Chroma vs Graphcore

Graphcore has a disclosed valuation of $600M, while Chroma's valuation is not publicly available, making a direct size comparison difficult. Graphcore employs 500-1000 people.

Graphcore has raised more in total funding at $767M, compared to Chroma's $18M — a gap of $749M. Combined, the two companies have completed 4 known funding rounds.

Graphcore leads with an Awaira Score of 85/100, while Chroma sits at 45/100. That 40-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Chroma was founded by Jeff Huber in 2022. Graphcore was founded by Nigel Toon 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. Graphcore: Graphcore designs the Intelligence Processing Unit, a processor architecture built specifically for machine learning workloads, offering a hardware alternative to NVIDIA GPUs for AI model training and inference. The Bristol-based company developed the IPU around a bulk synchronous parallel computation model that distributes model parameters across thousands of processor cores with local memory, achieving high efficiency for sparse and irregular neural network computations that GPUs handle inefficiently.\n\nThe company raised approximately $700 million across six funding rounds including a Series E that valued it at approximately $2.8 billion, with investors including Sequoia Capital, Microsoft, and Samsung Ventures. Graphcore processors are deployed in research institutions including Oxford, Cambridge, and the Rosalind Franklin Institute, as well as commercial AI platforms. The company has shipped multiple IPU generations including the MK2 IPU and Bow IPU, with the Colossus processor and IPU-POD system providing data centre scale AI compute.\n\nGraphcore competes directly against NVIDIA in the AI accelerator market, alongside AMD, Intel Gaudi, and other AI chip startups including Cerebras, SambaNova, and Groq. The AI accelerator market is projected to exceed $100 billion by 2027, driven by demand for model training compute. Graphcore faces the dominant position of NVIDIA and its CUDA software ecosystem as the primary barrier to adoption, requiring significant software investment to match the maturity of CUDA tooling that researchers and engineers have relied on for over a decade.

Graphcore 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; Graphcore has about 500-1000. 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 Graphcore compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.