Unravel Data, the data platform optimization company, announced Arvix AI, an agentic engine that autonomously tunes and optimizes across Databricks, Snowflake, and BigQuery. Built upon a decade of data platform telemetry, Arvix AI now powers every optimization action within Unravel Data, continuously analyzing workloads, rewriting code, right-sizing infrastructure, eliminating storage waste, and validating every change before applying it. Customers see an average 40% reduction in data platform spend and 4x faster performance.

Data and AI platforms now consume a significant share of cloud budgets, yet much of that spend is wasted. The rise of AI is only accelerating the issue. At the same time, the democratization of data has enabled hundreds of engineers, data scientists, and analysts to write thousands of queries daily on platforms like Databricks, Snowflake, and BigQuery, but the code is often expensive, unreliable, slow, and inefficient at scale.

"Unravel has spent the last decade building the most complete view of how data platforms actually run – across Databricks, Snowflake, and BigQuery. We've watched billions of jobs, every failure pattern, every cost spiral,” said Kunal Agarwal, co-founder and CEO of Unravel Data. “Now, we’ve turned all that telemetry into action with Arvix AI. It's the engine inside Unravel that closes the loop between knowing what's broken and actually fixing it.”

At the core of Arvix AI is the Context Graph, an intelligence layer that continuously maps an organization’s full data environment across six dimensions: compute, workload, data, code, platform, and business. Unlike tools that operate on SQL text alone, Arvix AI understands which cluster a query runs on, what downstream pipelines depend on it, which team owns it, what the SLA is, and how platform-specific configuration affects execution. This persistent, cross-dimensional awareness is what allows Arvix AI to make optimizations that do not break anything downstream, and what generic LLMs are fundamentally lacking.

Additional differentiators include:

• Full-stack optimization. Arvix AI covers workload optimization, code rewriting, infrastructure tuning, and storage management within a single platform. Users get a full picture across compute, workload, data, code, platform, and business, not a partial view stitched together from multiple tools.
• Purpose-built for data platforms, not bolted on. Arvix AI operates at the query, pipeline, cluster, and data levels across cost and performance simultaneously. Most tools stop at the warehouse. The depth of optimization, across code, configuration, infrastructure, and storage, is what separates a purpose-built platform from a feature bolted onto a monitoring or FinOps tool.
• Beyond observability to actionability. Fewer than 10% of platform-native recommendations (e.g., Databricks Advisor, Snowflake Recommendations, and BigQuery Recommender) ever get implemented. They all surface opportunities, but busy teams have no time to act on them. Arvix AI automates the other 90%.
• Validation before deployment, with a built-in safety net. Arvix AI tests every change against real workload behavior before applying it, then monitors the change after deployment and automatically reverts if anything degrades. That safety net is what makes autonomous optimization possible in production environments.

“We have merged the principles of data observability, FinOps, and infrastructure monitoring to create what we’re calling autonomous data platform optimization,” added Agarwal. “Moving beyond recommendations to autonomous action is what the CFOs who want costs down and the engineers who need performance up have been waiting for.”

Early Arvix AI users have already experienced an average 40% reduction in data platform spend while still meeting SLAs. One leading global airline realized $340,000 savings through 1,500 insights applied autonomously in just three days. Another major e-commerce platform identified $4.2 million in BigQuery storage optimization.