Financial services · Snowflake
TS Imagine Adopts Gen AI at Scale, Saving 30% in Costs and 4,000 Hours of Effort
Public case study · not a Metric Oak engagement
- cost reduction by using Snowflake Cortex AI vs. other top external pretrained LLM APIs
- 30%
- hours per year saved, previously spent on manual email monitoring tasks
- 4,000
What happened
The story in brief
A trading and risk software provider serving hundreds of financial clients wanted to apply generative AI across its operations. Using AI services inside Snowflake instead of external model APIs, it cut AI costs and automated thousands of hours of manual email monitoring a year.
How it compares
- 30% cost reduction by using Snowflake Cortex AI vs. other top external pretrained LLM APIs, against a median reported cost reduction of 60% across 19 financial services results.
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Frequently asked questions
What results did TS Imagine report?
TS Imagine reported 30% cost reduction by using Snowflake Cortex AI vs. other top external pretrained LLM APIs; 4,000 hours per year saved, previously spent on manual email monitoring tasks, according to its customer story published by Snowflake.
Which platform and technologies did TS Imagine use?
TS Imagine used Snowflake, including Apache Spark, Cortex AI, Streamlit, Secure Data Sharing, Snowflake Marketplace, Large language models.
How do these results compare with other financial services programs?
30% cost reduction by using Snowflake Cortex AI vs. other top external pretrained LLM APIs, against a median reported cost reduction of 60% across 19 financial services results.
Source
Customer story published by Snowflake: TS Imagine Adopts Gen AI at Scale, Saving 30% in Costs and 4,000 Hours of Effort (opens snowflake.com in a new tab) on snowflake.com. Figures as published, retrieved 27 October 2026. The summary above is Metric Oak's own. This is not a Metric Oak engagement.
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