Nanonets is a leading AI automation platform specializing in Intelligent Document Processing (IDP) and unstructured data extraction. Powered by advanced OCR and deep learning, Nanonets helps enterprises convert invoices, receipts, contracts, and forms into clean, structured, and actionable data fueling automation at scale.
As their AI workloads expanded across GCP, rising costs and limited visibility made it difficult to understand usage patterns, optimize heavy workloads, and maintain predictable spending.
To strengthen cost governance and gain deep visibility into their AI-driven infrastructure, Nanonets partnered with CloudKeeper to establish a structured, insight-led FinOps framework.
How Nanonets gained full FinOps visibility
& reduced GCP costs with CloudKeeper
How Nanonets gained full FinOps visibility
& reduced GCP costs with CloudKeeper

Overview
Challenges
As their AI workloads expanded, Nanonets began facing multiple visibility and cost-efficiency challenges across their GCP environment.
They needed granular, real-time visibility into AI workloads and a partner who could help them understand, optimize, and govern cloud costs effectively.
The Solution
Solution: CloudKeeper Lens
CloudKeeper worked with Nanonets to implement a strong FinOps visibility and optimization framework across GCP.
- Enabled transparent Gemini API cost attribution
- Built real-time Looker dashboards for BigQuery and compute insights
- Established reliable log sinks and pipelines for continuous governance
- Optimized workloads through Vision API tuning, Compute rightsizing, and AMD migration recommendations
This helped Nanonets gain clarity into their AI workloads, optimize high-consumption services, and build predictable cost governance.
Post Optimization Impact
Reduced BigQuery and Compute costs via query cleanup and rightsizing
Achieved transparent visibility across key GCP services
Enabled real-time dashboards for engineering, finance, and product teams
Predictable cloud spend and stronger governance supporting scalable AI growth
Conclusion
Nanonets’ collaboration with CloudKeeper transformed their cloud operations from limited visibility and rising costs to a structured, real-time FinOps practice. By enabling transparent attribution for key AI services, optimizing BigQuery, Vision API, and Compute Engine usage, and establishing reliable cost governance pipelines, CloudKeeper helped Nanonets gain clarity, control, and predictability across their GCP environment.
With deeper insights, reduced spend, and a stronger FinOps foundation, Nanonets is now better equipped to scale its AI workloads confidently and sustainably
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