Seemore Data Expands AI Infrastructure Control Plane to Databricks as Enterprise Cloud Costs Spiral

In a move that signals a critical shift in how enterprises manage the escalating costs of artificial intelligence, Tel Aviv-based startup Seemore Data has announced early access to its platform for Databricks. Previously focused on optimizing Snowflake environments, Seemore is now extending its "context-aware" control plane to the Databricks ecosystem, providing FinOps teams and data engineers with unprecedented visibility and automated cost-reduction capabilities.

As global corporations scramble to fund the massive investments required for generative AI—a market projected to reach $2.5 trillion by 2026 according to Gartner—the "data cloud" has emerged as the primary, yet often unmanaged, expense. With industry data indicating that nearly half of all AI budgets are being cannibalized from existing software allocations, the ability to squeeze efficiency out of data infrastructure is no longer a luxury; it is a financial imperative.

The Economic Context: Funding the AI Revolution

The surge in AI adoption has placed an unprecedented strain on enterprise balance sheets. While the strategic value of AI is widely accepted, the operational costs are frequently obscured by the complexity of cloud-native data platforms.

IDC research highlights a sobering reality for CIOs: approximately 45% of companies are funding their AI initiatives by reallocating budgets from other software departments. This "robbing Peter to pay Paul" approach creates immense pressure to optimize existing technical debt. However, for most organizations, the data cloud remains a "black box."

Yaniv Leven, CEO of Seemore Data, argues that the problem lies in the nature of cloud billing. "Every dollar moving into AI has to come from somewhere, and for most companies, the biggest line item nobody fully controls is the data cloud," Leven stated. Unlike traditional application-layer software, which can be monitored through standard APM (Application Performance Monitoring) tools, data clouds are inherently dynamic, complex, and notoriously difficult to optimize without deep, structural context.

Chronology: From Snowflake Success to Multi-Cloud Control

Seemore Data’s expansion into Databricks is the logical next step in its growth trajectory. The company originally gained industry traction by solving the "Snowflake puzzle."

• Initial Development: Seemore built its reputation by creating a control plane for Snowflake, focusing on the intersection of metadata and cost. By analyzing query structures, table dependencies, and downstream usage, the platform helped customers achieve an average 33% reduction in cloud bills.
• The "Context-Aware" Breakthrough: Unlike traditional cost-management tools that merely report on spending after the fact, Seemore’s approach was designed to be predictive and automated. By understanding the "context" of a data asset—how it is used, who relies on it, and its temporal importance—the platform could make micro-decisions on compute allocation without human intervention.
• Expansion Announcement: Recognizing that the majority of modern enterprises employ a multi-cloud or multi-platform data strategy, Seemore began development on the Databricks integration. Today’s launch brings the same philosophy—full cost attribution and automated rightsizing—to Databricks’ SQL warehouses, Lakeflow jobs, and AI service offerings.

Technical Architecture: How Seemore Tames the Data Cloud

Databricks presents a unique set of challenges compared to other platforms. The same workload can often run on various compute types, each with a different price point. For instance, data engineering compute might be billed at $0.15 per DBU (Databricks Unit), while interactive workloads can jump to $0.40. When these costs are aggregated across disparate departments, workspaces, and job clusters, the result is a billing nightmare that often leads to "overprovisioning by default."

Seemore for Databricks addresses this through a multi-layered technical suite:

1. Full Cost Attribution

The platform ingests data directly from Databricks system tables. By applying the customer’s specific negotiated rates, Seemore creates a granular, top-down view of spend. This is rolled up by workspace, job, warehouse, cluster, and SKU, effectively removing the ambiguity that currently plagues many FinOps dashboards.

2. SmartPulse for SQL Warehouses

Perhaps the most significant feature is "SmartPulse," an engine that tunes Databricks SQL auto-stop and scaling in real-time. By operating within customer-defined guardrails, the engine ensures that compute is available when needed but rapidly spun down when idle, eliminating the "paid idle time" that contributes to cloud bloat.

3. Idle-Time Optimization and Right-Sizing

Seemore leverages predictive modeling to tune auto-stop parameters to each specific warehouse’s query pattern. Furthermore, the platform offers a "closed beta" feature for right-sizing classic compute. By predicting the necessary cluster size for a workload before execution, Seemore reduces both the risk of under-sizing (which impacts Service Level Objectives) and the waste associated with over-provisioning.

4. Security and Privacy

Recognizing the sensitivity of corporate data, Seemore maintains a metadata-only access model. It connects through a customer-created service principal, ensuring that the platform never accesses the actual contents of catalogs, schemas, or tables. Write access is strictly limited to the necessary tasks of starting, stopping, or resizing warehouses, ensuring that the control plane remains non-intrusive.

Official Perspectives: The Imperative of Data Efficiency

The industry response to the launch highlights the widening gap between data capability and financial visibility. According to Seemore’s leadership, the fundamental issue is that data engineering teams are measured on reliability and performance, not cost. When reliability is the primary KPI, over-provisioning becomes the path of least resistance.

"A data asset means little until you understand where it’s used, how it’s used, and how that usage changes over time," said Yaniv Leven. "Capturing that context is what Seemore was built to do. We don’t just show you that you’re spending money; we show you why, and we handle the optimization automatically so that your engineering teams can stay focused on building, not managing cloud bills."

The implications for the industry are significant. As organizations look to scale their LLMs (Large Language Models) and machine learning pipelines, the cost of compute is becoming the primary bottleneck to ROI. By shifting from reactive cost reporting to active, context-aware management, Seemore aims to provide the "guardrails" that CFOs and CTOs desperately need to maintain fiscal discipline.

Implications for the Future of Enterprise Data

The entry of Seemore into the Databricks ecosystem signals a maturation of the FinOps sector. We are moving away from the era of "unlimited cloud budgets" into a period of extreme austerity, where every compute cycle must be justified by its business impact.

Financial Impact

For the average enterprise, the ability to cut cloud spend by 30% or more while maintaining performance is a massive value proposition. With a reported 6.1x ROI on the Snowflake platform, Seemore is positioning itself as an essential tool for the modern data stack. If these results translate to Databricks, the platform could become a standard component of the data infrastructure layer.

The Rise of Autonomous Optimization

The shift toward autonomous, AI-driven optimization—where the platform makes decisions about cluster sizes and idle times—represents a broader trend toward "autonomous infrastructure." As cloud environments grow too complex for manual intervention, tools like Seemore, which act as an intelligent layer above the platform, are likely to see increased adoption.

Competitive Landscape

This move puts Seemore in a direct position to assist organizations operating in hybrid-cloud or multi-cloud environments. By offering a single pane of glass for both Snowflake and Databricks, Seemore provides a unified cost-control strategy that transcends individual vendor ecosystems. This is a critical advantage for large enterprises that typically maintain heterogeneous data stacks.

Conclusion

The launch of Seemore for Databricks is more than just a new feature integration; it is a direct response to the economic pressures of the AI era. As enterprises strive to balance the high costs of innovation with the necessity of profitability, the ability to gain granular, context-aware control over data infrastructure will be a defining factor in which companies successfully scale their AI initiatives and which ones are sidelined by unsustainable cloud bills.

With its proven track record on Snowflake and its sophisticated approach to Databricks metadata, Seemore Data is well-positioned to serve as the critical "control plane" for the next generation of enterprise AI. As the $2.5 trillion spend begins to manifest in the coming years, the winners will be those who can optimize their data foundations today to fund the innovations of tomorrow.