kynetradb vs Weaviate

kynetradb

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

Weaviate

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

Feature comparison

Dimension	kynetradb	Weaviate
Full-text search	BM25 (parallel, 1.07 ms @ 100k)	BM25
Vector search	Brute-force cosine (2.21 ms @ 100k, no HNSW yet) Weaviate uses HNSW which scales better past ~100k vectors	HNSW
Auth	Built-in — email+password, refresh tokens, OTP/magic-link, OAuth (Google/GitHub), anonymous, /auth/v1	Built-in
Row-Level Security	Built-in — auth.uid() DSL, per-row enforcement, SELECT/INSERT/UPDATE/DELETE	No
File storage	Built-in — buckets, public/private, signed URLs, object RLS, local + S3-compatible	None
Realtime	WebSocket — postgres_changes (INSERT/UPDATE/DELETE), presence, broadcast; RLS-filtered	None
Edge Functions	Built-in — WASM /functions/v1, sandboxed, 10s timeout, SDK .invoke()	No
TypeScript SDK	@kynetra/client — supabase-js-compatible, from().select().eq()	No official client SDK
KV lookups	Yes (point lookup by ID)	No
Document filter	Yes (JSON predicates)	Yes
LLM runtime	Yes (Anthropic + OpenAI + Ollama)	No
Outbound DB sync	Yes (12 sinks: Postgres, DynamoDB, BQ, Firestore, CF, Mongo, Redis, Pinecone)	No
Self-host	Yes (single binary)	Yes
Single binary	Yes	No
License	Apache-2.0	BSD-3-Clause
Deploy targets	18 (1-click)	1 (1-click)
Free tier	Yes — Apache-2.0, self-host free	yes — 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 Supabase's developer experience — the @kynetra/client TypeScript SDK, PostgREST REST API, GoTrue-shaped auth, RLS, realtime channels — running as a single binary on infra you own.
You need BM25 full-text + vector similarity search built in with no separate service and no CDC pipeline.
You want to deploy to 18 targets (including 5 Indian providers) from one Dockerfile.
You need outbound sync to 12 databases (Postgres, DynamoDB, BigQuery, Firestore, Cloudflare, MongoDB, Redis, Pinecone) with zero extra code.
You want an agentic admin with 10 typed LLM-driven actions and a persisted audit trail.
You want Apache-2.0 with a self-host path that doesn't require an ops team.
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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