kynetradb

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

vs
Algolia

SaaS search-as-a-service with typo tolerance, merchandising rules, A/B testing, and analytics.

Dimension kynetradb Algolia
Full-text search BM25 (parallel, 1.07 ms @ 100k) BM25
Vector search Brute-force cosine (2.21 ms @ 100k, no HNSW yet) Algolia 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 None
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) No (managed only)
Single binary Yes No
License Apache-2.0 SaaS-only
Deploy targets 18 (1-click) 0 (1-click)
Free tier Yes — Apache-2.0, self-host free yes — 10k records, 10k search requests/mo

When to pick Algolia

Best-in-class typo tolerance, DPR (dynamic re-ranking), and analytics for storefront search. Kynetra has no merchandising rules or analytics today.

  • You need merchandising rules, A/B search testing, or built-in analytics — kynetradb has none of those today.
  • Your team is already invested in Algolia'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 need to run on your own infra — Algolia is managed-only.
  • You want a single binary with no runtime dependencies — no container fleet to operate.

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"
  }'
Algolia
// Algolia — JavaScript client
const results = await index.search('aurora espresso', {
  filters: 'kind:product',
  hitsPerPage: 10,
});