Should You Use Read Replicas or Caching?

Replicas are a common way to scale read traffic, but they don’t reduce compute. Every query still gets parsed, planned, and executed. Add lag and infrastructure cost, and teams hit scaling limits fast.

Here are a few reasons why teams quickly outgrow replicas and what to consider instead:

• Heavy query logic: Replicas still run full query plans including joins, sorts, and filters. Caching avoids repeating this work for frequent, expensive queries.
• Stale data: With async replication, lag can lead to outdated results. Caching gives you control over freshness using TTLs or app-specific invalidation, although this increases complexity.
• Rising costs: Replicas improve read capacity, but costs grow linearly. Caching can be more cost-efficient if implemented and maintained carefully.

That said, replicas still have their place:

• Read-after-write isolation: Sync or semi-sync replicas help when a separate process needs fresh data immediately without risking stale reads.
• Geographic distribution: Placing replicas closer to end users can reduce read latency in global apps where caching can’t fully cover dynamic access patterns.

Replicas can still be helpful, but for scaling read-heavy workloads, caching is often faster, cheaper, and more efficient when managed well. This post explains why in detail and delves into how Readyset removes the complexity and maintenance that comes with traditional caching.

🔍 What We’re Watching in the Database Ecosystem

Bringing Generative AI to your Data: Semantic Operators in Azure Database for PostgreSQL Azure Database for PostgreSQL now supports SQL-level LLM integration with four new azure_ai semantic operators: is_true, extract, generate, and rank. These let you embed AI-powered truth checks, entity extraction, text generation, and document reranking directly in your queries.

Timescale Is Now TigerData After eight years focused on time-series, Timescale has rebranded to TigerData, signaling its evolution into a full-featured PostgreSQL platform optimized for real-time analytics, AI vectors, and hybrid workloads.

SQL Server 2025 Launched Into Public Preview Microsoft just launched SQL Server 2025 into public preview, marking its most significant release in a decade. The update brings built-in AI features like vector search and semantic querying, zero‑ETL real‑time analytics via Fabric mirroring, tighter developer experiences with REST, JSON, and Regex support, and enhanced cloud flexibility through Azure Arc.

In Case You Missed It

Readyset QueryPilot Beta is Now Open. Say Goodbye to Manual Tuning and Scaling Headaches.

Tired of chasing slow queries and scrambling when traffic spikes?

QueryPilot takes manual query tuning off your plate. It automatically detects and caches your most demanding production queries with no TTLs, invalidation logic, or code changes. It also routes reads intelligently across replicas to protect your primaries and scale smoothly, even during peak load events.

We’re now accepting beta users. Be among the first to try QueryPilot and experience effortless database performance. Get early access here.