Groq vs Scale AI

By valuation, Scale AI is the larger company at $29B versus $20B — a 1.5x difference. Size can also be measured by team: Groq employs 300 people while Scale AI has 1,000 employees.

Scale AI has raised more in total funding at $15.9B, compared to Groq's $1.4B — a gap of $14.5B. Combined, the two companies have completed 9 known funding rounds.

Scale AI leads with an Awaira Score of 84/100, while Groq sits at 80/100. That 4-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Groq was founded by Jonathan Ross in 2016. Scale AI was founded by Alexandr Wang in 2016. Visit each company's profile on Awaira for a full founder biography.

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. Groq operates in the competitive AI infrastructure sector, competing with established players like NVIDIA, as well as emerging alternatives including custom chip manufacturers and cloud providers developing proprietary AI accelerators. In December 2025, Nvidia and Groq announced an agreement reportedly valued at approximately $20 billion to license Groq's AI inference technology. Groq's growth trajectory reflects increasing enterprise demand for efficient inference infrastructure. Groq's LPU architecture specifically optimizes for inference latency rather than training, addressing a distinct performance bottleneck in deployed AI systems. Groq operates in the AI Infrastructure sector and is headquartered in United States. Founded in 2016 by Jonathan Ross, Groq has raised $1.4B in total funding, achieving a valuation of $20B as of its latest round. The company's funding journey includes a Series A of $30M in 2019, a Series B of $40M in 2021, a Series C of $300M in 2021, a Series D of $450M in 2023. The most recent round was led by SoftBank Vision Fund 2. With approximately 300 employees, Groq has established itself as a Acquired-stage player in the AI Infrastructure market. The company holds an Awaira Score of 80/100, reflecting its strong position across valuation, funding trajectory, team scale, and market influence. Groq competes in a rapidly evolving segment alongside other AI Infrastructure companies. Based in United States, Groq is part of a growing international AI ecosystem attracting talent and investment. The AI Infrastructure space has attracted significant investment in recent years, with companies racing to capture enterprise and consumer demand for AI-powered solutions. 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.

Both Groq and Scale AI 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.

Groq has about 300 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 Groq 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.