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kynetradb

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

vs
Chroma

Open-source AI-native vector database built for LLM applications — simple Python-first API.

Feature comparison

DimensionkynetradbChroma
Full-text searchBM25 — reference catalogueNone
Vector searchBrute-force cosine evaluationChroma 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 scopeYes
Single binaryPreview scopeNo
LicensePreview terms — request a scoped evaluationApache-2.0
Deploy targets19 target profiles0 listed target profiles
Free tierPreview access by requestyes — self-host free

When to pick Chroma

The fastest path from LangChain prototype to working vector search. Great DX; not designed for multi-tenant production or non-Python stacks.

  • You need HNSW at scale past ~100k vectors — kynetradb uses brute-force today.
  • Your team is already invested in Chroma'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 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
  }'
Chroma
# Chroma — Python client
import chromadb
client = chromadb.Client()
collection = client.get_or_create_collection("products")

# Upsert
collection.upsert(
    ids=["aurora-espresso"],
    embeddings=[[0.1, 0.2, 0.3, 0.4]],
    metadatas=[{"title": "Aurora Espresso"}],
)

# Query
results = collection.query(
    query_embeddings=[[0.1, 0.2, 0.3, 0.4]],
    n_results=10,
)
See all 20 comparisonsChroma homepage ↗