kynetradb vs Elasticsearch

kynetradb

One Rust binary: BM25 search + vector + KV + document + auth + files + realtime + agentic admin.

Elasticsearch

Distributed Lucene-based search + analytics engine with kNN vector search and the ELK stack.

Feature comparison

Dimension	kynetradb	Elasticsearch
Full-text search	BM25 — reference catalogueElasticsearch's Lucene is more mature with fuzzy/synonym support	lucene
Vector search	Brute-force cosine evaluationElasticsearch uses HNSW which scales better past ~100k vectors	HNSW
Auth	Preview scope	Built-in
Row-Level Security	Preview scope	No
File storage	Preview scope	None
Realtime	Preview scope	None
Edge Functions	Preview scope	No
TypeScript SDK	Preview scope	No official client SDK
KV lookups	Preview scope	No
Document filter	Preview scope	Yes
LLM runtime	Preview scope	No
Outbound DB sync	Preview scope	No
Self-host	Preview scope	Yes
Single binary	Preview scope	No
License	Preview terms — request a scoped evaluation	SSPL
Deploy targets	19 target profiles	0 listed target profiles
Free tier	Preview access by request	yes — Elastic Cloud 14-day trial; self-host free (SSPL)

When to pick Elasticsearch

A decade of search tuning, mature observability stack (Kibana, Logstash), and the best fuzzy/synonym/multi-language support in the market.

You need fuzzy matching, synonyms, multi-language analyzers, or the Kibana observability stack.
Your team is already invested in Elasticsearch's SDK and ecosystem.

When to pick kynetradb

You want a comparison-led conversation about BM25 full-text, vector evaluation, and data-plane trade-offs.
You need a product-specific implementation scope rather than a generic migration claim.
You want to assess 19 hosting target profiles, including 5 Indian providers.
You want a scoped preview conversation about the data, deployment, and operating boundary.
You want a single binary with no runtime dependencies — no container fleet to operate.

Search query — both APIs side by side

Full-text search call. These are documentation-accurate shapes, not runnable end-to-end examples.

kynetradb

# kynetradb — BM25 search
curl -X POST https://your.host/v1/search \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "aurora espresso",
    "top_k": 10,
    "kind": "product"
  }'

Elasticsearch

# Elasticsearch — REST API
curl -X POST http://localhost:9200/products/_search \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "match": { "title": "aurora espresso" }
    },
    "size": 10
  }'

See all 20 comparisons Elasticsearch homepage ↗