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kynetradb

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

vs
Weaviate

Open-source vector database with hybrid search (BM25 + HNSW), modules, and GraphQL API.

Feature comparison

DimensionkynetradbWeaviate
Full-text searchBM25 — reference catalogueBM25
Vector searchBrute-force cosine evaluationWeaviate 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 scopeNo
LicensePreview terms — request a scoped evaluationBSD-3-Clause
Deploy targets19 target profiles1 listed target profiles
Free tierPreview access by requestyes — Weaviate Cloud sandbox

When to pick Weaviate

The most feature-rich open-source vector DB: hybrid BM25+HNSW, multi-tenancy, generative modules. Operationally heavier than kynetra.

  • You need HNSW at scale past ~100k vectors — kynetradb uses brute-force today.
  • Your team is already invested in Weaviate'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.

Vector upsert + query — both APIs side by side

Upsert an embedding and query nearest neighbours. These are documentation-accurate shapes, not runnable end-to-end examples.

kynetradb
# kynetradb — upsert entity with embedding
curl -X POST https://your.host/v1/entities \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "kind": "product",
    "attrs": { "title": "Aurora Espresso" },
    "embedding": [0.1, 0.2, 0.3, 0.4]
  }'

# kynetradb — vector similarity query
curl -X POST https://your.host/v1/vector \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "vector": [0.1, 0.2, 0.3, 0.4],
    "top_k": 10
  }'
Weaviate
# Weaviate — REST API
# Upsert
curl -X POST http://localhost:8080/v1/objects \
  -H "Content-Type: application/json" \
  -d '{
    "class": "Product",
    "properties": { "title": "Aurora Espresso" },
    "vector": [0.1, 0.2, 0.3, 0.4]
  }'

# Query via GraphQL
curl -X POST http://localhost:8080/v1/graphql \
  -d '{"query": "{ Get { Product(nearVector: {vector: [0.1,0.2,0.3,0.4]}, limit: 10) { title } } }"}'
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