kynetradb

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

vs
Typesense

Open-source typo-tolerant search engine in C++; single binary, fast, no Elasticsearch complexity.

DimensionkynetradbTypesense
Full-text searchBM25 — reference catalogueBM25
Vector searchBrute-force cosine evaluationTypesense uses HNSW which scales better past ~100k vectorsHNSW
AuthPreview scopeBuilt-in
Row-Level SecurityPreview scopeNo
File storagePreview scopeNone
RealtimePreview scopeNone
Edge FunctionsPreview scopeNo
TypeScript SDKPreview scopeNo official client SDK
KV lookupsPreview scopeNo
Document filterPreview scopeYes
LLM runtimePreview scopeNo
Outbound DB syncPreview scopeNo
Self-hostPreview scopeYes
Single binaryPreview scopeYes
LicensePreview terms — request a scoped evaluationGPL-3.0
Deploy targets19 target profiles1 listed target profiles
Free tierPreview access by requestyes — self-host free; Typesense Cloud trial

When to pick Typesense

Closer to Algolia than Elasticsearch in UX; one of the fastest BM25 engines in benchmarks. No auth, files, KV, or realtime beyond webhooks.

  • You need merchandising rules, A/B search testing, or built-in analytics — kynetradb has none of those today.
  • Your team is already invested in Typesense'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.

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"
  }'
Typesense
// Typesense — JavaScript client
const results = await client.collections('products').documents().search({
  q: 'aurora espresso',
  query_by: 'title,vendor',
  per_page: 10,
});