Tenstorrent vs Together AI
Side-by-side comparison of AI visibility scores, market position, and capabilities
Tenstorrent, led by Jim Keller, hit $3.2B valuation ($800M raised); Samsung, LG, and Hyundai license its Ascalon RISC-V CPU IP alongside its own AI accelerators.
About
Tenstorrent is an AI chip and RISC-V intellectual property company founded in Toronto in 2016, led by Jim Keller — one of the semiconductor industry's most celebrated chip architects, known for his contributions to AMD's Zen architecture, Apple's A-series chips, and Tesla's Autopilot hardware. Tenstorrent is building AI accelerator chips based on its proprietary Tensix architecture, as well as licensing its Ascalon RISC-V CPU IP to semiconductor companies seeking a modern, open-standard processor architecture for AI and edge applications. The company's dual strategy — chip products and IP licensing — gives it multiple commercialization paths in the AI hardware market.\n\nTenstorrent's AI accelerator chips are designed for both training and inference workloads, with a focus on efficiency and programmability that allows customers to optimize for specific model architectures. The company has licensed its Ascalon RISC-V architecture to Samsung, LG, and Hyundai — major Korean conglomerates building AI chips for consumer electronics, automotive, and industrial applications — demonstrating that Tenstorrent's IP has value beyond its own chip products. RISC-V's open-standard nature is a strategic advantage in markets where companies want to avoid dependence on ARM's licensing terms or Intel's x86 ecosystem.\n\nTenstorrent reached a $3.2B valuation and has raised $800M in total funding from investors including Samsung and LG Technology Ventures, reflecting the strategic interest of its largest licensing customers in the company's long-term success. Jim Keller's reputation as a chip architecture legend lends Tenstorrent technical credibility that few AI chip startups can match. The company competes in the AI chip market against Nvidia, Google, Amazon, and a field of well-funded startups including Groq, Cerebras, and Etched.
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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