Latency and cost determine retrieval backend

This comparison says the backend choice for large RAG systems is often an infrastructure decision, not a relevance decision. In Indeed's testing, TurboPuffer did not open a clear quality gap versus Vespa or Elasticsearch on retrieval metrics, so the deciding factor became which system could serve millisecond queries more cheaply as data got colder, traffic got spikier, and only a small slice of each corpus mattered for each user session.

• The cost win showed up in a very specific corpus shape, lots of cold data plus bursty demand. The team described per customer or per tenant retrieval where most records are irrelevant to a given session, so paying to keep the whole corpus hot would waste memory and SSD spend.
  1 sacra
• TurboPuffer is built around object storage as the source of truth, then promotes active namespaces into SSD and memory caches. Its docs say the first cold query can be much slower, then cached queries drop sharply, which matches the interview's tradeoff, strong steady latency with occasional cold fetch penalties.
  2 turbopuffer 3 turbopuffer
• Vespa and Elasticsearch matter more when ranking logic itself is the product. Vespa exposes rank profiles, rank features, and machine learned models in the serving path, while Elasticsearch is part of a broader search stack. That makes them stronger when teams need custom ranking, hybrid retrieval tuning, and recommendation logic, not just cheaper generic retrieval.
  1 sacra 4 vespa 5 elastic

The next step in this market is a clearer split between retrieval engines for generic agent answers and serving systems for high value ranking. As agent workloads get larger and more tenant segmented, storage tiering and serverless operations should pull more traffic toward TurboPuffer like systems, while personalization heavy products should keep favoring engines like Vespa that let ranking teams program the result order directly.