Qdrant vs Graphcore

Graphcore has a disclosed valuation of $600M, while Qdrant'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 Qdrant's $87.8M — a gap of $679.2M. Combined, the two companies have completed 5 known funding rounds.

Graphcore leads with an Awaira Score of 85/100, while Qdrant sits at 55/100. That 30-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Qdrant was founded by Andre Yatskov in 2021. Graphcore was founded by Nigel Toon in 2016. Visit each company's profile on Awaira for a full founder biography.

Qdrant: Qdrant builds a high-performance vector similarity search engine and managed vector database service optimized for production AI applications requiring fast nearest-neighbor retrieval at scale. The engine is implemented in Rust for memory efficiency and throughput, and supports filtered vector search, payload indexing, and hybrid dense-sparse retrieval in a single system.\n\nThe company raised approximately 28 million USD and serves AI engineering teams across Europe and North America building recommendation systems, semantic search, computer vision pipelines, and RAG-based applications that require low-latency retrieval over large embedding collections. Qdrant is open-source with a managed cloud offering, following the commercial open-source model common in developer infrastructure.\n\nThe vector database market is consolidating around a handful of well-funded players as enterprises standardize their AI data stacks and look for production-grade infrastructure with strong SLAs and support. Qdrant differentiates from pure-cloud competitors through its Rust-based performance characteristics and its strong adoption in European markets where data sovereignty concerns make self-hosted options more commercially attractive than US-operated managed services. 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 5 years of extra runway. Qdrant didn't arrive until 2021. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Qdrant has about 50-200 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 Qdrant and Graphcore compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.