The vector database that
evolves itself.
Stop tuning hyperparameters. EmergentDB discovers the fastest search strategy for your data automatically — delivering up to 28× faster search with 100% recall. Sub-millisecond. Multi-region.
The problem
Every other vector database makes you guess.
You picked an embedding model. Now you have to pick an index type, tune the parameters, and pray it scales. Sound familiar?
Manual Tuning Hell
HNSW M=16? M=32? ef_construction=100? Most teams guess, deploy, and hope for the best.
Workload Mismatch
Optimal config for 1K vectors ≠ optimal for 100K. Your data changes. Your database doesn't adapt.
Recall vs Speed
Fast search often means lower recall. You shouldn't have to choose between accuracy and performance.
The solution
EmergentDB doesn't tune. It evolves.
EmergentDB discovers and optimizes its own search algorithms. No manual tuning. No configuration. Just faster results.
Insert
Batch insert your vectors via REST API or TypeScript SDK. Embeddings from any source — documents, images, graph structures, semantic relationships.
Evolve
EmergentDB automatically discovers the optimal search strategy for your data. It adapts to your workload — no hyperparameter tuning, no guesswork.
Search
Query at sub-millisecond latency. EmergentDB maintains 100% recall — it won't trade accuracy for speed. Tested at 10M vectors across 768, 1536, and 3072 dimensions.
Discovered by EmergentDB
These algorithms don't exist in any textbook or any other database. EmergentDB discovered them.
| Algorithm | Architecture | Inserts/s | Search | Recall |
|---|---|---|---|---|
| Progressive Graph | Flat + HNSW | 12M | 5ms | 100% |
| Tiered IVF | Flat + IVF | 15M | 4ms | 100% |
| Clustered Ensemble | Multi-IVF | 8M | 8ms | 99.9% |
| Lazy Graph | Flat + On-Demand HNSW | 14M | 3ms | 100% |
Get started
Start building in seconds.
One API. Multi-tenant from day one.
# Insert vectors$ curl -X POST api.emergentdb.com/my-tenant/vectors/insert \ -d '{"vectors": [[0.1, 0.2, ...]]}'{"ids": [0], "count": 1}# Search — sub-millisecond$ curl -X POST api.emergentdb.com/my-tenant/vectors/search \ -d '{"vector": [0.1, 0.2, ...], "k": 10}'{"ids": [42, 17, 99], "distances": [0.05, 0.12, 0.15], "latency_ms": 0.11}# Delete a vector$ curl -X DELETE api.emergentdb.com/my-tenant/vectors/42{"ok": true}Performance
Measured, not marketed.
Same embeddings. Same document. Same queries. No network tricks. LLM-as-judge quality scoring. Apples-to-apples.
| Configuration | Latency | Quality | Speedup |
|---|---|---|---|
| EmergentDB (Evolved) | 43.6µs | 8.2/10 | 8.4× |
| EmergentDB (HNSW) | 51.4µs | 9.0/10 | 6.9× |
| EmergentDB (High-Recall) | 88.6µs | 9.6/10 | 4.1× |
| ChromaDB (Default) | 368.3µs | 9.0/10 | 1.0× |
| ChromaDB (Tuned) | 366.1µs | 9.4/10 | 1.0× |
than ChromaDB in batched mode — while scanning 10× more vectors. Also 2.5× faster than FAISS across every dimension from 768D to 3072D.
peak scan rate in batched mode. EmergentDB processes nearly a billion vectors per second on a single machine — no cluster required.
| Dimension | Model | EmergentDB | FAISS | ChromaDB |
|---|---|---|---|---|
| 768D | Gemini / Cohere | 0.11ms | 0.23ms | 1.97ms |
| 1536D | OpenAI ada-002 | 0.15ms | 0.38ms | 3.89ms |
| 3072D | OpenAI large | 0.28ms | 0.66ms | 7.70ms |
Tested from 100K to 10M vectors. All benchmarks use real Gemini embeddings, not random vectors — random vectors produce misleading results. Full methodology →
The difference
Why teams switch to EmergentDB
Faster, cheaper, and fully open source. No trade-offs.
Flat pricing. No per-query fees. No per-write fees. No minimums on top of usage.
Sub-millisecond search across every dimension. 885M vectors scanned per second on a single node.
Run EmergentDB on your own infrastructure for free. Read every line of code. Fork it. Modify it. Your data, your rules.
View on GitHubPricing
Flat pricing. No per-query fees. No surprises.
Other vector databases charge per query, per write, per GB — then add a minimum on top. EmergentDB is simple: one price, unlimited usage.
Community
Self-hosted, open source
- Unlimited vectors
- Unlimited queries & writes
- Full Bolt engine
- AGPL-3.0 · No vendor lock-in
- Community support
Launch
Managed cloud
$24/mo billed annually
- 500K vectors
- Unlimited queries & writes
- No per-query pricing
- SOC 2 · GDPR compliant
- Email support · 99.9% SLA
Scale
Dedicated resources
$66/mo billed annually
- 2.5M vectors
- Unlimited queries & writes
- Dedicated instance
- SOC 2 · GDPR · HIPAA-ready
- Priority support · 99.95% SLA
Production ready
Ship to production, not back to the drawing board.
Multi-tenant isolation, crash recovery, and data residency built in from day one — so you can focus on your product.
Three Endpoints. That's It.
Insert, search, delete. A clean REST API with a TypeScript SDK. No index configuration, no schema migrations, no cluster management. Ship your first query in under a minute.
Crash-Safe Persistence
Write-ahead log protects every mutation. Zero-copy persistence means a 10GB index reopens in microseconds after restart. Your data stays durable and available.
Your Data, Your Infrastructure
Self-host for free under AGPL-3.0 — or let us manage it. Either way, your data never leaves the region you choose. No vendor lock-in. No per-query pricing surprises.
Multi-Tenant Isolation
Every tenant gets their own namespace, API key, and region. Data is isolated at every layer — from API gateway to vector storage. Built for SaaS from the ground up.
Deploy where your users are
Your vectors deserve better.
Self-host for free or deploy on managed cloud for $29/mo. No per-query fees. No per-write fees. Just results.