DO YOU STILL NEED RAG? CAG IS VIABLE NOW — AND THE 2026 VECTOR DB DEFAULT IS SHIFTING
Longer context windows make Cache‑Augmented Generation workable for many internal apps, changing when you actually need a vector database. CAG keeps the corpus...
Longer context windows make Cache‑Augmented Generation workable for many internal apps, changing when you actually need a vector database.
CAG keeps the corpus in the prompt and skips retrieval, which simplifies stacks and cuts latency for stable, bounded docs. See the case for CAG in this explainer: Cache‑Augmented Generation.
If you still need retrieval, the 2026 tradeoffs got clearer: the roundup argues Qdrant is the default for most RAG, Pinecone is the easiest managed choice under ~10M vectors, Weaviate leans into built‑in vectorization and shipped an MCP server, and Milvus remains the scale play with a new WAL design. Details: Pinecone vs Weaviate vs Milvus vs Qdrant.
One caveat: long context can slow local inference as KV caches grow and memory gets tight. That matters if you plan to run models on your own hardware: Why Local LLMs Slow Down at Long Context.
You can drop retrieval for some internal assistants and ship faster with less infra if your corpus fits in context.
If you do need retrieval, newer Qdrant/Milvus/Weaviate updates change cost, ops, and integration decisions this year.
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terminal
Run a bake‑off: CAG vs your current RAG on a real doc set (latency, accuracy, infra cost).
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terminal
Prototype Qdrant v1.14 and measure index build time, filtered recall, and Multi‑AZ failover vs your incumbent.
Legacy codebase integration strategies...
- 01.
Carve out stable, bounded domains from your RAG stack to pilot CAG and simplify infra where it holds up.
- 02.
If you run Milvus, assess the 2.6 Woodpecker WAL shift and what it removes from your Kafka/Pulsar ops.
Fresh architecture paradigms...
- 01.
Start with CAG if your docs fit in a single prompt and don’t change often; fall back to RAG as the corpus grows.
- 02.
If choosing a vector DB, default to Qdrant unless you’re >100M vectors (Milvus) or want built‑in vectorizers (Weaviate).
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