Chroma vs Fireworks AI

Fireworks AI has a disclosed valuation of $4B, while Chroma's valuation is not publicly available, making a direct size comparison difficult. Fireworks AI employs 150 people.

Fireworks AI has raised more in total funding at $327M, compared to Chroma's $18M — a gap of $309M. Combined, the two companies have completed 3 known funding rounds.

Fireworks AI leads with an Awaira Score of 82/100, while Chroma sits at 45/100. That 37-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Chroma was founded by Jeff Huber in 2022. Fireworks AI was founded by Lin Qiao in 2022. 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. Fireworks AI: Fireworks AI, founded in 2022, develops infrastructure software for deploying and running large language models (LLMs) in production environments. The company provides a platform that enables organizations to deploy, fine-tune, and optimize open-source and proprietary models with reduced latency and cost. Fireworks AI's core offering focuses on inference acceleration, allowing enterprises to serve LLMs efficiently at scale through specialized hardware optimization and software acceleration techniques. The platform supports multiple model architectures and enables customization for specific use cases, including content generation, summarization, and code completion. The company has secured $327M in total funding at a $4.0B valuation as of Series C, reflecting significant investor confidence in AI infrastructure. Fireworks AI operates in a competitive segment alongside providers like Together AI, Modal, and cloud-based solutions from major vendors. The company targets enterprises seeking to reduce operational costs associated with LLM deployment while maintaining performance control and data privacy. Its position reflects broader market trends toward distributed AI infrastructure and the need for cost-efficient model serving solutions. The infrastructure layer Fireworks occupies addresses a critical gap between model development and production deployment, supporting the growing adoption of generative AI across industries. Fireworks AI specializes in inference optimization infrastructure, enabling cost-effective production deployment of large language models for enterprise customers.

Both Chroma and Fireworks AI launched in 2022. Same year, but even a few months' head start matters in AI — early movers lock in data, talent, and customer relationships fast.

Chroma has about 1-50 employees; Fireworks AI has about 150. 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 Fireworks AI compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.