kynetradb
One Rust binary: BM25 search + vector + KV + document + auth + files + realtime + agentic admin.
vs
Pinecone
Managed serverless vector database built for ML teams — insert, query, filter at any scale.
Feature comparison
| Dimension | kynetradb | Pinecone |
|---|---|---|
| Full-text search | BM25 (parallel, 1.07 ms @ 100k) | None |
| Vector search | Brute-force cosine (2.21 ms @ 100k, no HNSW yet) Pinecone uses HNSW which scales better past ~100k vectors | HNSW |
| Auth | Built-in (bcrypt + JWT, 3 roles) | None |
| File storage | Built-in (local + S3-compatible, SigV4) | None |
| Realtime | SSE (topic + kind filters) | None |
| 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 — 1 index, 100k vectors |
When to pick Pinecone
Managed serverless vector with zero infra, massive scale, and the deepest LangChain/LlamaIndex ecosystem integrations. No self-host option.
- You need HNSW at scale past ~100k vectors — kynetradb uses brute-force today.
- Your team is already invested in Pinecone's SDK and ecosystem.
When to pick kynetradb
- You need BM25 full-text + vector similarity + auth + files + realtime in one process — no external services.
- 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 — Pinecone is managed-only.
- You want a single binary with no runtime dependencies.
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
}' Pinecone
# Pinecone — REST API
# Upsert
curl -X POST https://INDEX_NAME.svc.ENV.pinecone.io/vectors/upsert \
-H "Api-Key: $PINECONE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"vectors": [{"id": "aurora-espresso", "values": [0.1, 0.2, 0.3, 0.4]}]
}'
# Query
curl -X POST https://INDEX_NAME.svc.ENV.pinecone.io/query \
-H "Api-Key: $PINECONE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"vector": [0.1, 0.2, 0.3, 0.4], "topK": 10}'