Private Beta • Serverless & BYOC Vector Cloud

Sub-5ms Filtered & Hybrid
Vector Search at Planet Scale.

MassEngine is a next-generation vector and lexical search engine built from the ground up for multi-tenant AI & RAG systems. Eliminate selective-filter latency cliffs while cutting cluster memory footprints by 3×.

Request Private Beta Access Explore 1536-d Benchmarks

23.5×

Higher Filtered QPS

vs. OpenSearch 3.3 HNSW at 5% selectivity

2.31 ms

0.1% Selective Latency

780 QPS across 990k × 1536-d vectors

2.33 GiB

RAM per Replicated Node

vs. 6.8 GiB on standard HNSW engines

0.986

Recall@10 Under Filter

Zero graph-disconnection recall drop

Verified Fleet Benchmarks

No Filter Cliffs. No Memory Bloat.

Evaluated on a 4-node replicated cluster (RF=2, 990,000 × 1536-d OpenAI embeddings, 10 concurrent clients). Select a filter selectivity below to compare throughput and latency:

Dataset: 990k × 1536-d • k=10 • RF=2

                  MassEngine Cloud
                  16.5× faster
                
                  594.1 QPS • 2.60 ms • Recall@10: 0.971 • RAM: 2.33 GiB/node

OpenSearch 3.3 (HNSW)

36.1 QPS • Recall@10: 0.998 • RAM: 6.80 GiB/node

Built for Production AI

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.

Unified Dense + BM25 Hybrid

Combine semantic vector similarity with Okapi BM25 keyword scoring and Reciprocal Rank Fusion in a single sub-5ms query—without running a separate lexical cluster.

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.

Developer Ergonomics

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, "... 1536 dims"],
  "query_text": "SOC2 compliance encryption policy",
  "hybrid_alpha": 0.70,
  "k": 10,
  "min_score": 0.75,
  "metadata_filter": {
    "department": { "$in": ["security", "legal"] },
    "clearance_level": { "$gte": 2 },
    "tags": { "$contains": "approved" }
  }
}

Private Beta

Request Early Access

We are onboarding design partners for MassEngine Serverless and BYOC deployments.