Rockset Raises $44 Million in Funding to Power Search, Analytics, and AI Applications

Latest round brings total capital raised to $105 million as company gains momentum replacing Elasticsearch in modern data applications

Rockset, the search and analytics company, today announced it has raised $44 million bringing the company’s total capital raised to $105 million. The round was led by Icon Ventures, with participation from new investors Glynn Capital, Four Rivers, K5 Global, and existing investors Sequoia and Greylock.

For two years in a row, Rockset has tripled its revenue and doubled its customer base as real-time AI adoption grows at a breakneck pace. As the next-generation real-time indexing database built for the cloud, Rockset is replacing Elasticsearch especially in modern fintech, gaming, e-commerce and logistics companies for building search and AI applications such as:

• Recommendation engines to increase revenue by delivering AI-powered personalization leveraging live user behavior signals
• Chatbots to improve customer experience by incorporating contextual data in responses
• Risk analytics to enable real-time anomaly detection for fighting fraud and managing risk
• Logistics tracking to increase efficiency using real-time monitoring as assets move through the supply chain
• Real-time search and reporting to enhance end user experiences in SaaS applications

Rockset ingests streaming data and uses a highly efficient Converged Index storage format, stored on RocksDB, to deliver low latency search, filtering, aggregations and joins, simply using SQL. By extending the company’s search and analytics capabilities into vector search, Rockset now enables developers to index and update both metadata and vector embeddings in real time, a vital component to implementing semantic search, recommendation engines and Generative AI applications.

“Enterprises are doubling down on their initiatives to bring new AI applications to production. Rockset’s integration of vector search into its search and analytics experiences enables enterprises to drive efficient AI-driven search experiences and power real-time decision-making faster with an all-in-one indexing solution,” said Preeti Rathi, General Partner at Icon Ventures. “We are delighted to partner with the Rockset team on its journey to enable the convergence of streaming data and AI.”

“We are on a mission to make it easy for customers to build fast search and AI applications at scale. With compute-compute separation, we’ve redefined the data cloud architecture for speed, efficiency and simplicity, and that, combined with advancements in AI, is creating a new wave of applications that turn data into action,” said Rockset co-founder and CEO Venkat Venkataramani. “This funding round will allow us to innovate in real-time AI and invest in more go-to-market initiatives that accelerate adoption.”

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Customers choose Rockset over Elasticsearch for speed, efficiency and simplicity

Clinical ink, a leading eSource platform focused on increasing the efficiency and accuracy of medical trials, chose Rockset over Elasticsearch. “We’re currently phasing real-time clinical trial monitoring into production as the new operational data hub for clinical teams. We have been blown away by the speed of Rockset and its ability to support complex filters, joins, and aggregations. Rockset achieves double-digit millisecond latency queries and can scale ingest to support real-time updates, inserts and deletes from DynamoDB,” said Alex Doan, senior director of enterprise architecture at Clinical ink, in a recent case study. “Unlike OpenSearch, which required manual interventions to achieve optimal performance, Rockset has proven to require minimal operational effort on our part. Scaling up our operations to accommodate larger virtual instances and more clinical sponsors happens with just a simple push of a button.”

Whatnot, a leading social marketplace to discover, buy and sell products, sought a platform where data science and machine learning professionals could quickly iterate and deploy to production. “The bigger pain point was the high operational overhead of Elasticsearch for our small team. This was draining productivity and severely limiting our ability to improve the intelligence of our recommendation engine to keep up with our growth,” said Emmanuel Fuentes, head of machine learning and data platforms at Whatnot, in a recent blog. “Rockset delivered true real-time ingestion and queries, with sub-50 millisecond end-to-end latency. That didn’t just match Elasticsearch, but did so at much lower operational effort and cost, while handling a much higher volume and variety of data, and enabling more complex analytics – all in SQL.”

Zembula, a real-time customer personalization platform saw traffic explode 10x and chose Rockset as a lower-ops, cost-effective and scalable alternative to Elasticsearch to pave the way for its next 100x phase of growth. “Our Smart Banners™ are faster with Rockset, with 90 percent of them delivered in 100 milliseconds or less, while the rest are refreshed to consumers in half a second or less. Rockset’s fully managed platform freed our team from time-consuming operational work,” said Robert Haydock, CEO of Zembula in a recent blog. “All told, when including its reduced management burden, Rockset is 3x more cost-effective for us than Elasticsearch.”

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