MonkDB - Transform Your Data Infrastructure
The AI-Native Unified Database
MonkDB is an AI-native unified database that supports Vector, Time-Series, Geo-Spatial, Blob Store, Document, Full-Text Search, and Streaming SQL — all in a single, high-performance engine. Built for enterprises to simplify infrastructure, break down data silos, and build intelligent applications faster.
Trusted Features & Capabilities
Experience the power of a unified database platform built for modern enterprises
Unified Data Engine
One platform for all data types
AI-Native by Design
Built-in AI capabilities
High Performance
1.5M+ QPS with low latency
Enterprise Security
Zero-trust architecture
Developer First
Intuitive SDKs & APIs
Multi-Modal Support
Vector, Time-Series, Geo & more
Lightning Fast
Sub-millisecond latency
Data Privacy
Complete data sovereignty
Scalable Architecture
Horizontal scaling built-in
Seamless Integration
Connect with existing tools
Revolutionizing Data
MonkDB is your AI-native solution for seamless data integration and actionable insights.
Built for ModernData Solutions
MonkDB is designed to unify fragmented data into a single platform, enabling smarter decisions and real-time insights for modern applications.
AI-Driven Insights
Built-in capabilities for advanced analytics and machine learning.
Replace Multiple Systems
One Platform to ReplaceLegacy Systems
MonkDB unifies vector, time-series, document, geospatial, blob storage, and streaming capabilities into a single powerful engine, eliminating the need for multiple specialized databases.
Optimized Architecturefor Seamless Data Flow
Efficient architecture for superior data management
Apps
IoT & Edge
Streams
PG Wire/SDK
MonkDB
Vector
Streaming SQL
Timeseries
Document/JSON
Geospatial
Full Text Search
Blob Store
Analytics
Search
AI Apps
& more
Apps
IoT & Edge
Streams
PG Wire
MonkDB
Vector
Streaming SQL
Timeseries
Document/JSON
Geospatial
Full Text Search
Blob Store
Analytics
Search
AI Apps
& more
Empowering Data Retrieval
MONKDB enhances data management with AI, enabling organizations to unify their data for smarter, real-time insights.
AI-Native by Design
Built ground-up for AI workloads with native vector processing, embedding models, and hybrid search capabilities integrated at the core.
- Native vector support
- Real-time inference
Unified Multi-Modal Engine
One powerful engine supporting vector, time-series, geospatial, document, blob, and streaming data types — all through a single query layer.
- 7+ data modalities
- Single query interface
- Zero integration overhead
Enterprise-Grade Performance
Achieves 1.5M+ QPS with distributed architecture optimized for low-latency analytics and real-time processing at scale.
- Sub-2-4 ms latency
- Horizontal scaling
- High availability
Security & Compliance
Built-in enterprise security with on-premises deployment, VPN-secured control plane, and comprehensive access controls.
- Zero-trust architecture
- Data sovereignty
- Audit compliance
Developer First
Modern SDKs, intuitive APIs, and comprehensive documentation to help teams build intelligent applications faster.
- Python & TypeScript SDKs
- REST & SQL APIs
- Rich documentation
Flexible Deployment
Deploy anywhere with our EKS-based control plane — works seamlessly across AWS, Azure, GCP, and on-premises environments.
- Multi-cloud ready
- Kubernetes native
- Easy orchestration
📊
🔄
💰
The Competition Matrix
| Feature | 🚀MonkDB | 💾SingleStore | ❄️Snowflake | 🏠Clickhouse | 🌟Aerospike | 💻SAP HANA |
|---|---|---|---|---|---|---|
| Deployment | Cloud, On-Prem, Edge | Cloud, On-Prem | Cloud only | Cloud, On-Prem | Cloud, On-Prem | Cloud, On-Prem |
| Processor | Supports ARM, x86_64 | Optimised for x86_64 | Optimised for x86_64 | Supports x86_64, ARM partial | Supports ARM, x86_64 | Requires in-mem arch |
| Multi-Model | V, TS, GIS, FTS, DOC, SQL, BLOB | V, TS, FTS, DOC, SQL | V, TS, GIS, FTS, DOC, SQL | V, TS, GIS, FTS, DOC, SQL | V, GIS, FTS, DOC, KV, G | V, TS, GIS, FTS, DOC, SQL, BLOB |
| Hybrid Search | Available | Available | Available | Not Available | Available | Not Available |
| HTAP | Only OLAP | Supports | Supports | Only OLAP | Supports | Supports |
| Licensing | Flexible EULAs | Flexible EULAs | Consumption Based | Open Core + Enterprise | Open Source + Enterprise | Extremely Expensive |
*Multi-model: Vector (V), Timeseries (TS), Geospatial (GIS), Full Text Search (FTS), Document JSON (DOC), Streaming SQL (SQL), Blob (BLOB), Key-Value (KV), Graph (G)
Why MonkDB
Feature comparison across different workload types
| Data Workload | MonkDB | Competition |
|---|---|---|
📊Timeseries Store | Distributed SQL and real-time time series ingestion with built-in TS functions. Seamlessly combine geospatial and time series data. Ideal for unified workloads with complete context. | InfluxDB 2.0 offers speed and scale, but its non-SQL interface adds complexity and limits support for non-numeric data. TimescaleDB, while purpose-built, enforces strict schemas and lacks native distribution, making it unsuitable for some production-grade scenarios. |
🔍Vector Store | Supports hybrid queries combining vector and full-text search, with KNN and similarity search over a SQL interface. However, limited to HNSW indexing, restricting flexibility for varied vector workloads. | Qdrant lacks SQL support and isn't multi-model. Milvus is operationally heavy, relying on components like etcd and Pulsar. Weaviate imposes opinionated data models, with inconsistent query performance. |
🌍Geospatial Store | Supports all GeoJSON shapes with distributed SQL and real-time ingestion. Enables seamless Geo + Time Series workloads. However, lacks native spatial visualization. | PostGIS is limited by single-node architecture and struggles with large-scale ingestion. Oracle is costly and enforces vendor lock-in. ESRI's ArcGIS uses proprietary formats and expensive licenses, with restricted SQL capabilities. |
📄Document/JSON Store | Manage JSON as SQL with built-in distributed querying. Delivers high ingest and query performance, with support for complex aggregations and joins. | Joins in MonkDB are cumbersome, often forcing complex aggregations and manual sharding. CouchDB struggles with query and ingestion performance at scale. |
🔎Full Text Search Store | Joins, filters, and scoring in a single query. Lighter than ELK, with SQL-native full-text and structured search. A unified engine for both search and analytics. | Elasticsearch's DSL is complex for non-developers and the system is heavy to manage. It's not SQL-first. OpenSearch lags in advanced NLP/ML features and retains the same DSL complexity. |
💾Blob Store Store | Natively integrated with MonkDB for low-latency record access. Simple REST-based blob API. While it lacks features like versioning, tiering, and ACLs, workarounds exist. Built on MonkDB's internal shard replication. | S3 has higher latency than DB-native access and lacks integrated DB semantics. Azure Blob Storage faces similar issues despite good performance, and adds a complex permissions model. The costs increase rapidly for ever growing data. |
Return on Investment with MonkDB
Significant Cost
Advantage
Enterprise-Grade
Performance
Operational
Excellence
AI & Real-Time
Analytics Ready
Before MonkDB
Licensing
Multiple Vendors
Data Management
Different protocols
increasing lead time
DevOps
Complex tuning &
infrastructure requirements
Data Movement
Complex & fragile
transformation pipelines
AI Integration
Fragmented approach
with limited capabilities
After MonkDB
Licensing
Single license, ~60% reduced overhead
Data Management
Unified pgWire SQL protocol
for faster deployment
DevOps
Auto-sharding, commodity hardware,
~80% cost savings
Data Movement
SQL-based transformations
eliminate ETL overhead
AI Integration
Unified vector & text search
with leading model integrations
Up to 60% TCO Reduction
Consolidate your database infrastructure with MonkDB
Why Choose MonkDB?
MonkDB is an AI-Native Unified Database that brings together diverse data models into one platform—supporting Vector, Time-Series, Geospatial, Blob, Document, Full-Text Search, and Streaming SQL with built-in AI capabilities.
1.5M+
Queries Per Second
<2-4 ms
Query Latency
3-5x
Infrastructure Reduction
The AI-Native Unified Database
MonkDB integrates various data models into one platform with AI capabilities.
AI-Native by Design
Purpose-built for AI workloads with integrated vector capabilities and LLM optimization
Unified Data Platform
Single engine for multiple data models including vector, time-series, and document storage
High Performance
1.5M+ QPS with optimized latency and horizontal scalability for enterprise workloads
Enterprise Security
Secure deployment with zero-trust architecture and end-to-end encryption
Infrastructure Optimization
Consolidate multiple databases into one solution, reducing complexity and costs
Rapid Development
Modern APIs and SDKs for all major programming languages with intuitive query interfaces
Let's Build the Future of Data Infrastructure—Together
Join us in transforming how organizations manage and activate their data. Whether you're looking to integrate MonkDB, build domain-specific solutions, or innovate together, we're ready to partner with you.
MonkDB is a unified, AI-native database that combines multiple data models (Vector, Time-Series, Geospatial, etc.) into one platform with built-in AI capabilities, simplifying architecture and reducing overhead.