Neysa vs Tenstorrent

Tenstorrent has a disclosed valuation of $3.2B, while Neysa's valuation is not publicly available, making a direct size comparison difficult. Tenstorrent employs 400 people.

Tenstorrent has raised more in total funding at $1.2B, compared to Neysa's $30M — a gap of $1.2B. Combined, the two companies have completed 5 known funding rounds.

Tenstorrent holds the higher Awaira Score at 80/100, compared to Neysa'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.

Neysa was founded by Shashank Samala in 2023. Tenstorrent was founded by Ljubisa Bajic in 2016. Visit each company's profile on Awaira for a full founder biography.

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. Tenstorrent: Tenstorrent is a Canadian AI infrastructure company founded in 2016 that designs and manufactures specialized processors and software platforms for artificial intelligence workloads. The company develops custom silicon chips and compute architectures optimized for training and inference of large language models and other machine learning applications. Its core offering includes the Grayskull and Wormhole processor families, which use a distributed computing approach to achieve high performance efficiency. The company's software stack enables developers to deploy AI models across its hardware without extensive optimization. Tenstorrent has positioned itself as an alternative to dominant GPU manufacturers in the AI accelerator market, targeting data centers and cloud providers seeking alternatives for AI infrastructure. The company has secured $1.18 billion in total funding across multiple rounds, achieving a $3.2 billion valuation while remaining in Series D funding stage. Founded by industry veterans, Tenstorrent operates as a fabless semiconductor company, partnering with foundries for chip manufacturing. The firm competes against established players like NVIDIA, AMD, and other emerging AI chip startups. Its growth trajectory reflects increasing demand for specialized AI computing resources, though adoption remains limited compared to incumbent GPU providers. The company continues expanding its processor roadmap and software ecosystem to capture market share in the rapidly growing AI infrastructure sector. Tenstorrent develops custom AI processors designed as a performance-efficient alternative to GPUs, leveraging distributed computing architecture for large language model workloads.

Tenstorrent was founded first in 2016, giving it 7 years of additional market experience. Neysa was founded later in 2023. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Neysa has approximately 50-200 employees, while Tenstorrent has approximately 400. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Neysa and Tenstorrent 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.