Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More<\/em><\/p>\n\n\n\n Hyperscale data warehouse vendor Ocient announced today that it has raised $42.1 million as the second extension of its series B funding to accelerate the development and delivery of energy-efficient solutions for costly and unwieldy operational data and AI workloads.<\/p>\n\n\n\n The funding infusion doesn\u2019t just add to the Chicago startup\u2019s already hefty war chest; it sharpens a mission to make hyperscale analytics radically cheaper and greener at the very moment enterprises fear ballooning data\u2011center power bills.\u00a0<\/p>\n\n\n\n The new round increases the company\u2019s total funding to $159.4 million. The latest round was led by climate-savvy backers such as Blue Bear Capital and Allstate Strategic Ventures \u2014 a signal that investors now view data-platform efficiency as a climate issue as much as a performance one.\u00a0<\/p>\n\n\n\n Ocient CEO Chris Gladwin told VentureBeat that Ocient\u2019s architecture already delivers \u201cten\u2011to\u2011one price\u2011performance gains\u201d on multi\u2011petabyte workloads, and plans are underway to carry that advantage into new verticals from automotive telemetry to climate modeling. The startup has doubled its revenues for three consecutive years and appointed Henry Marshall, formerly CFO at space-infrastructure firm Loft Orbital, to steer its financial operations, signaling that Ocient is entering a formal growth stage.<\/p>\n\n\n\n The $42.1 million top\u2011up follows the $49.4 million raise in March 2024 that lifted Ocient\u2019s invested capital to $119 million and marked 109 percent year\u2011over\u2011year revenue growth. Alongside its new investors, the company retains support from Greycroft and OCA Ventures, with Buoyant Ventures backing the extension for its \u201cdifferentiated approach to delivering energy\u2011efficient analytics.\u201d Gladwin linked the round to a broader mission: \u201cEnterprises are grappling with complex data ecosystems, energy availability, and the pressure to control costs while proving business value,\u201d he said.\u00a0<\/p>\n\n\n\n Modern data warehouses thrive when datasets are measured in terabytes. Beyond that, network and storage I\/O become the choke point, not raw CPU cycles. As Gladwin told VentureBeat, \u201cWhen datasets get bigger, the flow of data from storage to processing units becomes the true limiting factor.\u201d\u00a0<\/p>\n\n\n\n In telco, ad\u2011tech and government deployments, query engines must scan trillions of records while simultaneously ingesting streams that keep pouring in. Traditional cloud architectures that separate compute and object storage force huge volumes of data over the network, inflating latency and energy usage. Those costs escalate further as enterprises layer AI and geospatial workloads on top of each other.<\/p>\n\n\n\n Ocient flipped the cloud pattern by placing NVMe SSDs right next to compute in what it calls Compute\u2011Adjacent Storage Architecture (CASA). Company Co\u2011founder Joe Jablonski explains that this design can \u201cexecute trillions of operations per second\u201d on commodity gear.<\/p>\n\n\n\n Complementing CASA is MegaLane, a high\u2011bandwidth internal fabric that keeps \u201ca million parallel tasks in flight,\u201d as Gladwin likes to put it. The result: Ocient claims 10x price-performance gains on SQL and machine learning (ML) workloads, and between 3x and 300x gains on geospatial jobs, depending on query complexity \u2014 figures the CEO reiterated during our interview. Always\u2011on ingestion plus \u201czero\u2011copy\u201d reliability means enterprises can run ETL, ad\u2011hoc SQL and ML on the same dataset without resorting to separate systems.<\/p>\n\n\n\n \n
\n<\/div>A funding round framed by climate economics<\/strong><\/h2>\n\n\n\n
Why hyperscale analytics hits a wall<\/strong><\/h2>\n\n\n\n
Inside Ocient\u2019s architecture<\/strong><\/h2>\n\n\n\n