Qdrant vs Fireworks AI

Fireworks AI has a disclosed valuation of $4B, while Qdrant's valuation is not publicly available, making a direct size comparison difficult. Fireworks AI employs 150 people.

Fireworks AI has raised more in total funding at $327M, compared to Qdrant's $87.8M — a gap of $239.2M. Combined, the two companies have completed 4 known funding rounds.

Fireworks AI leads with an Awaira Score of 82/100, while Qdrant sits at 55/100. That 27-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Qdrant was founded by Andre Yatskov in 2021. Fireworks AI was founded by Lin Qiao in 2022. 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. Fireworks AI: Fireworks AI, founded in 2022, develops infrastructure software for deploying and running large language models (LLMs) in production environments. The company provides a platform that enables organizations to deploy, fine-tune, and optimize open-source and proprietary models with reduced latency and cost. Fireworks AI's core offering focuses on inference acceleration, allowing enterprises to serve LLMs efficiently at scale through specialized hardware optimization and software acceleration techniques. The platform supports multiple model architectures and enables customization for specific use cases, including content generation, summarization, and code completion. The company has secured $327M in total funding at a $4.0B valuation as of Series C, reflecting significant investor confidence in AI infrastructure. Fireworks AI operates in a competitive segment alongside providers like Together AI, Modal, and cloud-based solutions from major vendors. The company targets enterprises seeking to reduce operational costs associated with LLM deployment while maintaining performance control and data privacy. Its position reflects broader market trends toward distributed AI infrastructure and the need for cost-efficient model serving solutions. The infrastructure layer Fireworks occupies addresses a critical gap between model development and production deployment, supporting the growing adoption of generative AI across industries. Fireworks AI specializes in inference optimization infrastructure, enabling cost-effective production deployment of large language models for enterprise customers.

Qdrant got there first, launching in 2021 — that's 1 year of extra runway. Fireworks AI didn't arrive until 2022. 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; Fireworks AI has about 150. 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 Fireworks AI compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.