AACSearch vs Elasticsearch

Elasticsearch is a distributed search and analytics engine — flexible enough to power log analytics, observability, and search, with a query DSL that can express almost anything. That flexibility comes with operational weight: cluster sizing, shard management, relevance tuning that requires understanding BM25 internals, and typically a dedicated search or platform team to run it well for a customer-facing storefront.

AACSearch is narrower on purpose: it's built for one job — catalogue and content search for a product or storefront — and gives up Elasticsearch's general-purpose flexibility in exchange for a hosted service with merchandising, personalization and quality tooling already built for that job.

Operational model

Elasticsearch: you provision a cluster (or use a managed offering), design an index mapping, tune analyzers, and typically write custom relevance scoring by hand. AACSearch: you define a collection schema through the API or console, and typo tolerance, faceting, and ranking work out of the box, with merchandising rules layered on top without touching a scoring script.

Relevance tuning

Elasticsearch's function_score and script scoring can express nearly any ranking logic, but require you to write and maintain it. AACSearch exposes the same kind of control — boosts, custom sort expressions — through a declarative rules API, plus a quality lab to measure whether a change actually improved relevance before you ship it.

When Elasticsearch is the better fit

If you need one system for search, log analytics, and observability, or you have complex aggregation and analytics requirements beyond product/content search, Elasticsearch's generality is a genuine advantage. AACSearch is not trying to be a log analytics platform.

Функция	AACSearch	Elasticsearch
Purpose-built for catalogue/content search	Yes	General-purpose
Operational overhead	None (hosted)	Cluster ops, sizing, upgrades
Merchandising rules	Declarative API	Hand-written scripts
Personalization & recommendations	Built in	Build it yourself
Vector & hybrid search	Built in	Built in (kNN + BM25)
Log analytics / observability use cases	Not the focus	Strong fit
Relevance evaluation tooling	Built in (Quality Lab)	Not built in
AI assistant with citations	Built in	Build it yourself (with a vector store)

Frequently Asked Questions

Can AACSearch replace Elasticsearch for log analytics too?

No — AACSearch is purpose-built for catalogue and content search, not log or metrics analytics. If you need both, most teams run them as separate systems regardless of which search platform they pick.

Is AACSearch's ranking as flexible as Elasticsearch's function_score?

For the common merchandising cases — boost, bury, pin, exclude, redirect — AACSearch's declarative rules cover it without a scoring script. For arbitrary custom scoring formulas, Elasticsearch's scripting is more expressive, at the cost of writing and maintaining that script yourself.

How much operational work does AACSearch save compared to self-managed Elasticsearch?

Cluster sizing, shard rebalancing, version upgrades, and relevance-script maintenance disappear entirely — you interact with a REST API and a console.