Baseten takes on hyperscalers with AI platform that lets users own model weights
It’s a bit bold for a startup to take on the cloud giants, isn’t it? The hyperscalers have already rolled out huge AI pipelines, yet Basenen is betting on something else - a platform that actually lets developers keep the weights of their models. That kind of move seems to blur the line many draw between training and serving.
In the company’s own analysis you’ll find the line “training and inference are more interconnected than the industry realizes,” which feels like a clue to what they’re after. The internal “Parsed example,” mentioned only in briefings, hints at a deeper strategy: the gap between the two phases is shrinking, and Basenen wants a slice of that space. Their service claims you’ll see performance gains right away while also setting up a path for future upgrades.
That two-pronged angle is what the next comment focuses on.
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“Baseten gives us both - the performance edge today and the infrastructure for continuous improvement.”
Baseten gives us both -- the performance edge today and the infrastructure for continuous improvement." Why training and inference are more interconnected than the industry realizes The Parsed example illuminates a deeper strategic rationale for Baseten's training expansion: the boundary between training and inference is blurrier than conventional wisdom suggests. Baseten's model performance team uses the training platform extensively to create "draft models" for speculative decoding, a cutting-edge technique that can dramatically accelerate inference. The company recently announced it achieved 650+ tokens per second on OpenAI's GPT OSS 120B model -- a 60% improvement over its launch performance -- using EAGLE-3 speculative decoding, which requires training specialized small models to work alongside larger target models.
Baseten just rolled out a training platform that actually lets customers keep their own model weights. It feels like a move away from a pure inference-only service toward something more full-stack. By making it simpler to fine-tune open-source models, Baseten seems to be betting on less dependence on closed-source players like OpenAI.
The company claims the new offering cuts a lot of the operational hassle, yet it’s still unclear whether big enterprises will take it up at scale. “Baseten gives us both the performance edge today and the infrastructure for continuous improvement,” one client said, highlighting why a combined training-and-inference setup can be attractive. Historically, the two stages have been treated as separate, so the market is still figuring it out.
A $2.15 billion valuation hints at confidence, but only actual spending patterns will show if the platform can pull money away from the big hyperscalers. Bottom line: the service is live, the idea is promising, and we’ll have to watch real-world deployments to see if it lives up to the hype.
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Common Questions Answered
How does Baseten's platform differ from hyperscaler AI services regarding model ownership?
Baseten allows developers to retain full ownership of their model weights, unlike most hyperscaler offerings that keep weights within proprietary infrastructure. This gives users direct control over their models and the ability to move them across environments.
What rationale does Baseten give for blurring the line between training and inference?
Baseten argues that training and inference are more interconnected than the industry assumes, using its training platform to create draft models that can be quickly refined for serving. This integrated approach aims to streamline continuous improvement and reduce operational friction.
In what way does Baseten's new training platform aim to reduce reliance on closed‑source providers like OpenAI?
By simplifying fine‑tuning of open‑source models and letting customers keep their own weights, Baseten's platform offers an alternative to closed‑source solutions. This empowers enterprises to build and maintain AI capabilities without depending on external proprietary services.
What benefits does Baseten claim its platform provides for performance and infrastructure?
Baseten states that its platform delivers a performance edge today while also supplying the infrastructure needed for continuous model improvement. This combination is intended to eliminate operational headaches and support ongoing optimization.