$42.1 million poured into startup offering energy-efficient solutions for costly and unwieldy operational data and AI workloads

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.

The funding infusion doesn’t just add to the Chicago startup’s already hefty war chest; it sharpens a mission to make hyperscale analytics radically cheaper and greener at the very moment enterprises fear ballooning data‑center power bills. 

The new round increases the company’s total funding to $159.4 million. The latest round was led by climate-savvy backers such as Blue Bear Capital and Allstate Strategic Ventures — a signal that investors now view data-platform efficiency as a climate issue as much as a performance one. 

Ocient CEO Chris Gladwin told VentureBeat that Ocient’s architecture already delivers “ten‑to‑one price‑performance gains” on multi‑petabyte 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.

A funding round framed by climate economics

The $42.1 million top‑up follows the $49.4 million raise in March 2024 that lifted Ocient’s invested capital to $119 million and marked 109 percent year‑over‑year revenue growth. Alongside its new investors, the company retains support from Greycroft and OCA Ventures, with Buoyant Ventures backing the extension for its “differentiated approach to delivering energy‑efficient analytics.” Gladwin linked the round to a broader mission: “Enterprises are grappling with complex data ecosystems, energy availability, and the pressure to control costs while proving business value,” he said. 

Why hyperscale analytics hits a wall

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, “When datasets get bigger, the flow of data from storage to processing units becomes the true limiting factor.” 

In telco, ad‑tech 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.

Inside Ocient’s architecture

Ocient flipped the cloud pattern by placing NVMe SSDs right next to compute in what it calls Compute‑Adjacent Storage Architecture (CASA). Company Co‑founder Joe Jablonski explains that this design can “execute trillions of operations per second” on commodity gear.

Complementing CASA is MegaLane, a high‑bandwidth internal fabric that keeps “a million parallel tasks in flight,” 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 — figures the CEO reiterated during our interview. Always‑on ingestion plus “zero‑copy” reliability means enterprises can run ETL, ad‑hoc SQL and ML on the same dataset without resorting to separate systems.

Cutting power, not just cost

Efficiency is the new competitive weapon. Ocient’s own case study shows a legacy telco stack shrinking from 170 nodes to 12 NVMe‑rich nodes, slashing energy draw to 12 kW — a 90 percent reduction in power, cost and footprint. The company doubled down by certifying its software on fourth-generation AMD EPYC processors, which deliver 3.5 times more processing power and double the memory throughput per rack, further reducing kilowatt-hours per query.

Gladwin frames the stakes bluntly: “Energy demand in data centers is accelerating; supply isn’t. Efficiency isn’t optional.” That message resonates with investors like Blue Bear, whose new $200 million climate fund targets machine intelligence solutions for energy-hungry infrastructure.

Market traction and new frontiers

Ocient’s customer base spans telecommunications operators, intelligence agencies, ad‑tech exchanges and fintech firms processing high‑volume trading data. This year the company shipped its first named solution, the Ocient Data Retention and Disclosure System, to help telecom providers meet lawful‑disclosure requirements faster and with lower energy use. 

Gladwin says the next growth wave will come from automotive sensor analytics and climate‑intelligence modeling, where current workflows rely on supercomputers; Ocient’s architecture could cut those costs by at least 75%, enabling more frequent risk analyses for insurers and agribusinesses.

Competing in the hyperscale tier

Ocient does not pitch itself as a generative‑AI database. Gladwin argues that there are numerous other companies already serving that niche, and that Ocient’s sweet spot remains high-volume, structured analytics. Still, the warehouse stores vectors with built‑in linear‑algebra functions and has a similarity index on the roadmap. Against cloud leaders like Snowflake and Databricks, Ocient’s selling point is the point at which scale and concurrency make remote‑storage architectures too slow or too pricey. Industry analysts say that the threshold typically appears north of a few hundred terabytes, but telco workloads often reach it far earlier due to incessant data ingestion.

Flexible deployments

One reason Ocient has won government and telco deals is deployment choice. The platform ships as software for on‑premises clusters, as a managed service on public clouds or via the company’s own OcientCloud. That matters when data‑sovereignty rules forbid external SaaS or when customers want to keep compute close to radio‑access networks.

What’s next

Ocient says the fresh capital will accelerate it’s efforts and will fund investments in engineering headcount and partner programs set to expand accordingly. 

“Future growth will come from ideas no one’s thought of yet,” Gladwin told VentureBeat, pointing to climate models as one such nascent domain. If Ocient can keep turning petabyte headaches into sub‑second answers while trimming both bills and carbon, the decade‑long bet behind CASA could redefine what “enterprise scale” means in the age of data‑hungry AI.