External search with AlloyDB now supports Apache Solr in Preview. You can use the external_search_fdw extension to connect to a Solr cluster and query its data directly from your database.

AlloyDB integration with Knowledge Catalog is now generally available (GA).

This integration provides a unified metadata view to simplify data governance and analysis. It includes near real-time synchronization and expanded metadata details, like primary and foreign keys.

For more information, see Integrate AlloyDB with Knowledge Catalog.

The Database Insights remote Model Context Protocol (MCP) server now supports the following advanced query insights tools for AlloyDB for PostgreSQL:

• get_advanced_aggregated_query_stats
• get_advanced_aggregated_wait_event_stats
• get_advanced_time_series_query_stats
• get_advanced_time_series_wait_event_stats
• get_index_recommendations

For more information, see Database Insights remote MCP server.

Feature

You can publish an AlloyDB for PostgreSQL conversational analytics agent in Gemini Enterprise. This feature is in Preview.

You can now configure a cooldown period to determine when autoscaling occurs for your read pool instances. For more information, see Scale an instance.

ChatGPT users are now able to list and use the AlloyDB toolset provided by the AlloyDB remote MCP server.

AlloyDB now offers extended support for clusters running major PostgreSQL versions that have reached their end-of-life (EOL) as defined by the PostgreSQL community. Extended support provides an additional three years of support after the end of regular support, giving you more time to plan and perform major version upgrades. For more information, see Extended support for AlloyDB for PostgreSQL.

When the initial user or password is unspecified during cluster creation, a locked postgres role with null password is created.

Database Migration Service quick-start migrations (in Preview) are now integrated into AlloyDB to provide a lightweight, continuous migration flow. This feature automates setup for sources with private IPs in a VPC network, including Cloud SQL for PostgreSQL and self-managed databases on Compute Engine.

For more information, see Quick-start migrations overview in the Database Migration Service documentation.

The integration between AlloyDB for PostgreSQL and Knowledge Catalog (in Preview) is now enabled by default for all new AlloyDB clusters.

This integration provides a unified view of your metadata to simplify data governance and analysis. Recent enhancements feature near real-time synchronization, with updates reflecting in Knowledge Catalog within minutes, as well as expanded metadata details that now include Primary Keys and Foreign Keys.

For more information, see Integrate AlloyDB for PostgreSQL with Knowledge Catalog.

Feature

You can now use the AlloyDB columnar engine to act as a read-optimized, in-memory cache for HNSW indexes. This increases the number of queries per second (QPS) that your database can handle for vector search workloads. This feature is available in Preview.

For more information, see Accelerate vector search with the columnar engine.

You can now use the Database Insights remote MCP server to analyze AlloyDB's performance and system metrics. This feature is in GA.

Feature

The AlloyDB remote MCP server—available through the global endpoint—is now generally available (GA) and includes support for the following:

• Read-only execute SQL.
• Tools to help you update your instance, export and import data, create a backup, and restore your cluster.
• Public IP.
• Private Service Connect endpoint automation.

For more information, see Use the AlloyDB remote MCP server.

Issue

ChatGPT users aren't able to list or use the AlloyDB toolset provided by the AlloyDB remote MCP server.

When querying your Elasticsearch data using standard SQL queries and specifying an OFFSET, if the OFFSET gets pushed down, it gets applied twice. For example, if your SQL query contains OFFSET 5, AlloyDB tries to push the OFFSET down. Then, AlloyDB applies the OFFSET again when the results are returned.

Feature

External search with AlloyDB now supports Elasticsearch in Preview.

With this update, you can use the external_search_fdw extension to connect to Elasticsearch and perform hybrid searches within AlloyDB. This integration allows you to combine the capabilities of AlloyDB with Elasticsearch for advanced search scenarios. For more information, see Access Elasticsearch data from AlloyDB.

Announcement

The following AlloyDB AI function capabilities are available in Preview:

• You can now use AI function acceleration and the new AI Function Apply node to run faster queries with AI functions. This feature optimizes the execution of SQL queries that use the ai.if and ai.rank functions in PostgreSQL 17. For more information, see Accelerate performance for queries with AI functions.
• You can now use optimized AI functions to accelerate your AI queries while reducing operational costs. By training a smaller, faster proxy model on a sample of your data, AlloyDB can process most AI queries locally and only fall back to a remote LLM when necessary. For more information, see Accelerate queries using optimized functions.

• You can now use the sentiment analysis and summarization functions. These functions let you process and analyze unstructured data directly in your database:

  • ai.analyze_sentiment: classifies the emotional tone of text as positive, negative, or neutral, helping you analyze real-time customer feedback from thousands of raw, unstructured product reviews.
  • ai.summarize: condenses lengthy text into its essential information. Use this to extract key decisions and action items from sources like meeting transcripts or technical documentation.
  • ai.agg_summarize: an aggregate function that processes multiple rows in a column to generate a single, unified summary for a group. For instance, you can summarize all reviews for a specific seller using a GROUP BY clause.

  For more information, see Evaluate sentiment and Summarize content.

The following vector search improvements are now available in Preview:

• AlloyDB now supports Vector assist. Vector assist is an AlloyDB extension that simplifies the deployment and management of your AlloyDB vector workloads. It helps you set up production-ready vector search capabilities, such as embedding generation, query optimization, and index creation for vector types like HNSW. For more information about vector assist, how it works, and its limitations, see Vector assist overview.
• You can now defer ScaNN index creation on an empty table or a table with insufficient rows until the table has sufficient data. For more information, see Create a ScaNN index.
• The alloydb_scann extension now supports four-level tree indexes, providing support for tables with up to 10 billion vector rows. For more information, see Four-level ScaNN tree indexes.

Feature

Adaptive filtering from inline filtering to pre-filtering is now generally available (GA). With AlloyDB AI, you can use adaptive filtering to optimize filtered vector searches. This feature enables the query optimizer to use cost-based analysis to dynamically choose the most efficient filtering strategy—either inline filtering or pre-filtering—based on real-time data distributions. This improves filtered vector search performance without requiring manual tuning or intervention. Note that the feature adaptive filtering from pre-filtering to inline filtering is still in Preview.

For more information, see Understand adaptive filtering in AlloyDB AI.

Announcement

The alloydb_scann extension is updated to include the following vector search improvements. These features are generally available (GA):

• By default, new ScaNN vector index builds are automatically tuned. Manually-tuned indexes can be converted to automatically-tuned indexes. For more information, see Create a ScaNN index.
• You can now automatically maintain your ScaNN vector indexes. AlloyDB incrementally manages your index such that when your dataset grows, AlloyDB updates centroids and splits large outlier partitions to provide better QPS and search results. For more information, see Maintain indexes automatically.

The AlloyDB for PostgreSQL index advisor is enabled by default. It provides vector search index recommendations for Scalable Nearest Neighbors (ScaNN) indexes. For more information, see Use the index advisor.

As of April 10, 2026, you can create, run, and edit Gemini Cloud Assist investigations only if you have a Premium Support contract. You can use Gemini Cloud Assist investigations to monitor and troubleshoot your AlloyDB for PostgreSQL instance with AI assistance.

If you ran an investigation prior to April 10, 2026, then the results of the investigation continue to be available to you in the Google Cloud console.

The QueryData tool lets you to query the data in your database using conversational language and build data agents. For more information, see QueryData tool overview. This feature is available in (Preview).

The preview release increases the accuracy of SQL generation with value search queries which match values and their context within a database. Value search queries trigger automatically. QueryData also adds support for Parameterized secure views (PSVs) to help secure applications that use natural language queries. For more information, see Secure and control access to application data using parameterized secure views.

The gcloud beta alloydb connect command is now available in Preview. This command provides a simplified way to connect securely to AlloyDB instances by using the AlloyDB Auth Proxy and psql. For more information, see Connect using gcloud CLI.

You can now enable Advanced Query Insights on primary clusters which have secondary clusters configured. Advanced Query Insights is not supported on secondary clusters. If you perform a switchover, you must re-enable Advanced Query Insights on the new primary cluster.

Hot standby enhances the AlloyDB high availability (HA) architecture to improve failover times and to ensure consistent performance after failover. AlloyDB continuously replicates transactions to the standby node to keep caches warm and to ensure that the node is ready to take over quickly during a failover. This feature is generally available (GA) in PostgreSQL 18 and is automatically enabled for all new instances. For more information, see the AlloyDB high availability overview.

AlloyDB now offers conversational analytics, which lets users query their operational data using natural language. This feature is powered by the Conversational Analytics API, which can help you translate complex human dialog into precise database queries to provide actionable insights. This feature is in Preview. For more information, see Conversational analytics for AlloyDB overview.

Database server compatibility with PostgreSQL version 18 is now generally available (GA):

• You can create AlloyDB clusters with PostgreSQL 18 compatibility.
• You can upgrade existing AlloyDB clusters running PostgreSQL major versions 14, 15, 16, or 17 to PostgreSQL major version 18 with one click.
• You can use Database Migration Service to migrate databases to AlloyDB.

Feature

The following AlloyDB AI features are available in Preview:

• You can now use the ai.hybrid_search() function, which fuses results from each search type into a single list using the Reciprocal Rank Fusion (RRF) algorithm. For more information, see Run hybrid vector similarity search.

• AlloyDB supports the rum extension for complex full-text search operations. The rum extension extends standard GIN indexes by storing positional information directly in the index. This enables faster phrase searches and relevance ranking without needing to access the table data. For more information, see Create and manage a RUM index.

Change

When no major version is specified, AlloyDB for PostgreSQL now defaults to PostgreSQL major version 17 for new clusters.

AlloyDB lets you monitor node-level metrics in Google Cloud console and Metrics Explorer to provide detailed troubleshooting guidance for read pools and to identify nodes causing performance regressions. For more information, see System insights metrics reference.

AlloyDB now supports the 2 vCPU C4A machine type (c4a-highmem-2-lssd), which is powered by Google Axion, Google's custom Arm-based processor. This expansion provides a smaller entry point and more flexibility for scaling your production workloads using Axion-based instances. For more information, see Choose an AlloyDB machine type.

Feature

AlloyDB enhanced backups are generally available (GA). You can now select the Enhanced tier during cluster creation, manage your project-level backups with tiered tabs, and delete an enhanced backup. For more information, see Manage enhanced backups.

AlloyDB enhanced backups are generally available (GA). You can now select the Enhanced tier during cluster creation, manage your project-level backups with tiered tabs, and delete an enhanced backup. For more information, see Manage enhanced backups.

The following AlloyDB AI features are now generally available (GA):

• Auto vector embeddings provide a scalable, automated solution for managing the lifecycle of vector embeddings for large-scale datasets, eliminating the need for manual reindexing or custom scripts. This feature keeps embeddings in sync with transactional data and now supports incremental refresh in manual mode, ensuring that embeddings are only generated for new or updated rows. Additionally, you can perform incremental table refreshes or migration up to 130x faster than traditional row-by-row processing using bulk mode, improving efficiency for semantic search and Retrieval Augmented Generation (RAG).

• AI functions integrate LLMs like Gemini to bring 'world knowledge' to your AlloyDB data and incorporate advanced semantic search and ranking capabilities directly into your SQL workflows. This feature includes out-of-the-box functions for filtering (ai.if), semantic ranking (ai.rank), generation (ai.generate), and forecasting (ai.forecast).

• Experience higher performance in AlloyDB AI by utilizing array-based AI functions. You can perform batch processing of natural language prompts directly within your SQL queries, significantly improving efficiency for large-scale AI operations. For more information, see Perform intelligent SQL queries using AI functions.

Gemini Cloud Assist investigation capabilities are now supported in AlloyDB (Preview).

For more information, see Troubleshoot slow queries with AI assistance.

AlloyDB now integrates with Database Center to provide prioritized health monitoring in the Google Cloud console. This integration highlights critical and high-priority risks, offering one-click navigation to recommended fixes and system insights for quick resolution. For more information, see Monitor the health of your AlloyDB clusters and instances.

AlloyDB performance snapshot and reports now support read pool instance nodes, providing deeper observability into read operations and replica-specific performance issues.

Announcement

New best practices are available for securing generative AI agents using Model Context Protocol (MCP) with Google Cloud databases. This guide covers key security measures like least privilege, native database controls, and secure agent design to help you build safer AI applications. For more information, see Best practices for securing agent interactions with Model Context Protocol.

Feature

You can now make AI function calls in bulk rather than row-by-row, which lets you scale your intelligent workflows faster with new support for array-based processing. For more information, see Perform intelligent SQL queries using AI functions.

This feature is in Preview.

Feature

You can now use the AlloyDB remote MCP server. The AlloyDB remote MCP server lets you interact easily with AlloyDB clusters from LLMs, AI applications, and AI-enabled development platforms.

This feature is in Preview.

We are announcing the release of support for the AlloyDB language connectors and Auth Proxy with Auto IAM Authentication and managed connection pooling. This feature and the fix for the issue from below is available starting with maintenance version 20260107.02_05. Clusters with a maintenance window that may not have received this release can use self-service maintenance to perform a maintenance update.

Virtual columns for expressions is a feature of the columnar engine in Preview that significantly improves query performance and reduces CPU consumption. It caches the results of frequently used expressions, which is especially beneficial for analytical workloads on large datasets. This feature is supported for PostgreSQL version 16 and higher.

Database server compatibility with PostgreSQL version 18 is now available for preview (Preview). You can create AlloyDB clusters with PostgreSQL 18 compatibility.