d-Matrix vs Scale AI

By valuation, Scale AI is the larger company at $29B versus $2B — a 14.5x difference. Size can also be measured by team: d-Matrix employs 150 people while Scale AI has 1,000 employees.

Scale AI has raised more in total funding at $15.9B, compared to d-Matrix's $450M — a gap of $15.5B. Combined, the two companies have completed 8 known funding rounds.

Scale AI leads with an Awaira Score of 84/100, while d-Matrix sits at 68/100. That 16-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

d-Matrix was founded by Sid Sheth in 2019. Scale AI was founded by Alexandr Wang in 2016. Visit each company's profile on Awaira for a full founder biography.

d-Matrix: d-Matrix is an AI infrastructure company founded in 2019 that develops hardware and software solutions optimized for generative AI and large language model inference. The company designs specialized processors and system architectures to improve the efficiency and cost-effectiveness of deploying AI models at scale. d-Matrix's core technology focuses on reducing latency and power consumption in AI workloads, addressing key bottlenecks in data center operations. The company has raised $450 million across funding rounds, achieving a $2.0 billion valuation as of its Series C stage. d-Matrix competes in the competitive AI infrastructure market alongside companies developing custom silicon and inference acceleration platforms. Its approach targets enterprises and cloud providers requiring optimized inference capabilities for large-scale AI deployments. The company operates within the growing segment of AI infrastructure providers that emerged to support the infrastructure demands of modern generative AI applications. d-Matrix's positioning emphasizes efficiency gains and operational cost reduction compared to standard computing infrastructure. The company addresses a critical market need as organizations seek to deploy AI models economically while maintaining performance requirements. Its technology appeals to data center operators and enterprises managing substantial inference workloads. d-Matrix represents the category of specialized hardware companies built to support the computational demands of contemporary AI systems. The company's trajectory reflects broader industry expansion in AI infrastructure optimization and deployment technologies. d-Matrix specializes in inference optimization hardware specifically designed to reduce the computational and energy costs of deploying large language models at scale. 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.

Scale AI got there first, launching in 2016 — that's 3 years of extra runway. d-Matrix didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

d-Matrix has about 150 employees; Scale AI has about 1,000. 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 d-Matrix and Scale AI compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.