Performance Optimization in Azure AI Search - Ansi ByteCode LLP
                     Performance 
Optimization in 
Azure AI Search
Azure AI Search enables developers to build 
high-performance search applications. 
However, as data grows, ensuring optimal 
speed and efficiency becomes challenging. This 
guide explores advanced techniques to 
optimize query performance and indexing 
efficiency with real-world examples and code 
snippets.
Optimizing Index Configurations for Faster Queries
Choose the Right Field Types
Selecting the correct field types reduces storage overhead and improves query 
performance.
• Use Edm.String for text fields.
• Use Edm.Int32 or Edm.Double for 
numerical data.
• Set fields as searchable, filterable, or 
sortable based on query needs.
Optimize Index Size
• Avoid excessive filterable or sortable fields.
• Use facetable fields only where necessary.
• Remove unused fields to minimize index size.
Enhancing Query Performance
Implement Efficient Query Filtering
• Use $filter to refine queries and reduce dataset size.
• Filter fields should be indexed as filterable for better efficiency.
Optimize Query Execution with $select
Reduce payload size by selecting only required fields.
Improve Scoring Profiles
Enhance relevance ranking with custom scoring 
profiles.
• Boost recent products with higher 
relevance.
• Adjust boost values based on user 
search intent.
Caching for Faster Search Results
Caching helps reduce query latency and improves response times by storing frequently 
accessed data.
Enable Azure Front Door or Azure CDN for Caching
• Use Azure Front Door or Azure CDN to 
cache search responses closer to users.
• Reduces repeated queries to Azure AI 
Search, improving performance.
Leverage Application-Level Caching
• Use Redis Cache or Azure Cache for Redis to 
store frequent queries.
• Implement a TTL (Time-to-Live) strategy to 
refresh stale data.
• Use Sliding Expiration to extend cache 
lifetime when frequently accessed.
• Retrieves results from Redis if available; 
otherwise, fetches from Azure AI Search and 
caches them.
Scaling Azure AI Search for Large Datasets
Choosing the Right Service Tier
• Basic & Standard – Suitable for small to medium datasets.
• Standard 3 & Storage Optimized – Best for high-volume queries.
Managing Replicas and Partitions
• Increase Replicas – Enhances query throughput.
• Increase Partitions – Improves index storage capacity.
Monitoring and Troubleshooting Performance Issues
Using Azure Monitor and Logs
Enable diagnostic logs to track query performance.
Analyzing High-Latency Queries
• Use Azure Metrics Explorer to track 
query duration.
• Identify slow queries and optimize filters 
and indexes.
Improving Indexing Performance
Use Bulk Indexing for Faster Data Ingestion
• Use batch uploads for better performance.
• Avoid sending single document updates frequently.
• Batch documents in chunks of 1,000 for optimal speed.
Implement Incremental Updates
Reduce unnecessary re-indexing with partial updates.
• Only update changed fields instead of reindexing entire 
documents.
Tools and Resources for Optimization
• Azure Metrics Explorer – Monitor query 
latency and indexing speed.
• Azure Cognitive Search REST API – 
Automate search configurations.
• Application Insights – Identify 
performance bottlenecks.
Conclusion
Optimizing Azure AI Search ensures faster query execution, efficient indexing, and 
scalable performance. Implement these strategies to improve search relevance and 
user experience.
Need Expert Guidance?
Ansi ByteCode LLP specializes in Azure AI Search optimization. Contact us for tailored 
solutions to enhance your search performance.
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