Best Serverless Databases in 2026

Popular open-source relational database

4.5 /5 across SourceForge and G2 · 1,688 ratings, Sep 2026

MySQL is the relational database that powered the web. Open source, reliable, and understood-the database behind countless applications. The ecosystem is vast. The knowledge is widespread. The reliability is proven. Web applications often use MySQL because it works and everyone knows it.

Managed in-memory database for real-time data across clouds

4.5 /5 on G2 · 136 ratings, Sep 2026

Redis Cloud is a fully managed in-memory database service offering real-time data capabilities across AWS, Azure, and GCP with enterprise-grade features including active-active replication and auto-tiering.

Distributed NoSQL cloud database

4.3 /5 on G2 · 150 ratings, Sep 2026

Couchbase combines document database flexibility with SQL queries and in-memory caching. Scale horizontally for massive workloads while keeping the developer experience accessible. Mobile sync keeps apps working offline. The query language is SQL-like. Geographic distribution brings data close to users. Applications needing flexible schema, offline capability, and horizontal scale choose Couchbase for NoSQL with enterprise features.

Document database with sync and offline support

3.9 /5 on G2 · 64 ratings, Sep 2026

CouchDB stores JSON documents with built-in synchronization and conflict resolution. Deploy it anywhere, work offline, and trust that data will sync when connectivity returns. The replication protocol is elegant and proven. REST API makes access straightforward. The focus on reliability over speed serves specific use cases well. Applications with offline requirements or multi-master replication needs choose CouchDB for a database designed around sync.

Fully managed graph database service on AWS

4.4 /5 on G2 · 30 ratings, Sep 2026

Amazon Neptune is a graph database for connected data. When relationships between entities matter as much as the entities themselves-social networks, fraud detection, recommendations-Neptune handles queries that would crush relational databases. Both property graph and RDF models are supported, so you can choose the approach that fits your data. Fully managed on AWS means no database administration headaches. Organizations modeling complex relationships find Neptune makes queries natural that would require painful SQL joins. The graph model matches how you actually think about connected data.

Official MCP server to manage serverless Postgres with AI

Neon is serverless Postgres with database branching, where a branch is a copy on write clone of the data that spins up in seconds and can be discarded just as fast. Its MCP server exposes both that control plane and the SQL surface, which makes it one of the few database integrations where an agent can experiment without putting the main database at risk. An agent connected to it can list and create projects, create, list and delete branches, fetch connection strings, describe database and table schemas, and execute SQL against a chosen branch. It also supports a migration pattern that fits agents specifically: the change is applied to a temporary branch first, verified there, then committed to the main branch, so a migration that breaks something never lands on production data in the first place. This is built for developers working on a Neon project with an assistant in the editor. The assistant reads the real schema instead of guessing from model files, tries a query against a branch, and proposes a migration for approval before anything is committed. The usual caution for SQL access applies: the connection carries whatever permissions its role has, so use a limited role and keep destructive statements behind human review.

Serverless vector and full-text search engine built on object storage for AI applications.

Turbopuffer is a serverless vector and full-text search database designed for high-performance, cost-effective, and scalable search capabilities. It is built from first principles on object storage, allowing it to handle massive datasets and high query loads efficiently. The platform is engineered to separate compute and storage, leveraging NVMe and object storage for optimal cost and performance. This product is ideal for AI applications, semantic search, recommendation systems, and any use case requiring high-performance similarity search. It serves companies that need to connect AI with large amounts of fresh data without incurring the high costs and operational complexity of traditional vector databases. Turbopuffer is trusted by leading companies for production workloads, handling trillions of documents and millions of writes per second. Key benefits include significant cost savings (up to 10x cheaper than alternatives), automatic scaling to support billions of vectors, and low-latency query responses. Its architecture ensures reliability and performance, making it suitable for critical production systems.