vLLM vs Neysa

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. vLLM employs 1-50 people, while Neysa has 50-200 employees.

Neysa has raised $30M in disclosed funding across 0 known rounds. vLLM's funding history is not publicly available.

Neysa holds the higher Awaira Score at 55/100, compared to vLLM's 45/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 10-point gap that reflects meaningful differences in scale or traction.

Neysa was founded by Shashank Samala in 2023. vLLM's founder information is not currently available in our database.

vLLM: vLLM is an open-source high-throughput and memory-efficient inference and serving engine for large language models, developed initially at UC Berkeley and widely adopted in production AI deployments. The project introduced PagedAttention, a novel memory management technique that significantly increases GPU utilization during LLM inference by managing key-value cache memory analogously to how operating systems manage virtual memory pages.\n\nThe engine is used in production by AI infrastructure teams at major technology companies, AI labs, and cloud providers who need to maximize the number of concurrent LLM requests served per GPU. vLLM benchmarks consistently demonstrate throughput improvements of 10 to 20 times over naive inference implementations, translating directly into lower cost per inference query at scale. The project is maintained by a community of contributors from both academia and industry.\n\nHigh-throughput LLM serving infrastructure is foundational to the economics of AI deployment. As inference costs represent an increasing share of AI operating budgets, the performance characteristics of the serving engine directly determine the financial viability of AI-powered products. vLLM dominant position in open-source LLM serving gives it deep adoption among infrastructure engineers and makes it a reference implementation against which commercial serving solutions are measured. Neysa: Neysa builds cloud infrastructure purpose-built for AI workloads, offering GPU-accelerated compute, storage, and networking optimized for training and inference at scale. The platform targets enterprises and AI labs in India that need high-performance compute without the cost and complexity of hyperscaler lock-in.\n\nThe company raised approximately $30M in Series A funding and has attracted early adopters across the Indian AI startup ecosystem seeking affordable, low-latency GPU access. Neysa operates its own data centers with an emphasis on cost-per-token efficiency for large model training.\n\nAs Indian AI labs and enterprises scale their model development ambitions, Neysa sits at the center of a critical infrastructure gap. Domestic GPU cloud capacity is severely constrained relative to demand, and Neysa is one of the few India-headquartered players building the physical and software stack to address it.

Both vLLM and Neysa were founded in the same year — 2023. Despite sharing a founding year, they may have launched at different times within that year, which can matter in fast-moving AI markets.

vLLM has approximately 1-50 employees, while Neysa has approximately 50-200. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, vLLM and Neysa 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.