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AI ⇄ Database

Azure OpenAI to Google AlloyDB integration — real-time data sync

Keep Azure OpenAI and Google AlloyDB in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect Azure OpenAI and Google AlloyDB

Sync the records in Google AlloyDB into Azure OpenAI and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.

Azure OpenAI is a read-only source: Stacksync reads its data in real time and delivers it into Google AlloyDB, so Google AlloyDB always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.

AI systems do not hold customers or invoices the way business apps do. What they hold is derived from your data: the vectors and metadata in a vector store, or the classifications, extracted fields, and generated text a model produces over records it was given. Google AlloyDB is where those source records actually live. The bridge between the two is the row itself, since an item in Azure OpenAI and the record in Google AlloyDB it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Sequences, Replication Slots, Databases, Schemas in Google AlloyDB with Files, Batch jobs, Usage and quota, Assistants in Azure OpenAI in real time. Rows created or changed in Google AlloyDB flow into Azure OpenAI so inference and embedding run on current data, and the scores, labels, and generated fields Azure OpenAI produces flow back onto the matching rows in Google AlloyDB, mapped field by field. A change on either side appears on the other within seconds, with no extraction job or webhook plumbing to keep alive.

Because matching is by a stable identifier, every row in Google AlloyDB stays tied to its AI-side counterpart in Azure OpenAI. Retrieval, enrichment, and generated content always resolve back to the record they came from, so there are no orphaned vectors and no labels describing a version of a row that no longer exists.

Common use cases

  • 01 Mirror Assistants and Vector stores configuration into a database as an auditable inventory of retrieval assets and their linked files.
  • 02 Sync the Deployments inventory (model, version, TPM capacity) into Postgres so platform teams track every Azure OpenAI deployment across subscriptions in SQL.
  • 03 Mirror CRM objects into AlloyDB tables so product and internal tools query customer data with plain SQL.
  • 04 Use logical replication to feed AlloyDB changes into warehouses or event pipelines without batch jobs.

Common sync patterns

Write results back onto the record

Scores, labels, extracted fields, or generated text produced in Azure OpenAI land on the matching row in Google AlloyDB, next to the source data your applications already query.

Keep derived data fresh as sources change

When a row in Google AlloyDB is updated or removed, its counterpart in Azure OpenAI is updated or removed too, so nothing in Azure OpenAI describes a record that has since changed or gone.

Backfill once, then stay in step

Load your existing rows from Google AlloyDB into Azure OpenAI to build the index or enrichment set, then let ongoing changes sync automatically instead of re-running the whole job.

What you can sync between Azure OpenAI and Google AlloyDB

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Azure OpenAI objects Google AlloyDB objects How this pairing syncs
Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Databases Standard PostgreSQL databases within an AlloyDB cluster that syncs connect to. Models is specific to Azure OpenAI and Databases to Google AlloyDB — each maps to any object or custom field on the other side.
Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Schemas Namespaces used to separate synced SaaS data from application tables. Fine-tuning jobs is specific to Azure OpenAI and Schemas to Google AlloyDB — each maps to any object or custom field on the other side.
Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. Tables Primary read/write target for bi-directional sync with CRMs and other systems. Files is specific to Azure OpenAI and Tables to Google AlloyDB — each maps to any object or custom field on the other side.
Batch jobs Asynchronous bulk-inference jobs; status and output-file IDs are polled to completion to drive downstream pipeline triggers. Views Curated projections used as read-only sync sources. Batch jobs is specific to Azure OpenAI and Views to Google AlloyDB — each maps to any object or custom field on the other side.
Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. Materialized Views Precomputed aggregates refreshed and synced outward on a schedule. Usage and quota is specific to Azure OpenAI and Materialized Views to Google AlloyDB — each maps to any object or custom field on the other side.
Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Indexes Keep sync key lookups fast on high-volume tables. Assistants is specific to Azure OpenAI and Indexes to Google AlloyDB — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Google AlloyDB

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Azure OpenAI Google AlloyDB Interval-based propagation

DetectionStacksync polls Azure OpenAI for changes on an incremental schedule, reading only records changed since the previous pass. Polling: list endpoints plus GET on job IDs for status.

DeliveryEach detected change is applied to Google AlloyDB as a row-level write, with types converted between the two schemas.

Google AlloyDB Azure OpenAI Sub-second propagation

DetectionChanges in Google AlloyDB are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication.

DeliveryAzure OpenAI does not accept inbound record writes, so this direction carries requests rather than records: Azure OpenAI's output flows back as field updates on the originating Google AlloyDB records.

Rate-limit considerations

  • Azure OpenAI: Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.
  • Google AlloyDB: No API-style rate limits; throughput is bounded by instance size.
What ships with Azure OpenAI ⇄ Google AlloyDB

Connect Azure OpenAI and Google AlloyDB for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Azure OpenAI–Google AlloyDB connection.

Real-time

Real-time sync

Changes in Azure OpenAI or Google AlloyDB instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Azure OpenAI or Google AlloyDB data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Azure OpenAI or Google AlloyDB record.

Observability

Monitoring

Track your Azure OpenAI ⇄ Google AlloyDB sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Azure OpenAI and Google AlloyDB.

How the Azure OpenAI and Google AlloyDB connectors work

Azure OpenAI

Integration surface
REST data-plane (inference + authoring) and Azure Resource Manager control-plane
Authentication
API key in the api-key header, or a Microsoft Entra ID bearer token / managed identity
Change detection
Polling: list endpoints plus GET on job IDs for status; no webhooks or change feed. Fine-tuning and batch jobs expose queued/running/succeeded states.
Capabilities
read
Rate limits
Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.

Google AlloyDB

Integration surface
SQL wire protocol (PostgreSQL-compatible), with connectivity through the AlloyDB Auth Proxy or private IP
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC via PostgreSQL logical replication; polling on timestamp columns as a fallback
Capabilities
read · write · CDC
Rate limits
No API-style rate limits; throughput is bounded by instance size
How it works

How to connect Azure OpenAI to Google AlloyDB — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Azure OpenAI and Google AlloyDB with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Azure OpenAI connected
    Google AlloyDB connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Azure OpenAI and Google AlloyDB objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Azure OpenAI ⇄ Google AlloyDB
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Azure OpenAI Google AlloyDB
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Azure OpenAI and Google AlloyDB integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 420 integrations available for Azure OpenAI and Google AlloyDB.

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