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Hyperscale Data Warehouse Vendor OCIENT It was announced today that it has raised $42.1 million as the second expansion of Series B funding to accelerate the development and delivery of energy-efficient solutions for costly and cumbersome operational data and AI workloads.
Fundraising infusions aren’t just added to the already hefty war chests of Chicago startups. It sharpens its mission to make hyperscale analytics fundamentally cheaper and more environmentally friendly at a moment when businesses fear inflate their data center power bills.
The new round will increase the company’s total funding to $159.4 million. The latest round was led by climate-savvy supporters. Bluebear capital and Allstate Strategic Ventures – A signal that investors are currently viewing data platform efficiency as much as a climate problem as performance issues.
Ocient CEO Chris Gladwin tells VentureBeat that Ocient’s architecture already offers “10 to 1 pricing profit” for large workloads, with plans underway to carry its benefits to new verticals, from car telemetry to climate modeling. It’s available at startups Doubled revenue For the third year in a row, he has appointed Henry Marshall, former CFO of Space-Infrastructure Firm Loft Orbital, to take action on his financial operations, indicating that Ocient is in the formal stage of growth.
Funding round surrounded by climate economics
The $42.1 million top-up increased Ocient’s investment capital to $119 million, following a $49.4 million increase in March 2024, reaching an annual annual revenue growth of 109%. Next to it New Investorsthe company maintains support from Greycroft and OCA Ventures, and Buyant Ventures supports extensions for “a differentiated approach to delivering energy-efficient analytics.” Gladwin linked the round to a broader mission. “Companies are working on pressures to control costs while demonstrating complex data ecosystems, energy availability and business value,” he said.
Why Hyperscale Analysis is a Wall
When datasets are measured in terabytes, modern data warehouses thrive. Beyond that, network and storage I/O become chokepoints rather than raw CPU cycles. As Gladwin told VentureBeat, “As a data set grows, the flow of data from storage to processing units becomes a true limiting factor.”
With Telco, Ad-Tech and government deployments, query engines must consume streams that continue to pour simultaneously, scanning trillions of records. These costs escalate further as businesses stack AI and geospatial workloads on one another.
Within Ocient’s architecture
OCIENT flipped the cloud pattern by quickly placing the NVME SSDS and calculating what it calls Computing Asgent Storage Architecture (CASA). Company co-founder Joe Jablonski explains that the design can “run trillions of operations per second” in Commodity Gear.
Complementing CASA is Megalane, a high-bandwidth internal fabric that maintains “one million parallel tasks in flight,” as Gladwin likes to say. As a result, Ocient claims 10x more price performance improvements in SQL and Machine Learning (ML) workloads, and earns 3x to 300x more profits on GEOSPATIAL jobs, depending on the complexity of the query. Always conflicting intake and “zero copy” reliability means that enterprises can run ETL, AD-HOC SQL, and ML on the same dataset without relying on separate systems.
It reduces electricity as well as costs
Efficiency is a new competitive weapon. OCIENT’s original Case studies It shows a legacy phone stack shrinking from 170 nodes to 12 NVME rich nodes, reducing the energy draw to 12 kW. This is a 90% reduction in electricity, costs and footprint. The company doubled by certifying fourth generation software AMD EPYC The processor offers 3.5 times more processing power per rack, doubles memory throughput and further reduces kilowatt-hours per query.
“The energy demand in data centers is accelerating. Supply is not accelerating. Efficiency is not an option,” says Gladwin, whose message resonates with investors like Blue Bear, where the $200 million Climate Fund for energy-hungry infrastructure targets mechanical intelligence solutions.
Market traction and new frontiers
Ocient’s customer base spans telecom operators, intelligence agencies, AD-Tech exchanges, and FinTech companies that process large amounts of trading data. This year, the company shipped it First named solutionan economic data retention and disclosure system. Help telecommunications providers to speed up legal dispute requirements and reduce energy usage.
Gladwin says the next growth wave comes from automotive sensor analysis and climate intelligence modeling, where current workflows rely on supercomputers. Ocient’s architecture could reduce these costs by at least 75%, making risk analysis more frequent for insurance companies and agribusinesses.
Competition in the Hyperscale Layer
Ocient does not sell itself as a generated database. Gladwin argues that there are many other companies already serving that niche, and that Ocient’s sweet spot remains a massive amount of structured analysis. Still, the warehouse stores vectors with built-in linear algebra functions and has similarity indexes in the roadmap. For cloud leaders like Snowflake and Databricks, Ocient’s selling point is that scale and concurrency make remote storage architectures too slow or too expensive. Thresholds usually appear several hundred terabytes north, according to industry analysts, but telephone company workloads often reach much faster due to data ingestion.
Flexible deployment
One of the reasons Ocient has won government and telephone deals is its deployment choice. This platform is shipped as software for on-premee clusters, as public cloud management services, or through the company itself OcientCloud. It is important if Data -Sovereginty bans external SAAS or if the customer wants to continue computations near the wireless access network.
What’s next?
Ocient says Fresh capital It accelerates efforts and funds engineering personnel investments and partner programs that will expand accordingly.
“Furthermore growth comes from ideas that no one has thought of yet,” Gladwin told Venture Beat, pointing out climate modeling as one of such early areas. If OCIENT can continue to turn petabyte headaches into subsecond answers, the 10-year bet behind CASA, while trimming both invoices and carbon, can redefine the meaning of “enterprise scale” in the age of AI where data is hanging.
