How Nanonets gained full FinOps visibility
& reduced GCP costs with CloudKeeper

Industry:

Software Development

Headquarters:

San Francisco, California

Founded in:

2017

Company Size:

51-200 employees

Featured Tags:

Overview

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.

Challenges

As their AI workloads expanded, Nanonets began facing multiple visibility and cost-efficiency challenges across their GCP environment.

Low visibility into Gemini API cost spikes

BigQuery and Compute costs lacked query-level clarity

Vision API misconfigurations driving high spend

No real-time dashboards due to missing log sinks

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.

Values Delivered Description

Post Optimization Impact

Description

Reduced BigQuery and Compute costs via query cleanup and rightsizing

Description

Achieved transparent visibility across key GCP services

Description

Enabled real-time dashboards for engineering, finance, and product teams

Description

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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