Sub-2ms Pre-Filtered
Vector Search at Planet Scale.
MassEngine is a next-generation kNN vector database built from the ground up for multi-tenant AI & RAG systems. Eliminate selective-filter latency cliffs while cutting cluster memory footprints by 7×.
No Filter Cliffs. No Memory Bloat.
Evaluated on an identical 2-node replicated cluster (RF=2, 8 Shards, 2,000,000 × 1024-d English Wikipedia BAAI/bge-m3 embeddings, 10 concurrent clients). Select a filter selectivity below to compare throughput, latency, and memory:
Everything Modern RAG Needs in One Engine
Replace fragile multi-system pipelines with a single, deterministic search layer.
True Multi-Tenant Isolation
Host thousands of customer workspaces on shared infrastructure with zero noisy-neighbor degradation, scoped RBAC API keys, per-tenant QoS rate limits, and instant GDPR tenant purge.
Hardware SIMD FastScan
Evaluate 32 quantized vectors per SIMD instruction while pruning non-matching 64-byte micro-block headers before distance computation—delivering up to 36.3× higher filtered QPS.
Rich Metadata Pre-Filtering
Filter on arbitrary JSON attributes using expressive operators ($eq, $ne, $gte, $lte, $in, $contains), date ranges, and category bitmaps with zero recall penalty.
In-Place Metadata Patching
Update document permissions, pricing, tags, or categories in place via PATCH without re-computing or re-uploading expensive high-dimensional embeddings.
Flexible Dimension Support
Native support for any embedding model dimension—from compact 128-d and 384-d models up to 1536-d and 3072-d frontier embeddings.
Serverless Cloud or BYOC
Start instantly on MassEngine Serverless, or deploy our zero-dependency static binary directly inside your own AWS, GCP, or bare-metal VPC with automated self-healing.
Clean REST & SDK Interface
Simple, predictable JSON APIs and typed SDKs for Go, Python, and TypeScript.
POST https://api.massengine.com/v1/search
Authorization: Bearer mk_live_9f8a7b6c...
Content-Type: application/json
{
"tenant": "workspace-acme",
"vector": [0.021, -0.044, 0.089, "... 1024 dims"],
"k": 10,
"nprobe": 16,
"rerank_pool": 64,
"min_score": 0.75,
"metadata_filter": {
"department": { "$in": ["security", "legal"] },
"clearance_level": { "$gte": 2 },
"tags": { "$contains": "approved" }
}
}
Query a Live 1,000,000-Vector Cluster
Run real-time kNN vector search and hardware pre-filtered queries against 1,000,000 English Wikipedia passages (1024-d embeddings across 8 shards, RF=2)—with zero data upload or code required.
RF=2) • Corpus: 1,000,000 × 1024-d Vectors
POST /v1/search)
{
"tenant": "demo-cloud",
"vector": [
0.0241,
-0.0188,
0.0412,
-0.0093,
"... (1024 float32 dims)"
],
"k": 8,
"nprobe": 16,
"rerank_pool": 64,
"metadata_filter": {
"category": { "$eq": "science" }
}
}
Request Demo & Early Access
Enter your work email and company to immediately unlock the live 8-shard Wikipedia cluster playground above and reserve your Private Beta credentials.