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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

DimensionkynetradbPinecone
Full-text searchBM25 — reference catalogueNone
Vector searchBrute-force cosine evaluationPinecone uses HNSW which scales better past ~100k vectorsHNSW
AuthPreview scopeNone
Row-Level SecurityPreview scopeNo
File storagePreview scopeNone
RealtimePreview scopeNone
Edge FunctionsPreview scopeNo
TypeScript SDKPreview scopeNo official client SDK
KV lookupsPreview scopeNo
Document filterPreview scopeYes
LLM runtimePreview scopeNo
Outbound DB syncPreview scopeNo
Self-hostPreview scopeNo (managed only)
Single binaryPreview scopeNo
LicensePreview terms — request a scoped evaluationSaaS-only
Deploy targets19 target profiles0 listed target profiles
Free tierPreview access by requestyes — 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 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.
  • You need to run on your own infra — Pinecone is managed-only.
  • 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
  }'
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}'
See all 20 comparisonsPinecone homepage ↗