8 Best Open Source MongoDB Alternatives
A curated collection of the 8 best open source and self-hosted alternatives to MongoDB in 2026.
Looking for a free, privacy-friendly, or self-hosted replacement for MongoDB in 2026? The top open-source alternative is TiDB — Fully managed auto-scaling SQL database.
Other highly rated open-source MongoDB alternatives include Turso, Convex and Instant.
Fully managed auto-scaling SQL database
TiDB Cloud is a fully managed, auto-scaling SQL database designed for modern applications, including AI workloads. Built on open-source TiDB, it offers seamless scalability, high availability, and compatibility with MySQL. - Auto-scaling: Dynamically adjusts resources to handle fluctuating workloads without manual intervention. - MySQL Compatibility: Fully compatible with MySQL, enabling easy migration and integration. - High Availability: Multi-region deployments ensure uptime and data durability. - AI-Optimized: Purpose-built for AI applications, including vector search and persistent context for agents. - Generous Free Tier: Start for free with scalable resources and pay-as-you-go pricing. - Global Reach: Deploy in multiple regions for low-latency access worldwide. - Security & Compliance: Enterprise-grade security with GDPR and SOC 2 compliance. TiDB Cloud is a fully managed service, but the open-source TiDB database can be self-hosted for complete control. Self-hosted deployments support Kubernetes, bare metal, and cloud environments. For AI workloads, TiDB provides native vector indexing and retrieval-augmented generation (RAG) pipelines.
Lightweight SQLite database for AI agents
Turso is a lightweight, cloud-native database built on SQLite, optimized for the era of AI agents. It enables developers to deploy databases everywhere—on servers, in browsers, or on devices—just like files, with no cold starts or wake-up penalties. - Millions of Databases: Spin up unlimited databases for every user, agent, or tenant at minimal cost. - Vector Search: Native similarity search for AI apps and RAG workflows, no extensions required. - Concurrent Writes: Multiple writers with zero conflicts, eliminating locking issues. - Browser & Persistence: Runs in the browser with WebAssembly and OPFS for offline-first applications. - SQLite Compatible: Fully backwards compatible with SQLite, extending its capabilities for modern workloads. - Always On: Databases are files, not processes, ensuring no cold starts or scale-to-zero penalties. - Branching: Create isolated Copy-on-Write branches for rapid experimentation and rollbacks. - Replication & Sync: Keep devices in sync with each other on demand via Turso Cloud. - Analytics: Monitor performance and usage across databases in real-time. Turso is built on libSQL, an open-source fork of SQLite, and is available as: - Turso Cloud: Fully managed cloud service with unlimited databases. - Embedded Engine: Deploy Turso locally or on-device for offline-first applications. - Self-Hosting: The open-source libSQL engine can be self-hosted for full control over your infrastructure. Turso is designed to scale effortlessly, making it ideal for AI-driven applications, multi-tenant systems, and edge computing scenarios.
Reactive backend platform for agents and apps
Convex is a reactive backend platform designed to keep pace with developers and their AI agents. It combines a realtime database, functions, workflows, sync, search, and file storage into a single TypeScript-based solution with zero glue code. - Realtime Database: Keep your app up to date with built-in realtime capabilities. - Authentication: Over 80+ OAuth integrations for seamless user management. - Components: Independent, modular TypeScript building blocks for your backend. - AI Coding: Generate high-quality Convex code with AI tools and plugins. - Open Source: Self-host and develop locally with full control. - Templates: Quick-start recipes for common use cases. - Search: Cross-document and community search for efficient development. - File Storage: Integrated file storage solutions. Convex is open source and can be self-hosted, allowing full control over your infrastructure. It supports TypeScript natively and integrates with popular frameworks and tools. The platform is designed for scalability and performance, making it suitable for both small projects and enterprise applications.
Instant is a database solution for building real-time and offline-enabled applications, simplifying the development of collaborative products.
Instant is a database solution for building real-time and offline-enabled applications, simplifying the development of collaborative products.
Unified distributed database for transactions, analytics, and AI
OceanBase is a unified distributed database designed to handle transactional, analytical, and AI workloads with ultra-fast performance. It maintains world records in both TPC-C (OLTP) and TPC-H (analytics) benchmarks, demonstrating its exceptional scalability and efficiency. - Unified Database: Supports OLTP, real-time analytics, and AI workloads in a single system - World-Record Performance: Holds TPC-C and TPC-H benchmark records for speed and scalability - Global Deployment: Enables multi-cloud and global application deployment with active geo-redundancy - Elastic Scaling: Automatically scales to handle peak traffic demands without downtime - Mission-Critical Reliability: Powers critical systems for 400+ enterprises including Alipay - Open Source Community Edition: Free, open-source distributed database available - AI Lakebase: Specialized version for operational data, real-time analytics, and AI applications - Database Consolidation: Supports multi-tenant architectures for efficient resource utilization OceanBase offers multiple deployment options: - OceanBase Cloud: Fully managed cloud service for easy deployment and scaling - OceanBase Enterprise: Self-hosted version for on-premises or private cloud deployment - Community Edition: Open-source version available on GitHub for self-hosting - OceanBase seekdb: Open-source AI-native search database The database is compatible with standard SQL interfaces and provides connectors for various programming languages and frameworks. Comprehensive documentation and training resources are available for all deployment options.
Continual Learning Infrastructure for AI Agents
Activeloop provides the infrastructure for continual learning, enabling AI agents to improve with each execution. Their GPU database, Deeplake, unifies vector and tensor data for efficient agent operations. - Deeplake Database: GPU-native database combining vector and tensor storage for AI agents - Hivemind Memory: Agent traces become shared organizational skills for cross-team reuse - Refinery Software Factory: Continuous learning pipeline that turns feedback into production improvements - GPU Streaming: Enables real-time fine-tuning and optimization - Serverless Postgres Interface: Familiar SQL interface for vector and tensor data - Trajectory Capture: Records agent execution paths for analysis and improvement - Skill Distribution: Shares learned skills across teams and projects - Benchmarking & Verification: Ensures only validated improvements move forward Activeloop offers both cloud-hosted and self-hosted solutions. The open-source Deeplake database can be deployed on-premises or in cloud environments. The company provides enterprise-grade support and professional services for production deployments.
High-performance in-memory graph database
Memgraph is a high-performance, in-memory graph database designed for real-time analytics and AI workloads. It enables organizations to process and query connected data at scale with millisecond latency, making it ideal for fraud detection, knowledge graphs, logistics optimization, and AI agent memory. - Real-time graph processing: In-memory architecture for sub-millisecond query responses - Cypher compatibility: Full support for Neo4j's Cypher query language - Vector search integration: Native support for semantic search and AI workloads - GraphRAG capabilities: Structured context for AI applications - High availability & security: Enterprise-grade features including encryption and role-based access control - ZeroETL: Direct query capabilities without traditional ETL pipelines - Multi-model support: Handles both property graphs and vector embeddings - Developer tools: Memgraph Lab (visual interface), Python drivers, and NetworkX compatibility Memgraph offers both cloud and self-hosted deployment options. The open-source core is available on GitHub, with enterprise features requiring a license. Supports Docker, Kubernetes, and bare-metal installations. Compatible with major cloud providers and offers managed services for production deployments.
Embedded key-value database on object storage
SlateDB is an open-source embedded key-value database built on an LSM tree architecture that relies solely on object storage for durability. Designed for modern cloud-native applications, it eliminates the need for local disks while maintaining 99.999999999% durability and transactional semantics. - Zero-disk architecture: Persist data directly to object storage (S3, R2, GCS) without local disks or cross-AZ traffic, reducing costs and complexity. - Flexible deployment: Single-writer multi-reader model with formally verified manifest fencing for consistency, supporting embedded or separate compaction processes. - Transactional semantics: Full ACID transactions, snapshots, and optimistic concurrency control for demanding OLTP workloads. - Tunable performance: Optimize write durability latency vs. API request costs and leverage hybrid memory/disk caching for reads. - Clones & branches: Zero-copy checkpointing enables efficient cloning, forking, and rescaling operations with O(1) splits and merges. - Multi-language support: Rust core with Go, Java, Node.js, and Python bindings for broad compatibility. - Cloud-agnostic: Runs on AWS S3, Cloudflare R2, Google Cloud Storage, and most S3-compatible object stores. - Apache 2.0 license: Free and open-source with permissive licensing. SlateDB is designed for self-hosting and cloud-native environments. It requires no local storage infrastructure and can be deployed as an embedded library or with separate compaction processes. The system is optimized for object storage performance and cost efficiency, making it ideal for scalable, distributed applications. Configuration is handled through standard object storage settings, and the project provides comprehensive documentation for quick setup and tuning.
About MongoDB
MongoDB is a flexible, AI-ready database platform that enables developers to build and scale applications faster. Combining operational data, vectors, and streaming data in a unified platform, MongoDB supports modern workloads including AI-driven applications.
Key Features of MongoDB
- Atlas Database: Deploy a multi-cloud database with built-in scalability and performance.
- Vector Search: Design intelligent apps with generative AI using vector search capabilities.
- Stream Processing: Integrate MongoDB with Kafka for real-time data processing.
- AI-Ready Platform: Combine operational, vector, and streaming data for AI applications.
- Self-Managed Options: Run MongoDB yourself with Enterprise Advanced or Community Edition.
- Developer Tools: Use Compass for GUI-based data management and Relational Migrator for seamless migrations.
- Global Scalability: Deploy across multiple clouds with enterprise-grade reliability.
- AI Partner Ecosystem: Access state-of-the-art embedding models and AI tools through partnerships.
Tech / Self-Hosting
MongoDB offers both cloud-based (Atlas) and self-hosted (Enterprise Advanced, Community Edition) deployment options. The platform is designed for high availability, scalability, and security, with support for multi-cloud and hybrid deployments. MongoDB is GDPR compliant and provides enterprise-grade data governance features.