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.
High-Throughput Reads & Writes
Ingest over 72,000 vectors/sec with instant availability while sustaining up to 6,993 filtered QPS and sub-millisecond query latency under concurrent production load.
Rich Metadata Pre-Filtering
Filter on arbitrary JSON attributes using expressive operators ($eq, $ne, $gte, $lte, $in, $contains), date ranges, and category bitmaps with zero recall penalty.
In-Place Metadata Patching
Update document permissions, pricing, tags, or categories in place via PATCH without re-computing or re-uploading expensive high-dimensional embeddings.
Flexible Dimension Support
Native support for any embedding model dimension—from compact 128-d and 384-d models up to 1536-d and 3072-d frontier embeddings.
Serverless Cloud or BYOC
Start instantly on MassEngine Serverless, or deploy our zero-dependency static binary directly inside your own AWS, GCP, or bare-metal VPC with automated self-healing.
Clean REST & SDK Interface
Simple, predictable JSON APIs and typed SDKs for Go, Python, and TypeScript.
POST https://api.massengine.com/v1/search
Authorization: Bearer mk_live_9f8a7b6c...
Content-Type: application/json
{
"vector": [0.021, -0.044, 0.089, "... 1024 dims"],
"k": 10,
"nprobe": 16,
"rerank_pool": 64,
"min_score": 0.75,
"metadata_filter": {
"department": { "$in": ["security", "legal"] },
"clearance_level": { "$gte": 2 },
"tags": { "$contains": "approved" }
}
}
Same 20M Records. 3 Live Hardware Setups.
See how MassEngine serves the exact same 20,000,000 English Wikipedia passages (1024-d embeddings, 83.46 GiB logical corpus across 8 shards) across three distinct live hardware profiles with 100% Top-K recall parity.
2-Node High-End In-Memory
All 8 shards (RF=2) pinned in high-bandwidth RAM for ultra-low tail latency under heavy concurrent load.
2-Node High-End NVMe
Identical 20M-record dataset served on a strict memory cap with zero-copy mmap NVMe segment tiering.
4-Node Low-End NVMe
20,000,000 vectors distributed across 4 lightweight nodes (1 vCPU / 6.8 GiB RAM each) backed by NVMe storage.
Self-Service Vector Cloud or In-VPC Enterprise BYOC
Every cloud workspace is backed by dedicated Stage-1 index capacity and hard vector/storage guardrails—so you get predictable sub-5ms performance with zero surprise overages. Need to test first? Start with a $5 Developer Sandbox in the Cloud Console.
Starter — $29 / mo
Instant self-service provisioning via Square. Ideal for production RAG apps, semantic search, and multi-partition workloads up to 250K vectors.
RF=2 HA)
1.1 GiB Included
Growth Pro — $149 / mo
Pinned Stage-1 DRAM index blocks with NVMe Tier-2 reranking for multi-million vector collections and high-concurrency production traffic.
RF=2 HA)
11 GiB Included
Enterprise BYOC
Deploy MassEngine's zero-dependency static binary directly inside your own AWS, GCP, or bare-metal VPC so sensitive vectors never leave your environment.
Contact Enterprise Engineering
Planning an In-VPC deployment or 100M+ vector cluster? Reach out below—or create a self-service Cloud Console account to start immediately.