Google Cloud Blueprint: Introducing new AI functions for AlloyDB: ai.classify and ai.extract — How Does It Work in Production?

TL;DR: Google Cloud has fundamentally shifted how enterprise applications process unstructured data by embedding LLM capabilities directly into the transactional database engine. With the release of ai.classify and ai.extract in AlloyDB AI (PostgreSQL 18), architects can now transform raw text—like clinical notes or invoices—into structured JSON using Gemini 2.5 models via standard SQL, eliminating the latency, token overhead, and security risks of traditional application-side API loops.

What Google Cloud Shipped & The Enterprise Problem It Solves

In our architectural evaluation of modern enterprise workloads, transactional databases are increasingly burdened with vast amounts of unstructured text. Clinical notes, customer reviews, support tickets, scanned invoices, and property descriptions sit dormant in relational tables. Historically, extracting business value from this text required engineering teams to build a complex, fragile "application-side loop."

To process this data, an application had to query the database, serialize the payload, call an external LLM endpoint over the network, manage rate limits and retry logic, parse unpredictable string responses, and finally execute an UPDATE statement to write the structured data back to the database. This architectural anti-pattern introduces severe operational friction. It bloats application code, increases query latency due to network hops, drives up model token costs through redundant prompt overhead, and creates significant data compliance risks by moving sensitive Personally Identifiable Information (PII) across multiple network boundaries.

To solve this, Google Cloud shipped ai.classify and ai.extract functions in AlloyDB AI. Available in the google_ml_integration extension (version 1.6.1 or later) on PostgreSQL 18, these native SQL functions bring Gemini's world knowledge directly into the operational database engine. Rather than treating artificial intelligence as a disconnected external service, model intelligence is now an intrinsic database capability.

Crucially, this release includes smart batching for AI functions. When ai.classify or ai.extract are used in SQL WHERE clauses or bulk SELECT statements, the AlloyDB engine automatically groups inputs into optimized micro-batches before sending them to the Gemini Enterprise Agent Platform. This delivers production scalability by accelerating inference and drastically reducing total model token spend.

Real-World Use Cases: Where This Moves the Needle in the Field

When we inspect production topologies, the ability to execute AI functions natively in SQL unlocks immediate value across several high-stakes industries. Here is how this architecture is applied in practice:

1. Healthcare & Life Sciences: Zero-Trust Clinical Data Extraction

• The Everyday Problem: Hospitals store millions of free-text clinical notes containing critical patient data (symptoms, medications) mixed with highly sensitive Protected Health Information (PHI). Exporting this data to external microservices for NLP processing violates strict data residency and compliance boundaries.
• How It Works in Practice: Using ai.extract, database administrators can securely convert free-text clinical notes into structured JSON records containing patient names, symptoms, and prescribed medications entirely within the AlloyDB boundary.
• The Tangible Impact: Patient data never leaves the database's Virtual Private Cloud (VPC) perimeter, satisfying zero-trust governance while making clinical data instantly queryable for downstream analytics.

2. Financial Services: High-Throughput Invoice & Contract Processing

• The Everyday Problem: Financial institutions process thousands of vendor invoices and legal contracts daily. Building custom parsers for every vendor's unique invoice format is an unscalable engineering bottleneck.
• How It Works in Practice: Financial systems can insert raw OCR text from scanned documents directly into AlloyDB. A continuous SQL process uses ai.extract to pull vendor details, invoice dates, and line-item totals into a strict JSON schema, while ai.classify categorizes the contract's liability caps and governing law.
• The Tangible Impact: Eliminates the need for middleware parsing fleets, isolating tail-latency and reducing the cost-per-request under burst traffic conditions.

3. Real Estate & Knowledge Graphs: Automated Entity Generation

• The Everyday Problem: Real estate platforms receive descriptive listing text that lacks structured metadata (e.g., exact bedroom counts, property types, architectural styles), making search filtering inaccurate.
• How It Works in Practice: As new listings are inserted, ai.extract pulls property types and price ranges, while ai.classify tags the sentiment of neighborhood descriptions. This structured output can immediately populate PostgreSQL Property Graph (PGQ) structures.
• The Tangible Impact: Enhances search accuracy and feeds directly into knowledge graphs without requiring a separate asynchronous data pipeline.

Reference Architecture on Google Cloud

To implement in-database AI securely and at scale, the architecture must integrate AlloyDB, Vertex AI, and downstream analytics platforms while enforcing strict network perimeters. The following reference architecture demonstrates a production-grade topology utilizing the latest 2026 Google Cloud services.

flowchart LR
    subgraph "Google Cloud VPC (VPC Service Controls Enforced)"
        ClientApp["Enterprise Application<br/>(Cloud Run / GKE)"]
        
        subgraph "AlloyDB Cluster (PostgreSQL 18)"
            PrimaryDB[("AlloyDB Primary<br/>(google_ml_integration)")]
            ReadPool[("AlloyDB Read Pool<br/>(Autoscaling)")]
            
            PrimaryDB -- "Logical Replication /<br/>HA Sync" --> ReadPool
        end
        
        ClientApp -- "Standard SQL (INSERT/SELECT)" --> PrimaryDB
        
        subgraph "Vertex AI (Gemini Enterprise)"
            GeminiFlash["Gemini 2.5 Flash<br/>(High-Throughput Extraction)"]
            GeminiPro["Gemini 2.5 Pro<br/>(Complex Reasoning)"]
        end
        
        PrimaryDB -- "Smart Batching API Calls<br/>(Private Google Access)" --> GeminiFlash
        PrimaryDB -. "Fallback" .-> GeminiPro
        
        subgraph "Data Analytics Platform"
            BQ[("BigQuery<br/>(Enterprise Data Warehouse)")]
        end
        
        ReadPool -- "Federated Query /<br/>Datastream" --> BQ
    end
    
    style ClientApp fill:#f9f9f9,stroke:#333,stroke-width:1px
    style PrimaryDB fill:#c4e8c1,stroke:#333,stroke-width:2px
    style ReadPool fill:#c4e8c1,stroke:#333,stroke-width:1px
    style GeminiFlash fill:#d4e4ff,stroke:#333,stroke-width:2px
    style GeminiPro fill:#d4e4ff,stroke:#333,stroke-width:1px
    style BQ fill:#fce8b2,stroke:#333,stroke-width:1px

Architectural Component Breakdown

1. AlloyDB Primary Instance (PostgreSQL 18): The core transactional engine. By enabling the google_ml_integration extension, the database gains the ability to invoke Vertex AI models natively. The engine handles the smart batching of rows, optimizing the payload before it hits the Vertex AI endpoint.
2. Vertex AI (Gemini 2.5 Flash & Pro): For high-throughput, structured extraction (ai.extract) and classification (ai.classify), Gemini 2.5 Flash is the recommended model due to its low latency and cost-efficiency. Gemini 2.5 Pro can be targeted for highly complex, reasoning-heavy text analysis.
3. AlloyDB Read Pools & High Availability: Following the principles of Cloud SQL for PostgreSQL High Availability, the architecture utilizes regional replication and autoscaling read pools. Heavy AI extraction queries can be routed to read pools to ensure the primary instance's CPU is preserved for mission-critical INSERT and UPDATE transactions.
4. BigQuery Integration: Once unstructured text is transformed into structured JSON within AlloyDB, it becomes immediately valuable for enterprise analytics. Using BigQuery federated queries or continuous replication, this structured data flows into the BigQuery data warehouse for multimodal analysis, joining transactional data with historical enterprise datasets.
5. VPC Service Controls (VPC-SC): The entire topology is wrapped in a VPC-SC perimeter. This ensures that the communication between AlloyDB and Vertex AI occurs exclusively over Google's private backbone, mitigating data exfiltration risks.

Step-by-Step Implementation

Implementing this architecture requires configuring the AlloyDB cluster to communicate with Vertex AI and utilizing the new SQL functions. Below is the technical implementation path using current 2026 gcloud CLI commands and standard SQL.

1. Infrastructure Provisioning & IAM Setup

First, ensure the AlloyDB cluster has the necessary IAM permissions to invoke Vertex AI models. The service account attached to the AlloyDB cluster must have the roles/aiplatform.user role.

# Set project variables
PROJECT_ID="enterprise-data-prod-2026"
REGION="us-central1"
CLUSTER_ID="alloydb-ai-cluster"
SA_NAME="alloydb-vertex-sa"

# Create a dedicated Service Account for AlloyDB
gcloud iam service-accounts create $SA_NAME \
    --display-name="AlloyDB to Vertex AI Integration SA"

# Grant Vertex AI User role to the Service Account
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_NAME@$PROJECT_ID.iam.gserviceaccount.com" \
    --role="roles/aiplatform.user"

# Update the AlloyDB cluster to use the new Service Account
gcloud alloydb clusters update $CLUSTER_ID \
    --region=$REGION \
    --service-account="$SA_NAME@$PROJECT_ID.iam.gserviceaccount.com"

2. Enabling the AI Functions in PostgreSQL

Connect to your AlloyDB instance using psql or Cloud SQL Studio. You must enable the preview AI functions within the google_ml_integration extension.

-- Enable the extension (requires version 1.6.1+)
CREATE EXTENSION IF NOT EXISTS google_ml_integration;

-- Enable the preview AI functions for the current session
SET google_ml_integration.enable_preview_ai_functions = TRUE;

3. Implementing ai.classify for Sentiment & Risk Analysis

The ai.classify function categorizes text without manual prompt engineering. In this example, we classify incoming customer support tickets. We use a 2D array to provide explicit context for each label, enabling few-shot prompting directly in SQL.

CREATE TABLE support_tickets (
    ticket_id INT PRIMARY KEY, 
    customer_message TEXT
);

INSERT INTO support_tickets VALUES 
(101, 'The new dashboard is incredibly fast and intuitive. Great job!'),
(102, 'I cannot access my account, it keeps throwing a 500 error. Help immediately!'),
(103, 'When is the next billing cycle?');

-- Execute multi-label classification with smart batching
SELECT 
    ticket_id,
    customer_message,
    unnest(
        ai.classify(
            inputs => ARRAY(SELECT customer_message FROM support_tickets ORDER BY ticket_id),
            categories => ARRAY[
                ['urgent_issue', 'System outages, errors, or immediate help required'],
                ['positive_feedback', 'Praise or good sentiment about the product'],
                ['billing_inquiry', 'Questions regarding invoices, payments, or cycles']
            ],
            batch_size => 10,
            output_mode => 'multi'
        )
    ) AS ticket_category
FROM support_tickets;

4. Implementing ai.extract for Structured JSON Generation

The ai.extract function transforms unstructured text into machine-readable JSON matching a target schema. This is highly effective for clinical notes or legal contracts. The function handles the system instructions internally, routing the request to Gemini 2.5 Flash.

-- Extracting structured data from a free-form medical note
SELECT ai.extract(
    input => 'Patient Jane Smith, 52 years old, visited the clinic on Oct 25, 2026. She is currently experiencing a mild fever, persistent cough, and severe body aches. The doctor prescribed Ibuprofen and Amoxicillin.',
    output_schema => '{
        "patient_name": {"type": "string"},
        "age": {"type": "integer"},
        "symptoms": {
            "type": "array",
            "items": {"type": "string"}
        },
        "prescribed_medications": {
            "type": "array",
            "items": {"type": "string"}
        }
    }'
) AS structured_clinical_record;

Expected Output:

{
  "patient_name": "Jane Smith",
  "age": 52,
  "symptoms": [
    "mild fever",
    "persistent cough",
    "severe body aches"
  ],
  "prescribed_medications": [
    "Ibuprofen",
    "Amoxicillin"
  ]
}

Production Readiness: FinOps, Quotas & Security Guardrails

Deploying in-database AI functions to production requires strict adherence to Google Cloud's Well-Architected Framework, particularly regarding security perimeters, quota management, and unit economics.

Security & Governance Guardrails

When processing sensitive unstructured text (like PHI or financial data), the primary directive is data residency. By utilizing ai.extract within AlloyDB, the data never traverses the public internet. However, architects must enforce VPC Service Controls (VPC-SC) to create a secure perimeter around the AlloyDB cluster and the Vertex AI API. Furthermore, IAM Database Authentication should be mandated to ensure that only authorized service accounts can execute the google_ml_integration functions, adhering to the principle of least privilege.

Quota Management & Connection Pooling

Executing AI functions at the database level shifts the bottleneck from the application's network I/O to the database's CPU and Vertex AI quotas.

• Vertex AI Quotas: Ensure your project has sufficient Tokens Per Minute (TPM) and Requests Per Minute (RPM) quotas for the specific model (e.g., gemini-2.5-flash) in your deployment region. Smart batching mitigates RPM exhaustion, but TPM must be monitored.
• Database Connection Pooling: Because AI functions take longer to execute than standard indexed SELECT queries, database connections are held open longer. Implement PgBouncer or the Cloud SQL Auth Proxy with connection pooling enabled to prevent connection starvation during burst traffic events.

FinOps & Unit Economics: The Cost of Intelligence

According to the Cost optimization pillar of the Google Cloud Architecture Framework, aligning cloud spending with business value requires continuous optimization of resource usage. The shift from an application-side LLM loop to in-database smart batching represents a massive leap in FinOps efficiency.

To quantify this, we modeled a production scenario processing 10 million unstructured text records per month. We compared the legacy approach (Option A: Cloud Run fetching data and calling Gemini 2.5 Pro) against the new native approach (Option B: AlloyDB smart batching using Gemini 2.5 Flash).

📊 Production FinOps & TCO Simulation: Unstructured Text Processing: App-Side vs. In-Database AI (10M Records/Month) (Based on the latest Google SKU information)

Production Workload Assumptions (us-central1 / asia-southeast1):

• Processing 10,000,000 unstructured text records (e.g., clinical notes, invoices) per month.
• Option A (Application-Side Loop): Uses Cloud Run to fetch data, call Vertex AI Gemini 2.5 Pro, parse, and write back. Cloud Run averages 1s per request at 2 vCPU / 4 GiB. LLM payload averages 500 input tokens and 100 output tokens per record.
• Option B (AlloyDB Smart Batching): Uses in-database ai.extract/ai.classify with Gemini 2.5 Flash. Smart batching reduces prompt overhead to 300 input tokens and 50 output tokens per record. Requires a 4-vCPU AlloyDB instance running 24/7 (730 hours/month).

Architecture Option	Google SKU Unit Price & Monthly Formula	Estimated Monthly Cost
Option A: Application-Side Loop (Cloud Run + Gemini 2.5 Pro)	Cloud Run vCPU Allocation (20M vCPU-sec): $2.4e-05/vCPU-second × 20,000,000 = $480.00 Cloud Run Memory Allocation (40M GiB-sec): $2.5e-06/GiB-second × 40,000,000 = $100.00 Vertex AI Gemini 2.5 Pro Input (5,000M tokens): $1.25/1M input tokens × 5,000 = $6,250.00 Vertex AI Gemini 2.5 Pro Output (1,000M tokens): $10/1M output tokens × 1,000 = $10,000.00	$16,830.00 / mo
Option B: In-Database Smart Batching (AlloyDB + Gemini 2.5 Flash)	AlloyDB Primary Instance (4 vCPU x 730 hrs): $0.0662/vCPU-hour × 2,920 = $193.30 Vertex AI Gemini 2.5 Flash Input (3,000M tokens): $0.15/1M input tokens × 3,000 = $450.00 Vertex AI Gemini 2.5 Flash Output (500M tokens): $0.6/1M output tokens × 500 = $300.00	$943.30 / mo
Net FinOps Impact (Monthly Savings)	Based on the latest Google SKU information	94.4% TCO Reduction ($15,886.70 / mo)

Official Google Cloud SKU Pricing Sources (2026.09): cloud.google.com, cloud.google.com, cloud.google.com

Architectural FinOps Analysis: The deterministic calculation reveals a staggering 94.4% reduction in Total Cost of Ownership (TCO). This optimization is driven by three architectural factors:

1. Elimination of Idle Compute: Option A requires Cloud Run instances to sit idle, burning vCPU and Memory seconds while waiting for network I/O from the LLM endpoint. Option B eliminates this middleware entirely.
2. Smart Batching Token Reduction: By grouping inputs into micro-batches within the SQL WHERE clause, AlloyDB drastically reduces the repetitive system prompt overhead sent to the model, lowering the total input tokens from 5,000M to 3,000M.
3. Model Right-Sizing: Because ai.extract and ai.classify handle the complex system instructions internally, workloads that previously required the heavy reasoning capabilities of Gemini 2.5 Pro can now be reliably executed by the much faster, cost-effective Gemini 2.5 Flash model.

By pushing AI execution down to the database engine, Google Cloud has not only simplified the developer experience but fundamentally rewritten the unit economics of processing unstructured enterprise data at scale.