Qdrant vs Groq

Groq has a disclosed valuation of $2.8B, while Qdrant's valuation is not publicly available, making a direct size comparison difficult. Groq employs 300 people.

Groq has raised more in total funding at $640M, compared to Qdrant's $28M — a gap of $612M. Combined, the two companies have completed 4 known funding rounds.

Groq holds the higher Awaira Score at 80/100, compared to Qdrant's 55/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 25-point gap that reflects meaningful differences in scale or traction.

Groq was founded by Jonathan Ross in 2016. Qdrant's founder information is not currently available in our database.

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. 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. With $640 million in total funding and a valuation of $2.8 billion as of its Series D stage, Groq operates in the competitive AI infrastructure sector. The company competes with established players like NVIDIA, as well as emerging alternatives including custom chip manufacturers and cloud providers developing proprietary AI accelerators. Groq's growth trajectory reflects increasing enterprise demand for efficient inference infrastructure, though specific customer names and revenue figures remain undisclosed. Groq's LPU architecture specifically optimizes for inference latency rather than training, addressing a distinct performance bottleneck in deployed AI systems.

Groq was founded first in 2016, giving it 5 years of additional market experience. Qdrant was founded later in 2021. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Qdrant has approximately 50-200 employees, while Groq has approximately 300. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Qdrant and Groq are direct competitors — both operate in the AI Infrastructure space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.