Untether AI vs Neysa

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Untether AI employs 100-500 people, while Neysa has 50-200 employees.

Untether AI has raised more in total funding at $160M, compared to Neysa's $30M — a gap of $130M.

Untether AI holds the higher Awaira Score at 68/100, compared to Neysa's 55/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 13-point gap that reflects meaningful differences in scale or traction.

Untether AI was founded by Martin Snelgrove in 2018. Neysa was founded by Shashank Samala in 2023. Visit each company's profile on Awaira for a full founder biography.

Untether AI: Untether AI designs AI inference accelerator chips using a compute-near-memory architecture that places processing elements directly adjacent to memory storage rather than in a separate compute chip, dramatically reducing the data movement bottleneck that limits inference throughput and energy efficiency in conventional von Neumann architectures. The Toronto company develops both the chip architecture and the supporting software compiler toolchain required to deploy AI models on its hardware.\n\nThe company raised approximately $160 million in venture funding from investors including Intel Capital, Export Development Canada, and BDC Capital. Untether AI targets inference-intensive AI applications including edge AI deployment, data centre inference servers, and cloud inference acceleration where power efficiency and throughput per dollar are the primary evaluation metrics. The compute-near-memory approach is architecturally distinct from both GPU-based inference and other AI accelerator designs, with published benchmarks showing significant performance-per-watt advantages for specific inference workload types.\n\nUntether AI competes in the AI inference chip market against NVIDIA TensorRT, Groq, Cerebras, and other AI accelerator companies pursuing different architectural approaches to the inference efficiency problem. Canada position as a global AI research hub, anchored by the Vector Institute in Toronto and Mila in Montreal, provides a strong talent pipeline and research collaboration environment for deep chip design and AI systems work. The company architecture-first approach requires a longer path to production deployment than companies adapting existing GPU infrastructure, but targets a structurally different point on the performance-efficiency curve. 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.

Untether AI was founded first in 2018, giving it 5 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.

Untether AI has approximately 100-500 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, Untether AI 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.