2 min read
Prompt Caching: The Performance Win Nobody Talks About
When you send the same prompt prefix repeatedly—system instructions, context documents, examples—the model recomputes them every time. Prompt caching stores…
Performance
104 articles
1 min read
Performance Optimization for .NET 9 Applications on Azure
Results: Cold start reduced from 800ms to 150ms. These optimizations combined typically reduce Azure compute costs by 30-50% while improving response times.…
1 min read
Semantic Caching for LLM Applications: Reducing Costs and Latency
Traditional caching requires exact matches. Users asking "What is Azure?" and "Can you explain Azure?" would generate two separate API calls. Semantic…
1 min read
Microsoft Fabric Capacity Planning: Right-Sizing Your Data Platform
For development and test environments, implement auto-pause policies to reduce costs during inactive periods. Monitor utilization patterns and adjust…
1 min read
Semantic Caching for LLM Applications: Reducing Costs and Latency
Unlike exact-match caching, semantic caching uses embeddings to find similar queries even when worded differently.
1 min read
Delta Lake Optimization: VACUUM, OPTIMIZE, and Z-ORDER Explained
VACUUM removes data files no longer referenced by the Delta log. Without regular vacuuming, your storage costs grow unbounded as old file versions accumulate.
1 min read
Implementing Semantic Caching for LLM Applications
Use Redis Vector Search for efficient similarity matching at scale. Tune the similarity threshold based on your use case - higher values ensure more precise…
1 min read
AI Drift Detection: Monitoring Model Performance Over Time
Proactive drift detection prevents silent AI performance degradation.
1 min read
Speculative Decoding: Accelerating LLM Generation
Speculative decoding can achieve 2-3x speedup without any quality degradation.
1 min read
Efficient Inference Patterns: Maximizing AI Throughput
Efficient inference patterns can improve throughput by 5-10x while reducing costs.
1 min read
NPU Programming: Leveraging Neural Processing Units
NPUs enable efficient on-device AI with 10-100x better power efficiency than CPUs.
1 min read
Prompt Caching Strategies: Reducing Latency and Cost
Smart caching can reduce AI costs by 30-50% for applications with repetitive queries.
1 min read
Vector Search Optimization: Scaling to Billions of Vectors
Quantization and HNSW tuning enable vector search at billion-scale with reasonable latency.
1 min read
Model Optimization Techniques: From Training to Deployment
Model optimization is both science and art. Start with the techniques that offer the best impact for your specific constraints.
1 min read
Efficient Inference: Optimization Techniques for Production AI
Efficient inference is crucial for production AI. Apply these techniques systematically and measure the impact at each step.
1 min read
Inference Optimization: Making AI Fast and Cost-Effective
Inference optimization is a continuous process. Start with caching and routing for quick wins, then progressively implement more sophisticated techniques.
1 min read
Scaling AI Systems: From Prototype to Enterprise
Scaling AI Systems: From Prototype to Enterprise
1 min read
Online Inference Patterns: Low-Latency ML Predictions at Scale
Online inference requires careful attention to latency, reliability, and scalability. Choose patterns based on your specific requirements and constraints.
1 min read
Rate Limit Handling: Maximizing Throughput Within Constraints
Effective rate limit handling is about working with the API, not against it. Use token buckets, queuing, and adaptive limits to maximize throughput while…
1 min read
Timeout Management in AI Applications: Balancing Speed and Completeness
Timeout management in AI applications requires balancing responsiveness with allowing complex operations to complete. Use adaptive timeouts, deadline…
1 min read
Parallel Function Calling: Execute Multiple Tools Simultaneously
Parallel function calling dramatically improves response times when multiple independent operations are needed. Use it wisely to build faster, more…
1 min read
Understanding Smoothing and Bursting in Fabric
Smoothing averages capacity consumption over a time window, allowing brief spikes without immediate throttling.
1 min read
Autoscale in Microsoft Fabric
Autoscale in Microsoft Fabric provides the flexibility to handle variable workloads while optimizing costs. Configure base capacity for typical load, allow…
1 min read
Microsoft Fabric Capacity Optimization
1. Monitor utilization patterns : Understand your usage before optimizing 2. Stagger workloads : Avoid concurrent peaks 3. Right size jobs : Use appropriate…
1 min read
Native Execution Engine in Microsoft Fabric
Native execution bypasses traditional JVM-based Spark execution to run queries using native code optimized for modern CPUs.
1 min read
Spark Optimization in Microsoft Fabric
1. Enable AQE : Adaptive Query Execution handles many optimizations automatically 2. Right size partitions : Target 128MB partitions 3. Broadcast small…
1 min read
Query Performance Tuning in Microsoft Fabric
1. Filter early : Push filters as close to data source as possible 2. Use appropriate joins : Broadcast small tables 3. Enable AQE : Adaptive Query…
1 min read
Z-Order vs V-Order: Choosing the Right Data Ordering
1. Always enable V Order : Low overhead, universal benefits 2. Add Z Order for specific needs : Known filter patterns 3. Limit Z Order columns : Maximum 3 4…
1 min read
V-Order: Microsoft Fabric's Performance Optimization
V-Order is a write-time optimization that physically reorders data within Parquet files to maximize compression and query performance, especially for Direct…
1 min read
Optimized Writes in Delta Lake
1. Enable by default : For most analytical workloads 2. Choose appropriate bin size : 128MB general, 256MB for Direct Lake 3. Combine with auto compact :…
1 min read
File Compaction Strategies in Delta Lake
1. Schedule regular compaction : Daily for high volume tables 2. Use partition aware compaction : Avoid rewriting entire large tables 3. Enable auto…
1 min read
Parquet File Optimization for Analytics
1. Target 128 256MB files : Optimal for most query engines 2. Use Snappy or ZSTD : Best compression/speed balance 3. Sort by filter columns : Improves…
1 min read
Filtered Vector Search in Azure AI Search: Best Practices
Pre-filtering: Filter before vector search Post-filtering: Filter after vector search Azure AI Search uses pre-filtering with automatic optimization.
1 min read
HNSW Tuning for Azure AI Search: A Practical Guide
HNSW builds a multi layer graph where: Higher layers : Sparse, for fast long range navigation Lower layers : Dense, for precise local search Layer 0 :…
1 min read
Scalar Quantization for Vector Search: A Practical Guide
Scalar quantization maps floating point values to integers by dividing the value range into buckets: Understanding quantization error helps set…
2 min read
Binary Vectors and Quantization in Azure AI Search
With quantization, this can be reduced to 1-2 GB.
1 min read
Improved Vector Search in Azure AI Search: Deep Dive
Vector search finds similar items by comparing their mathematical representations (embeddings). The key components: Embeddings : Dense vectors representing…
1 min read
Latency-Based LLM Routing: Real-Time AI Applications
Latency has multiple components: 1. Network latency : Request travel time 2. Queue time : Waiting for processing 3. Time to first token (TTFT) : Initial…
1 min read
Capacity Management in Microsoft Fabric
Effective capacity management is crucial for Fabric performance and cost control. Today I'm diving deep into capacity planning and optimization.
1 min read
ONNX Runtime: Cross-Platform AI Deployment
ONNX Runtime is the unsung hero of AI deployment. Today I'm exploring how to use it for consistent AI inference across platforms.
1 min read
GPT-4o Speed Improvements: Building Low-Latency Applications
GPT-4o is 2x faster than GPT-4 Turbo. Today I'm exploring how to leverage this speed for responsive applications.
1 min read
Provisioned Throughput Deep Dive: Architecture and Implementation
1. Multi region deployment Distribute for resilience 2. Monitor continuously Track utilization and latency 3. Plan scaling windows PTU changes aren't…
1 min read
Prompt Caching: Reducing Latency and Cost for Repetitive Contexts
Many applications send the same system prompt and context repeatedly: 1. Identify common prefixes System prompts, few shot examples 2. Batch similar…
1 min read
Caching Strategies for LLM Cost Reduction
1. Start with exact match Simple and effective 2. Add semantic caching For variable phrasing 3. Set appropriate TTL Balance freshness and savings 4. Monitor…
1 min read
Vector Compression in Azure AI Search: Reducing Costs Without Sacrificing Quality
Vector compression reduced storage costs dramatically in a production index I worked on. Here are the practical trade-offs and configuration tips I used to…
1 min read
Direct Lake Best Practices: Getting Import-Mode Performance Without the Import
Direct Lake can feel like magic until you hit its limits. In production I've learnt which constraints matter and how to tune tables and reports to stay in…
1 min read
Microsoft Fabric Performance Tuning: From Notebooks to Reports
When diagnosing Fabric performance, I've found the root cause can be anywhere from Spark configs to report visuals. This guide consolidates tuning…
1 min read
Microsoft Fabric Capacity Management: Optimization Strategies
Capacity choices in Fabric determine both performance and cost. From real projects, I'll share the monitoring signals and optimization steps that actually…
1 min read
Query Performance Optimization in Microsoft Fabric
Query performance is where UX and cost meet. My practical approach is to measure user-facing latency, inspect query plans, and prioritise optimisations that…
1 min read
V-Order Optimization: Deep Dive into Fabric's Secret Weapon
V-Order feels like a secret weapon because it's a write-time optimisation with outsized read-time benefits. In practice I enable V-Order on wide, heavily…
1 min read
Direct Lake Optimization: Maximizing Power BI Performance
Direct Lake changes the Power BI performance story — but it's not automatic. Over the past months I've seen Direct Lake deliver dramatic improvements when…
1 min read
Fabric Performance Tuning: Optimizing Your Data Platform
Performance tuning in Fabric blends write-time optimisations (v-ordering, partitioning) with query-time strategies (predicate pushdown, materialised views).…
1 min read
Parallel Execution in LangChain: Optimizing LLM Applications
Master parallel execution patterns in LangChain to build faster, more efficient LLM applications.
1 min read
Context Caching for LLM Applications
Tomorrow we'll explore conversation summarization techniques. Redis Caching Semantic Search LLM Caching Patterns
1 min read
GPU Optimization for LLM Workloads
Tomorrow we'll explore Azure ML compute options. PyTorch CUDA Semantics Flash Attention NVIDIA Optimization Guide
1 min read
Batching Strategies for LLM Inference
Tomorrow we'll explore GPU optimization techniques. Continuous Batching Paper vLLM Batching Triton Inference Server
1 min read
Efficient Inference: Optimizing LLM Response Times
Tomorrow we'll explore batching strategies in detail. vLLM TensorRT LLM Speculative Decoding Paper
1 min read
Quantization Basics: Reducing Model Size and Improving Speed
Tomorrow we'll explore INT8 quantization in more detail. PyTorch Quantization bitsandbytes GPTQ Paper
1 min read
Model Optimization Techniques for Production Deployment
Tomorrow we'll explore quantization basics in more detail. PyTorch Quantization Flash Attention torch.compile
1 min read
ONNX Runtime: Optimizing Model Inference
ONNX Runtime is the inference engine I reach for when a Python-trained model needs to be deployed somewhere other than a Python service — a .NET…
1 min read
Query Insights in Fabric: Monitoring and Optimization
Tomorrow we'll explore Fabric Real Time Analytics. Query Insights Documentation Performance Tuning Monitoring DMVs
1 min read
Response Streaming: Real-Time LLM Output
Streaming improves the user experience significantly. Tomorrow, I will cover error handling for AI applications.
1 min read
Caching Strategies for LLM Applications
Effective caching significantly reduces LLM costs and latency. Tomorrow, I will cover response streaming patterns.
1 min read
Direct Lake Mode: Power BI Performance Without Import Overhead
Direct Lake is the quiet superpower of Microsoft Fabric, and I think it's underexplained in most of the post-Build coverage. The classic Power BI trade-off…
1 min read
LLM Caching Strategies: Reducing Costs and Latency
LLM caching strategies are essential for production systems. By combining exact matching, semantic similarity, and intelligent invalidation, you can…
1 min read
Streaming Responses with Azure OpenAI: Real-Time AI Output
Without streaming, users wait for the entire response: 500 tokens at 50 tokens/second = 10 seconds of waiting Users see nothing, then everything at once…
1 min read
Rate Limiting and Throttling with Azure OpenAI Service
Azure OpenAI uses Tokens Per Minute (TPM) as the primary quota metric: Model Default TPM Max TPM (with increase) GPT 3.5 Turbo 120K 300K+ Text Davinci 003…
1 min read
Right-Sizing Azure Resources: A Data-Driven Approach
1. Collect metrics CPU, memory, network, IOPS 2. Analyze patterns Peak, average, trends 3. Identify candidates Under utilized resources 4. Recommend changes…
1 min read
Auto-Scaling Best Practices in Azure
Auto scaling is powerful but requires careful tuning. Start with conservative settings, monitor behavior, and adjust based on real data. Combine metric…
1 min read
Azure Cosmos DB Best Practices for 2022
The partition key decision is crucial and difficult to change later: New in 2022 partition up to 3 levels: Optimize indexing for your query patterns: Write…
1 min read
Black Friday Azure Tips: Cost Optimization for Peak Traffic
Don't wait for auto scaling to catch up during traffic spikes: Reduce database load with intelligent caching: Use read replicas and query optimization:…
1 min read
Output Caching in ASP.NET Core 7: A Complete Guide
Output caching stores the complete HTTP response and serves it directly for subsequent identical requests, bypassing the entire request pipeline. Unlike…
2 min read
.NET 7 Release Candidate: Final Preview Before GA
.NET 7 continues the tradition of making .NET faster and more cloud-native with each release. Here's a preview of the key improvements we'll see at GA.
1 min read
GitHub Actions Caching Strategies
Proper caching can reduce build times by 50-80% for dependency-heavy projects.
1 min read
GitHub Actions Larger Runners Deep Dive
Larger runners can dramatically reduce build times when used effectively with parallelizable workloads.
1 min read
Burst Capacity in Azure Cosmos DB
When your Cosmos DB container isn't fully utilizing its provisioned throughput, the unused capacity accumulates as burst credits. These credits can be used…
1 min read
Enrichment Cache in Azure Cognitive Search
Enrichment cache significantly reduces costs and improves performance for AI-enriched search solutions.
2 min read
Scaling Reads with MySQL Read Replicas on Azure
Read replicas in Azure Database for MySQL Flexible Server enable scaling out read-heavy workloads by directing reporting queries, analytical queries, and…
1 min read
Data Colocation in Citus for Optimal Join Performance
When two distributed tables share the same distribution column and colocation group, their corresponding shards are placed on the same worker node.
1 min read
Mastering Distributed Queries in Citus
Understanding these query patterns helps you leverage Citus effectively for high-performance distributed PostgreSQL applications.
1 min read
Using Reference Tables in Citus for Efficient Joins
Reference tables eliminate network round-trips for joins, significantly improving query performance in distributed setups.
1 min read
.NET 7 Preview: Performance and Cloud-Native Features
Generic math interfaces Required members Improved minimal APIs Native AOT compilation Enhanced performance One of the most anticipated features is generic…
2 min read
Photon Engine: Accelerating Spark Workloads on Databricks
Photon is Databricks' native vectorised query execution engine written in C++—a replacement for the JVM-based Apache Spark execution engine for SQL and…
1 min read
Synapse Serverless SQL Performance: Optimization Strategies
Serverless SQL charges based on data processed. Optimization reduces both cost and query time.
1 min read
Power BI Automatic Aggregations: AI-Powered Query Optimization
Automatic aggregations bring AI-powered optimization to Power BI, making it easier than ever to achieve excellent query performance on large datasets.
2 min read
Workload Management in Azure Synapse
Workload management in Synapse Dedicated SQL Pool is the resource governance system that prevents low-priority ad-hoc queries from consuming all compute and…
2 min read
Result Set Caching for Query Acceleration in Synapse
Result set caching in Synapse Dedicated SQL Pool stores the output of a query in the dedicated pool's storage and returns the cached result for subsequent…
2 min read
High-Performance Data Loading with COPY Command in Synapse
The COPY command replaced PolyBase as the recommended data loading mechanism for Synapse Dedicated SQL Pool because it's simpler to use and performs at the…
2 min read
Azure Synapse Dedicated SQL Pool Best Practices
Synapse Dedicated SQL Pool (formerly Azure SQL Data Warehouse) is the massively parallel processing (MPP) engine for structured, petabyte-scale analytical…
2 min read
Materialized Views in Azure Data Explorer
Materialized views in ADX pre-aggregate data according to a defined query, updating continuously as new data is ingested—so when you query the view, you're…
1 min read
Mastering Azure CDN Caching Rules and Optimization
CDN caching rules are the configuration that determines how long cached content stays at the edge before the CDN checks with the origin for updates—and…
1 min read
Accelerating Content Delivery with Azure CDN
Azure CDN significantly improves application performance by reducing latency and offloading traffic from origin servers.
1 min read
Azure Cache for Redis: High-Performance Caching Strategies
Azure Cache for Redis is the caching layer I add to almost every production Azure application that has read-heavy patterns with data that doesn't change on…
1 min read
Azure Cosmos DB Partition Strategies for Optimal Performance
Proper partition key design is fundamental to Cosmos DB success. Take time to analyze your access patterns and data distribution before finalizing your…
1 min read
Azure SQL Automatic Tuning: Self-Healing Database Performance
Azure SQL's Automatic Tuning is the machine learning system that acts on Query Store data to make index and query plan decisions that would otherwise…
1 min read
Query Store Deep Dive: Your Database's Flight Recorder
Query Store transforms database performance troubleshooting from guesswork into data-driven analysis, making it an indispensable tool for maintaining…
1 min read
Intelligent Query Processing in Azure SQL: Automatic Performance Optimization
IQP represents a significant step toward self-tuning databases, reducing the manual effort required to optimize query performance while automatically…
1 min read
Azure Front Door Standard/Premium: Global Application Delivery
Azure Front Door Standard/Premium is the convergence I've been waiting for since the days when you had to choose between classic Front Door (global routing…
2 min read
Cosmos DB Integrated Cache: Reducing RU Costs at Scale
The Cosmos DB integrated cache is the feature I've been waiting for since the first time I watched a read-heavy application burn through its RU budget…
1 min read
Power BI Incremental Refresh: Efficient Large Dataset Updates
"Why is the dataset refresh taking three hours?" is the question that eventually leads every Power BI shop to incremental refresh. The first time I switched…
1 min read
Load Testing Azure Applications with Apache JMeter
Deploy JMeter on Azure VMs for distributed testing: Run JMeter in containers for quick, scalable tests: Metric Description Target Response Time (avg)…
1 min read
Azure Cache for Redis: Caching Patterns
Redis caching transforms application performance at scale.
1 min read
Power BI Incremental Refresh: Handle Large Datasets
Right-click table → Incremental refresh Incremental refresh transforms multi-hour refreshes into minutes.
1 min read
Azure Front Door: Global Load Balancing and CDN
Front Door is the global entry point for serious production workloads.