This round nearly triples the company’s valuation from where it was six months ago, bringing it to $2.15 billion, according to a Friday (Sept. 5) press release.
Baseten’s platform is purpose-built for inference and is used to power AI products and applications across industries, according to the release.
It brings together applied AI research, flexible infrastructure and seamless developer tooling to help companies bring models into production, per the release.
“Every breakout AI application depends on fast, reliable and cost-effective inference, the same way the last 15 years of companies depended on the cloud,” Baseten Co-founder and CEO Tuhin Srivastava said in the release. “Baseten makes that possible.”
Baseten will use its new funding to invest in model performance research, infrastructure and developer tooling and to expand its customer teams, according to the release.
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Jay Simons, general partner at BOND, which led the Series D round, said in the release: “Inference is a major bottleneck, and solving it is key to unleashing the full potential of AI. Baseten is doing this today.”
PYMNTS reported Aug. 25 that inference is the stage where an AI model is used to generate predictions, responses or insights and that the costs of inference are recurring and can add up fast. Every AI-enabled workflow involves inference, and the more queries or predictions a company’s AI systems make, the bigger the bill.
Baseten’s Series D funding round came about seven months after its Series C, which was announced on Feb. 19. In the earlier round, the company raised $75 million.
In May, the company announced two new inference products powered by its Baseten Inference Stack: Baseten Model APIs for open-source AI models and Baseten Training features for training models to improve inference performance.
“In the AI market, your number one differentiator is how fast you can move,” Srivastava said in a press release announcing the products. “Model APIs give developers the speed and confidence to ship AI features knowing that we’ve handled the heavy lifting on performance and scale.”
