Chroma vs Scale AI

Scale AI has a disclosed valuation of $29B, while Chroma's valuation is not publicly available, making a direct size comparison difficult. Scale AI employs 1,000 people.

Scale AI has raised more in total funding at $15.9B, compared to Chroma's $18M — a gap of $15.9B. Combined, the two companies have completed 6 known funding rounds.

Scale AI leads with an Awaira Score of 84/100, while Chroma sits at 45/100. That 39-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Chroma was founded by Jeff Huber in 2022. Scale AI was founded by Alexandr Wang in 2016. Visit each company's profile on Awaira for a full founder biography.

Chroma: Chroma builds an open-source AI-native vector database designed specifically for storing, indexing, and querying the high-dimensional embeddings that power retrieval-augmented generation (RAG) systems, semantic search, and LLM memory layers. The database is designed to be simple to embed in Python applications with minimal configuration, making it the default choice for developers prototyping AI products.\n\nThe company raised approximately 18 million USD and has accumulated significant open-source adoption among AI developers building LLM applications who need a lightweight, locally runnable vector store that can also scale to cloud deployments. Chroma usage grew rapidly in parallel with the LangChain ecosystem, where it became a default integration for document retrieval workflows.\n\nVector databases are foundational infrastructure for the retrieval-augmented generation architecture that dominates production LLM application design. Chroma competes with Pinecone, Weaviate, Qdrant, and Milvus, and while it faces stronger enterprise competition from well-funded rivals, its developer-first positioning and open-source distribution give it deep penetration at the prototyping stage where tool choices frequently persist into production. Scale AI: Scale AI is a data infrastructure company founded in 2016 that specializes in data labeling and annotation services for artificial intelligence model development. The company provides managed workforce solutions and software platforms that enable organizations to prepare high-quality training data at scale. Scale AI's core offerings include data labeling, model evaluation, data generation, and managed workforce services across computer vision, natural language processing, and other AI domains. The company serves enterprise customers across automotive, defense, technology, and other sectors requiring large-scale data annotation for machine learning applications. Notable use cases include autonomous vehicle development, where precise labeled data is critical for training perception systems. Scale AI operates a hybrid model combining software automation with human-in-the-loop annotation capabilities. With a Series F valuation of $13.8 billion and total funding of $1.6 billion, Scale AI represents one of the highest-valued data infrastructure companies in the AI stack. The company competes in the data labeling market alongside firms like Labelbox, Snorkel AI, and various in-house solutions deployed by large technology companies. Its growth trajectory reflects increasing enterprise demand for quality training data as AI model development accelerates. Scale AI's positioning emphasizes enterprise-grade reliability, security, and quality assurance in data preparation workflows. Scale AI has achieved unicorn valuation by solving the critical bottleneck of high-quality labeled data production at enterprise scale.

Scale AI got there first, launching in 2016 — that's 6 years of extra runway. Chroma didn't arrive until 2022. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Chroma has about 1-50 employees; Scale AI has about 1,000. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Chroma and Scale AI compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.