OpenAI is projected to generate over $10 billion in revenue next year, a clear sign that the adoption of generative AI is accelerating. Yet, most companies struggle to deploy large AI models in production. With the steep costs and complexities involved, nearly 90% of machine learning projects are estimated never to make it to production.
Addressing this pressing issue, San Francisco-based Simplismart, which has built the fastest inference engine that helps AI apps be more responsive and cheaper for businesses to use, has raised $7 million in Series A funding led by Accel (which recently invested in Omnea and Zepz) with participation from Shastra VC, Titan Capital, and high-profile angels, including Akshay Kothari, Co-Founder of Notion. The investment will fuel R&D and growth for their enterprise-focused MLOps orchestration platform.
Make AI apps more responsive and cheaper
Simplismart was co-founded in 2022 by Amritanshu Jain, who tackled cloud infrastructure challenges at Oracle Cloud, and Devansh Ghatak, who honed his expertise on search algorithms at Google Search. Its inference engine is a standardised language that software engineers can use when building GenAI products to reduce the time it takes the underlying model to respond to queries.
Its infrastructure enables organisations to deploy AI models seamlessly. Like the shift to cloud computing, which relied on tools like Terraform and mobile app development fueled by Android, Simplismart is positioning itself as the critical enabler for AI’s transition into mainstream enterprise operations.
The platform offers organisations a declarative language that simplifies fine-tuning, deploying, and monitoring genAI models at scale. Third-party APIs often bring concerns around data security, rate limits, and utter lack of flexibility, while deploying AI in-house comes with its own set of hurdles: access to computing power, model optimisation, scaling infrastructure, CI/CD pipelines, and cost efficiency, all requiring highly skilled machine learning engineers. It allows teams to focus on their core product needs rather than spending numerous manhours building this infrastructure.
“Building generative AI applications is a core need for enterprises today. However, the adoption of generative AI is far behind the rate of new developments. It’s because enterprises struggle with four bottlenecks: lack of standardised workflows, high costs leading to poor ROI, data privacy, and the need to control and customise the system to avoid downtime and limits from other services,” said Amritanshu Jain, Co-Founder and CEO at Simplismart
“As GenAI undergoes its Cambrian explosion moment, developers are starting to realise that customising & deploying open-source models on their infrastructure carries significant merit; it unlocks control over performance, costs, customisability over proprietary data, flexibility in the backend stack, and high levels of privacy/security”, said Anand Daniel, Partner at Accel. “We were happy to see that Simplismart’s team saw this opportunity quite early, but what blew us away was how their tiny team had already begun serving some of the fastest-growing GenAI companies in production. It furthered our belief that Simplismart has a shot at winning in the massive but fiercely competitive global AI infrastructure market.”
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