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
| Dimension | kynetradb | DynamoDB |
|---|---|---|
| Full-text search | BM25 (parallel, 1.07 ms @ 100k) | None |
| Vector search | Brute-force cosine (2.21 ms @ 100k, no HNSW yet) | None |
| Auth | Built-in (bcrypt + JWT, 3 roles) | External |
| File storage | Built-in (local + S3-compatible, SigV4) | None |
| Realtime | SSE (topic + kind filters) | polling |
| KV lookups | Yes (point lookup by ID) | Yes |
| 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) | Limited |
| 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 — 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 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 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
}' DynamoDB
# See DynamoDB documentation at https://aws.amazon.com/dynamodb