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kynetradb

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

vs
Redis

In-memory KV store with optional modules: RediSearch (BM25) and Redis Vector (HNSW).

Feature comparison

DimensionkynetradbRedis
Full-text searchBM25 — reference catalogueBM25
Vector searchBrute-force cosine evaluationRedis uses HNSW which scales better past ~100k vectorsHNSW
AuthPreview scopeBuilt-in
Row-Level SecurityPreview scopeNo
File storagePreview scopeNone
RealtimePreview scopepolling
Edge FunctionsPreview scopeNo
TypeScript SDKPreview scopeNo official client SDK
KV lookupsPreview scopeYes
Document filterPreview scopeYes
LLM runtimePreview scopeNo
Outbound DB syncPreview scopeNo
Self-hostPreview scopeYes
Single binaryPreview scopeYes
LicensePreview terms — request a scoped evaluationRSAL
Deploy targets19 target profiles0 listed target profiles
Free tierPreview access by requestyes — Redis Cloud 30 MB

When to pick Redis

Sub-millisecond KV at scale is Redis's home turf. For pure speed-of-read caching, nothing beats it. The license changed from BSD to RSAL in 2024.

  • You need unlimited horizontal scale for a single access pattern.
  • Your team is already invested in Redis'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.

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
  }'
Redis
# See Redis documentation at https://redis.io
See all 20 comparisonsRedis homepage ↗