Scale AI vs Graphcore

By valuation, Scale AI is the larger company at $29B versus $500M — a 58x difference. Size can also be measured by team: Scale AI employs 1,000 people while Graphcore has 500-1000 employees.

Scale AI has raised more in total funding at $15.9B, compared to Graphcore's $700M — a gap of $15.2B. Combined, the two companies have completed 8 known funding rounds.

Graphcore leads with an Awaira Score of 85/100, while Scale AI sits at 84/100. That 1-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Scale AI was founded by Alexandr Wang in 2016. Graphcore was founded by Nigel Toon in 2016. Visit each company's profile on Awaira for a full founder biography.

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. 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.

Both Scale AI and Graphcore launched in 2016. Same year, but even a few months' head start matters in AI — early movers lock in data, talent, and customer relationships fast.

Scale AI has about 1,000 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 Scale AI and Graphcore compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.