- | :--- | :--- | | Website URL | https://scale.com | https://vana.com | | Categories | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Agent | AI Models, Open Source AI Models, Large Language Models (LLMs), Web3 | | Pricing Model | Contact for Pricing | Free | | Monthly Visits | 616,915 | 5,701 | | MoM Growth Rate | +12.5% | -45.5% | | Primary Market | United States | United States | | Direct Visits | 191,408 | 2,339 | | Review Score | 4.7 | 4.2 | | Saved Count | 3 | 8 | | Supported Platforms | Website | Website | ## 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) ## Vana Vana is built on a simple premise: the data people generate should belong to them, and AI should be able to learn from it only with explicit, revocable permission. To make that practical, Vana combines a peer-to-peer protocol with a data portability layer. Users connect the services they already use through open-source connectors or a desktop agent, and their Personal Server — running locally in a browser tab or bundled into the desktop app — encrypts the collected data and syncs only ciphertext to hosted storage. Nothing leaves the device in the clear, and nothing is decrypted without a permission the user has granted. Access is handled through first-class protocol primitives: identity, permissions, fees, schemas, files and Protocol-Governed Encryption. When a builder presents a valid grant, the user's Personal Server decrypts the covered scope and returns it directly to the builder; the grant itself is recorded on-chain and every read lands in an access log the user can review. DataDAOs and pooled confidential compute sit on top as an optional application layer, letting groups contribute data to shared jobs without exposing raw records. For developers, Vana looks like a normal SDK workflow. You install the Vana SDK, request user-approved data, read it from the user's Personal Server, and settle protocol fees from an app escrow balance. The protocol runs on the Vana L1 with the VANA token used for staking, bridging and network security. Collection, encrypted storage, on-chain primitives, the DP RPC and grant-gated access are all live on mainnet and the Moksha testnet, with confidential compute and decentralizing the sequencer on the roadmap. **Pricing & Plans:** Vana is free to use for the core protocol and SDK. Builders pay protocol fees from an app escrow balance when reading user-approved data, and the network supports an optional USD-billing gateway for OAuth-based apps. - **User-owned data portability:** People collect data from services they already use through open-source connectors, and that data stays on their own device or encrypted storage they control. - **Personal Server and on-chain grants:** A locally running access engine encrypts data and releases it only against a valid on-chain permission, with every read written to a reviewable access log. - **Vana SDK for builders:** Install @opendatalabs/vana-sdk to request user-approved data, read it from a Personal Server, and settle protocol fees from an app escrow balance. - **DataDAOs and confidential compute:** Groups pool data for privacy-preserving compute jobs, so contributions can be used by AI without exposing raw records. - **Vana L1 and VANA token:** The protocol settles on its own L1 with validators, core contracts, and the VANA token used for staking and bridging. **Strengths:** Genuine user-ownership model: local encryption, on-chain grants, and a reviewable access log instead of silent data sharing; Public, well-structured developer documentation with an SDK, CLI, and protocol spec; Open-source connectors and a permissionless direct SDK path keep the network from depending on a single operator; Live on mainnet with a clear roadmap for confidential compute and sequencer decentralization **Limitations:** Requires understanding of wallets, encryption concepts, and grants, which is heavy for non-technical users; Parts of the hosted stack (RPC gateway, Context Gateway) are still operated by Open Data Labs; Confidential compute and Protocol-Governed Encryption are partly in progress rather than fully shipped [Profile (Markdown)](https://openaitools.app/tool/vana-com.md) ## Frequently Asked Questions ### How do Scale and Vana compare in monthly traffic? Scale has 616,915 monthly visits, while Vana has 5,701. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Scale has a month-over-month growth rate of +12.5%; Vana has -45.5%. ### How does pricing compare? 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. Vana: Free. Vana is free to use for the core protocol and SDK. Builders pay protocol fees from an app escrow balance when reading user-approved data, and the network supports an optional USD-billing gateway for OAuth-based apps. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Ollama](https://openaitools.app/tool/ollama) | 9,421,973 | +4.3% | Free, Freemium, Paid | Run and build with open AI models locally or in the cloud | [.md](https://openaitools.app/tool/ollama.md) | | 2 | [Gemma Open Models by Google](https://openaitools.app/tool/gemma-open-models-by-google) | 4,900,914 | -13.5% | Freemium, Paid | Google's family of lightweight, open-weight AI models | [.md](https://openaitools.app/tool/gemma-open-models-by-google.md) | | 3 | [ModelScope](https://openaitools.app/tool/modelscope-community) | 3,085,423 | +5.1% | Free | Open-source model and dataset hub with one-click online demos | [.md](https://openaitools.app/tool/modelscope-community.md) | | 4 | [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) | | 5 | [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) | | 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 | [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) | | 8 | [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) | | 9 | [Chutes](https://openaitools.app/tool/chutes) | 250,187 | -15.2% | Paid | Serverless, decentralized inference for open-source AI models | [.md](https://openaitools.app/tool/chutes.md) | | 10 | [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) | --- Source: https://openaitools.app/compare/scale-vs-vana-com More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)