BANKING COST OPTIMIZATION

Reduce Snowflake & Azure Costs
by 15-20% with AI Workload Optimization

Without discipline, AI workloads can 2-3x your Snowflake bill within 18 months. Our cost optimization solutions help financial institutions control AI spending through intelligent model selection, batch processing, and ROI tracking.

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THE CHALLENGE

AI Workloads Drive Uncontrolled Cost Growth

Financial institutions deploying AI workloads face exponential cost growth. Without proper discipline and optimization, Snowflake bills can increase 2-3x within 18 months due to unstructured data processing, model experimentation, and real-time inferencing creating unchecked growth.

Rising LLM Inference Costs

LLM inference costs are rising 40-60% year-over-year as teams deploy more AI agents. Cortex AI and agentic workflows drive compute consumption without adequate cost controls.

Unstructured Data Processing

Call logs, claims, and transcripts drive compute spikes and storage bloat. Processing unstructured data requires significant warehouse credits without proper optimization.

Model Experimentation

Without ROI tracking, every new AI prototype utilizes warehouse credits. Model experimentation creates cost sprawl without visibility into which experiments deliver value.

Real-Time AI Features

Streaming ingestion plus continuous inferencing equals unchecked growth. Lack of adequate caching and batch processing strategies drives costs higher.

2-3x Cost Growth

Without discipline, AI workloads can 2-3x your Snowflake bill within 18 months. Unchecked growth in compute, storage, and inference costs erodes profitability.

Lack of Caching

Inadequate caching strategies result in redundant processing of the same data, driving unnecessary compute costs and slower response times.

THE DAGUI SOLUTION

Intelligent Cost Optimization for AI Workloads

DagUI generates cost optimization pipelines that track AI spending, optimize model selection, implement batch processing, and enforce ROI thresholds—reducing Snowflake and Azure costs by 15-20%.

Cortex AI for Financial Services

Optimized for Banking Use Cases: Pipelines are optimized for key financial services applications:

  • • KYC (Know Your Customer) processing and verification
  • • AML (Anti-Money Laundering) detection and monitoring
  • • Fraud detection and prevention
  • • Market research and analysis

Result: Specialized pipelines that deliver cost-effective AI processing for banking-specific use cases.

Cost Per Inference Tracking

Granular Cost Visibility: Pipelines track Claude 3.5 and Llama 3 spend by use case with cost per inference tagging:

  • • Track inference costs by model (Claude 3.5, Llama 3, etc.)
  • • Tag costs by use case (KYC, AML, fraud detection)
  • • Monitor cost trends and identify optimization opportunities
  • • Generate cost reports by department and project

Result: Complete visibility into AI spending enables data-driven cost optimization decisions.

AI Profitability Threshold

ROI-Based Cost Control: Pipelines define and enforce "AI Profitability Threshold"—inference runs with validated projected ROI value:

  • • Set minimum ROI thresholds for AI inference runs
  • • Validate projected ROI before executing expensive operations
  • • Block or flag low-ROI inference requests
  • • Track actual ROI vs. projected ROI over time

Result: Only profitable AI operations execute, preventing wasteful spending on low-value inference.

Intelligent Model Selection

Cost-Optimized Routing: Pipelines optimize model selection to route low-complexity tasks to cheaper Llama 3:

  • • Automatically route simple tasks to cost-effective models (Llama 3)
  • • Reserve premium models (Claude 3.5) for complex use cases
  • • Balance cost and performance based on task complexity
  • • Monitor model performance and cost trade-offs

Result: Significant cost savings by using the right model for each task complexity level.

Batch Inference Processing

Efficient Batch Operations: Pipelines batch inference where possible—group daily KYC reviews into batch jobs:

  • • Group similar inference requests into batch jobs
  • • Schedule batch processing during off-peak hours
  • • Reduce per-inference overhead costs
  • • Optimize warehouse credit utilization

Result: 15-20% cost reduction through efficient batch processing and reduced overhead.

Intelligent Caching Strategy

Reduce Redundant Processing: Pipelines implement comprehensive caching strategies:

  • • Cache inference results for repeated queries
  • • Implement smart cache invalidation policies
  • • Reduce compute costs for duplicate processing
  • • Optimize storage costs with efficient cache management

Result: Reduced redundant processing and lower compute costs through intelligent caching.

OPERATIONAL QUICK WINS

Deploy These Optimizations in Parallel

Accelerate ROI and demonstrate immediate value by deploying these optimizations in parallel with core priorities.

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Cloud Services Layer

Transient Tables

Batch DML updates and eliminate table cloning for audit trails. Set zero fail-safe on staging tables to reduce storage overhead.

Cost Impact: 8-12%
Storage Impact: 5-10%
🔍

Search Optimization

Point Lookups

Enable selectively for point lookups on stable tables only. Optimize query performance for targeted searches.

Query Performance: 3-8%
🔗

Salesforce Zero-Copy

Native Integration

Eliminate CRM data silos through native integration. Streamline data access without data duplication.

Ingestion Cost: 2-5%
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Data Model and Query Optimization

Optimize data structures and query patterns for maximum efficiency

Data Model Optimization

  • • Normalize and denormalize tables based on query patterns
  • • Implement clustering keys for frequently filtered columns
  • • Optimize table partitioning strategies
  • • Reduce data redundancy and improve storage efficiency

Query Optimization

  • • Optimize JOIN operations and reduce data scanning
  • • Implement materialized views for common queries
  • • Use query result caching for repeated queries
  • • Optimize aggregation and window functions
10-15%
Query Performance
5-10%
Storage Reduction
8-12%
Compute Cost

Deploy in Parallel for Maximum Impact

These quick wins can be implemented alongside your core optimization priorities, providing immediate cost savings and performance improvements while you work on larger strategic initiatives.

ADVANCED OPTIMIZATION

Iceberg-Based ELT for High-Volume Ingestion

Move 80% of ingestion compute off Snowflake to Azure spot instances plus Iceberg federation. Dramatically reduce data ingestion costs while maintaining query performance and data accessibility.

80%

Ingestion compute moved off Snowflake

70-90% cheaper compute costs with Azure spot instances

Data Storage

Azure Blob Storage (ADLS Gen2)

  • • GDPR-compliant data storage
  • • Open format flexibility with Apache Iceberg
  • • Cost-effective object storage
  • • Seamless integration with Snowflake

Compute Layer

Spark ETL on Azure Spot Instances

  • • 70-90% cheaper than Snowflake credits
  • • Scalable Spark-based ETL processing
  • • Spot instance cost optimization
  • • High-volume data ingestion

Query Federation

Snowflake Queries Iceberg Tables

  • • Direct query access to Iceberg tables
  • • No data movement required
  • • Maintain query performance
  • • Unified data access layer

How Iceberg Federation Optimizes AI/ML Workflows

Cost Reduction

Move high-volume data ingestion workloads from expensive Snowflake compute to cost-effective Azure spot instances, reducing ingestion costs by 70-90%.

  • • 80% of ingestion compute off Snowflake
  • • Azure spot instances for ETL processing
  • • Significant cost savings on data ingestion

Open Format Flexibility

Apache Iceberg provides open table format that enables seamless data access across multiple compute engines without vendor lock-in.

  • • Vendor-agnostic data storage
  • • Multi-engine query support
  • • Future-proof architecture

AI/ML Workflow Benefits

Optimize data pipelines for AI/ML workloads by separating ingestion compute from analytical queries, enabling better resource allocation.

  • • Efficient data preparation for ML
  • • Reduced compute costs for training data
  • • Faster data ingestion pipelines

Architecture Overview

1

Data Ingestion

High-volume data streams are ingested using Spark ETL jobs running on Azure spot instances. Data is processed and written to Azure Blob Storage (ADLS Gen2) in Apache Iceberg format.

2

Iceberg Storage

Data is stored in Azure Blob Storage using Apache Iceberg table format, providing open format flexibility, GDPR compliance, and efficient data organization for analytical workloads.

3

Query Federation

Snowflake queries Iceberg tables directly via federation, enabling analytical queries without data movement. This maintains query performance while keeping ingestion costs on cost-effective Azure infrastructure.

PROJECTED IMPACT

15-20% Cost Reduction on AI Compute

15-20%

Projected impact on AI compute costs

Model Optimization
Route tasks to cost-effective models
Batch Processing
Group inference into efficient batches
ROI Tracking
Enforce profitability thresholds
Smart Caching
Reduce redundant processing
KEY OPTIMIZATION STRATEGIES

Proven Approaches to Reduce AI Workload Costs

1

COST TRACKING & VISIBILITY

Implement comprehensive cost tracking for all AI workloads, including inference costs by model, use case, and department. Tag all operations with cost metadata for granular visibility.

  • • Track Claude 3.5 and Llama 3 spend by use case
  • • Cost per inference tagging and reporting
  • • Monitor cost trends and identify anomalies
  • • Generate cost reports by department and project
2

AI PROFITABILITY THRESHOLD

Define and enforce "AI Profitability Threshold"—inference runs with validated projected ROI value. Block or flag low-ROI operations before they consume expensive compute resources.

  • • Set minimum ROI thresholds for AI inference
  • • Validate projected ROI before execution
  • • Track actual vs. projected ROI over time
  • • Automatically route based on profitability
3

MODEL SELECTION OPTIMIZATION

Optimize model selection to route low-complexity tasks to cheaper Llama 3, reserving premium models like Claude 3.5 for complex use cases that require advanced capabilities.

  • • Automatically route simple tasks to cost-effective models
  • • Reserve premium models for complex use cases
  • • Balance cost and performance based on task complexity
  • • Monitor model performance and cost trade-offs
4

BATCH INFERENCE PROCESSING

Batch inference where possible—group daily KYC reviews into batch jobs. Schedule batch processing during off-peak hours to optimize warehouse credit utilization.

  • • Group similar inference requests into batch jobs
  • • Schedule batch processing during off-peak hours
  • • Reduce per-inference overhead costs
  • • Optimize warehouse credit utilization
5

CACHING & STORAGE OPTIMIZATION

Implement intelligent caching strategies to reduce redundant processing. Optimize storage costs for unstructured data (call logs, claims, transcripts) to prevent storage bloat.

  • • Cache inference results for repeated queries
  • • Implement smart cache invalidation policies
  • • Optimize storage for unstructured data
  • • Reduce compute costs for duplicate processing
KEY BENEFITS

Control Costs While Scaling AI Capabilities

15-20% Cost Reduction

Projected 15-20% reduction in AI compute costs through intelligent model selection, batch processing, and ROI tracking.

Complete Cost Visibility

Track AI spending by model, use case, and department with granular cost per inference tagging and reporting.

ROI-Based Control

Enforce AI Profitability Threshold to ensure only profitable operations execute, preventing wasteful spending.

READY TO OPTIMIZE YOUR AI COSTS?

Let's reduce your Snowflake and Azure AI workload costs

Schedule a demo to see how DagUI generates cost optimization pipelines that reduce AI compute costs by 15-20%.

Schedule a Demo
📞
Call: +1 416 407 0940
✉️
Email: info@wordjog.com