Nexthop AI vs Together AI
Side-by-side comparison of AI visibility scores, market position, and capabilities
Raised $500M Series B at $4.2B valuation (March 2026) for AI-optimized Ethernet switches; targets hyperscaler GPU cluster networking; replaces InfiniBand with open, scalable fabric
About
Nexthop AI is a networking hardware company building AI-optimized Ethernet switches purpose-built for hyperscaler AI data centers. Founded by veterans of the networking industry, the company recognized that as AI training clusters grew to tens of thousands of GPUs, the networking fabric connecting them became a critical performance bottleneck. Standard data center switches were not designed for the all-to-all communication patterns of distributed AI training, and InfiniBand—the traditional high-performance interconnect—carried significant cost and vendor lock-in. Nexthop AI is building Ethernet-based switching silicon and systems that deliver InfiniBand-class performance for AI at Ethernet-class economics.\n\nNexthop's switches are architected for the specific traffic patterns of large-scale AI workloads: high bandwidth, ultra-low and consistent latency, and support for collective communication operations like AllReduce that are central to distributed training. The company targets hyperscalers and large cloud providers building GPU clusters at the scale of tens of thousands to hundreds of thousands of accelerators. By offering a high-performance, open-standards alternative to InfiniBand, Nexthop AI competes in a market where even small per-port cost reductions translate to hundreds of millions in savings at hyperscaler scale.\n\nIn March 2026, Nexthop AI raised a $500M Series B at a $4.2B valuation, reflecting the enormous market opportunity in AI networking as hyperscalers invest trillions in data center buildout. The round positions the company to scale its silicon development, manufacturing partnerships, and go-to-market motion with the world's largest AI infrastructure buyers. Nexthop competes and collaborates in a space alongside Arista, Broadcom, and emerging players like Enfabrica as the AI networking market undergoes rapid transformation.
Open-source AI cloud. $300M ARR (Sep 2025). $3.3B valuation. $533M total raised. Backed by Salesforce, NVIDIA, Kleiner Perkins. Founded by ex-Stanford AI researchers.
About
Together AI was founded in 2022 with a mission to build the leading open-source AI cloud—a platform where developers and enterprises can train, fine-tune, and run inference on open-weight models without the constraints and costs of proprietary AI APIs. The company recognized early that as powerful open-weight models like Llama, Mistral, and FLUX proliferated, there was a massive opportunity to provide optimized infrastructure for running and customizing them. Together AI built a multi-cloud GPU platform with custom inference kernels and distributed training optimizations specifically engineered for open-source models.\n\nTogether AI's platform offers fine-tuning, inference, and training services across a curated library of leading open-weight models, with performance-optimized endpoints that often outperform what users can achieve running models on general-purpose cloud infrastructure. The company targets AI engineers, ML researchers, and enterprises that want flexibility—either for cost reasons, privacy requirements, or the need to customize model behavior through fine-tuning. Together's API design closely mirrors OpenAI's, making migration straightforward. Its pricing is consistently below proprietary model APIs for comparable capability tiers.\n\nTogether AI has achieved $300M in annualized revenue as of September 2025, growing to a $3.3B valuation with $533M in total funding. Investors include NVIDIA, Salesforce, and Kleiner Perkins—a combination that provides both strategic GPU supply chain relationships and enterprise go-to-market leverage. The open-source AI cloud market is a significant and growing segment as enterprises prioritize model flexibility and cost control alongside the maturation of open-weight models that increasingly compete with frontier proprietary models.
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