kynetradb
One Rust binary: BM25 search + vector + KV + document + auth + files + realtime + agentic admin.
vs
Qdrant
Rust-native vector similarity search engine with HNSW, payload filtering, and distributed mode.
Feature comparison
| Dimension | kynetradb | Qdrant |
|---|---|---|
| Full-text search | BM25 (parallel, 1.07 ms @ 100k) | None |
| Vector search | Brute-force cosine (2.21 ms @ 100k, no HNSW yet) Qdrant 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 | Yes |
| License | Apache-2.0 | Apache-2.0 |
| Deploy targets | 18 (1-click) | 1 (1-click) |
| Free tier | Yes — Apache-2.0, self-host free | yes — Qdrant Cloud free tier |
When to pick Qdrant
The fastest and most memory-efficient dedicated vector engine in benchmarks. If you need pure vector search at scale, Qdrant is purpose-built for it.
- You need HNSW at scale past ~100k vectors — kynetradb uses brute-force today.
- Your team is already invested in Qdrant's SDK and ecosystem.
When to pick kynetradb
- You want Supabase's developer experience — the
@kynetra/clientTypeScript 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.
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
}' Qdrant
# Qdrant — REST API
# Upsert
curl -X PUT http://localhost:6333/collections/products/points \
-H "Content-Type: application/json" \
-d '{
"points": [{"id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "payload": {"title": "Aurora Espresso"}}]
}'
# Query
curl -X POST http://localhost:6333/collections/products/points/search \
-H "Content-Type: application/json" \
-d '{"vector": [0.1, 0.2, 0.3, 0.4], "limit": 10}'