Qdrant vs Scale AI

Scale AI has a disclosed valuation of $29B, while Qdrant's valuation is not publicly available, making a direct size comparison difficult. Scale AI employs 1,000 people.

Scale AI has raised more in total funding at $15.9B, compared to Qdrant's $87.8M — a gap of $15.8B. Combined, the two companies have completed 7 known funding rounds.

Scale AI leads with an Awaira Score of 84/100, while Qdrant sits at 55/100. That 29-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Qdrant was founded by Andre Yatskov in 2021. Scale AI was founded by Alexandr Wang 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. 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 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; 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 Qdrant 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.