AI inference company Baseten is said to be securing $1.5B just months after its previous large funding round.

AI inference company Baseten is said to be securing $1.5B just months after its previous large funding round.
Summary
Baseten is nearing a $1.5 billion funding round at a $13 billion valuation.
The latest round shows a 160% valuation increase in less than six months.
Investors are participating at different valuations, creating a split-priced funding round.

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According to a report from the Wall Street Journal, Baseten, a company specializing in AI inference, is nearing completion of an impressive funding round estimated at $1.5 billion, positioning its valuation at $13 billion. This development comes just five months after Baseten secured $300 million in a Series E funding round that valued the company at $5 billion. Remarkably, this earlier investment came only nine months after they raised $150 million in a Series D round.

If this new funding round is finalized, it would signify an extraordinary 160% increase in valuation in less than six months. However, the Journal notes that the funding is structured as a split-priced round—a strategy that startups are employing to enhance their perceived valuation and provide favorable outcomes for leading investors on their financial statements. Reports indicate that some investors in this round are participating at the higher valuation of $13 billion, while others are investing at a lower valuation of $11 billion. Leading this funding effort are notable firms such as Spark Capital, Sands Capital, Altimeter Capital, and Wellington Management.

Launched in 2019, Baseten is riding the wave of what has been described as the "inference gold rush," as venture capitalists are heavily investing in companies that are developing solutions for the inference layer of AI. Inference refers to the processing that occurs after a user submits a prompt to the AI model. Baseten features a promise to deliver inference rapidly while managing expenses effectively by directing requests to the most suitable models, including some competent and cost-efficient open-source alternatives.

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