Chroma vs Groq

Groq has a disclosed valuation of $2.8B, while Chroma's valuation is not publicly available, making a direct size comparison difficult. Groq employs 300 people.

Groq has raised more in total funding at $640M, compared to Chroma's $18M — a gap of $622M. Combined, the two companies have completed 4 known funding rounds.

Groq holds the higher Awaira Score at 80/100, compared to Chroma's 45/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 35-point gap that reflects meaningful differences in scale or traction.

Chroma was founded by Jeff Huber in 2022. Groq was founded by Jonathan Ross 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. Groq: Groq is an AI infrastructure company founded in 2016 that designs and manufactures specialized processors for artificial intelligence workloads. The company's core product is the Language Processing Unit (LPU), a custom-built chip architecture optimized for inference tasks in large language models and other AI applications. Unlike traditional GPUs designed for general-purpose computing, Groq's LPUs prioritize deterministic latency and throughput for sequential AI processing, enabling faster token generation in inference scenarios. Groq has positioned itself as an alternative to NVIDIA's GPU-dominated infrastructure market, targeting enterprises requiring high-performance AI inference at scale. The company offers cloud-based access to its hardware through GroqCloud, allowing developers to run inference workloads with reduced latency compared to conventional GPU implementations. With $640 million in total funding and a valuation of $2.8 billion as of its Series D stage, Groq operates in the competitive AI infrastructure sector. The company competes with established players like NVIDIA, as well as emerging alternatives including custom chip manufacturers and cloud providers developing proprietary AI accelerators. Groq's growth trajectory reflects increasing enterprise demand for efficient inference infrastructure, though specific customer names and revenue figures remain undisclosed. Groq's LPU architecture specifically optimizes for inference latency rather than training, addressing a distinct performance bottleneck in deployed AI systems.

Groq was founded first in 2016, giving it 6 years of additional market experience. Chroma was founded later in 2022. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Chroma has approximately 1-50 employees, while Groq has approximately 300. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Chroma and Groq are direct competitors — both operate in the AI Infrastructure space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.