Google Cloud Next 2026 introduced substantial upgrades across its database portfolio, shifting the focus toward Agentic AI enablement, hybrid cloud flexibility, and automated AI operations. Below is the summary of announcements by service with the link to more info.
A direct link to the official Google Cloud announcement blogs.
What's New for Google Cloud Databases at Next '26

🚀 Database Center & AI Observability

Before diving into individual services, Google introduced a core platform-wide capability that affects most of these databases:

• Gemini-powered Database Observability Agent: A new proactive AI agent that continuously monitors performance and health across AlloyDB, Bigtable, Cloud SQL, and Spanner. It identifies the root causes of issues and delivers automated remediation playbooks to reduce operational downtime.

• 🔗 Database Center Improvements

🧠 Memorystore

• Move-to-Managed Migration Paths: Google introduced a streamlined, managed migration tool specifically designed to help users modernize self-managed Redis and Valkey instances by transitioning them smoothly into Memorystore.

• New Node Sizes & Core Modules: To provide better price-performance flexibility, Memorystore added smaller and larger node sizes alongside native support for the popular Valkey-bloom and Valkey-json modules.

• Enterprise-Grade Security: The platform added robust access security features including custom Access Control Lists (ACLs), token-based authentication, and Flexible Certificate Authorities.

• 🔗Memorystore for Valkey 9.0 GA Announcement

🛢️ Cloud SQL

• Move-to-Managed Experience (Preview): Embedded directly into the Cloud SQL console and Database Center, this update upgrades the Database Migration Service. For PostgreSQL, it introduces advanced capabilities like parallel consolidation of multiple source databases into a single live target, and support for migrating tables that lack primary keys.

• Security & Observability Integrations: Cloud SQL received native brute-force connection protection and air-gapped recovery options, alongside integration into the new fleet-wide Gemini slow query analysis tools.

• 🔗Cloud SQL Release Notes

⚡ AlloyDB

• AlloyDB AI-Powered Search at Scale (Preview): AlloyDB can now scale to handle up to 10 billion vectors using Google’s custom ScaNN index, delivering vector queries up to 6 times faster than standard PostgreSQL HNSW indexes. Native BM25 support is also coming soon to empower high-speed hybrid search engines.

• New and Optimized AI Functions (Preview): Google added ai.analyze_sentiment and ai.summarize into AlloyDB's built-in suite of LLM functions. Additionally, the existing ai.if fraud-detection function received a highly optimized mode to boost performance and slash processing costs.

• 🔗AlloyDB Release Notes

🌐 Spanner

• Spanner Omni: This milestone release allows users to run Spanner’s globally consistent, multi-model database environment entirely outside of Google Cloud—including on-premises, across alternative cloud environments, or locally on developer laptops.

• Spanner Columnar Engine (GA): This engine accelerates analytical queries on live operational data, executing data scans up to 200 times faster. It now natively supports Iceberg tables, continuous reverse ETL from BigQuery, accelerated federated queries.

• 🔗 Spanner Release Notes

📊 Bigtable

• Enterprise Plus Edition & Hybrid Storage: Bigtable introduced a brand-new Enterprise Plus edition featuring nodes with a hybrid storage architecture spanning RAM, SSD, and HDD. This allows the database to intelligently keep frequently accessed cache data in-memory while delegating older records to cost-effective archival storage under a single service.

• 🔗Bigtable Release Notes

🔥 Firestore

• Third-Party AI Agent Skills & MCP (GA): Connecting Firestore to external programming agents like Claude Code, Cursor, and Codex is now natively streamlined via the General Availability of Firestore Skills and the Firestore remote Model Context Protocol (MCP) service.

• Natural Language to MongoDB Queries (Preview): Powered by Gemini Code Assist in the Google Cloud console, developers can type natural language questions to automatically generate complex, MongoDB-compatible Firestore queries.

• 🔗 Official Blog: Firestore: Agentic AI, Search, and MongoDB Compatibility