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kynetradb

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

vs
DynamoDB

AWS managed KV + document store with single-digit-millisecond latency at any scale.

Feature comparison

DimensionkynetradbDynamoDB
Full-text searchBM25 — reference catalogueNone
Vector searchBrute-force cosine evaluationNone
AuthPreview scopeExternal
Row-Level SecurityPreview scopeNo
File storagePreview scopeNone
RealtimePreview scopepolling
Edge FunctionsPreview scopeNo
TypeScript SDKPreview scopeNo official client SDK
KV lookupsPreview scopeYes
Document filterPreview scopeYes
LLM runtimePreview scopeNo
Outbound DB syncPreview scopeNo
Self-hostPreview scopeLimited
Single binaryPreview scopeNo
LicensePreview terms — request a scoped evaluationSaaS-only
Deploy targets19 target profiles0 listed target profiles
Free tierPreview access by requestyes — 25 GB, 200M requests/mo

When to pick DynamoDB

Unlimited scale for simple KV access patterns with no ops burden. The data modeling discipline required (partition keys, GSIs) is the real cost.

  • You need unlimited horizontal scale for a single access pattern.
  • Your team is already invested in DynamoDB'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
  }'
DynamoDB
# See DynamoDB documentation at https://aws.amazon.com/dynamodb
See all 20 comparisonsDynamoDB homepage ↗