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AI ⇄ Database

Azure OpenAI to Google Cloud SQL integration — real-time data sync

Keep Azure OpenAI and Google Cloud SQL 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Azure OpenAI and Google Cloud SQL

Sync the records in Google Cloud SQL 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 Cloud SQL, so Google Cloud SQL 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 Cloud SQL 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 Cloud SQL it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Instances, Databases, Schemas, Tables in Google Cloud SQL with Deployments, Models, Fine-tuning jobs, Files in Azure OpenAI in real time. Rows created or changed in Google Cloud SQL 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 Cloud SQL, 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 Cloud SQL 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 Pull per-deployment TPM/RPM usage into a warehouse for FinOps chargeback and quota-exhaustion alerting.
  • 02 Mirror Assistants and Vector stores configuration into a database as an auditable inventory of retrieval assets and their linked files.
  • 03 Keep an internal admin application backed by Cloud SQL consistent with an ERP or billing system.
  • 04 Migrate from a self-managed database by syncing Cloud SQL and the legacy system during cutover.

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 Cloud SQL, next to the source data your applications already query.

Keep derived data fresh as sources change

When a row in Google Cloud SQL 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 Cloud SQL 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 Cloud SQL

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 Cloud SQL objects How this pairing syncs
Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Tables Mapped directly to sync targets; schema changes can be propagated. Vector stores is specific to Azure OpenAI and Tables to Google Cloud SQL — each maps to any object or custom field on the other side.
Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. Rows Read and written by primary key during each sync cycle. Deployments is specific to Azure OpenAI and Rows to Google Cloud SQL — each maps to any object or custom field on the other side.
Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Views Read-only sources for shaping data before syncing it out. Models is specific to Azure OpenAI and Views to Google Cloud SQL — 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. Transaction logs MySQL binlog or PostgreSQL WAL, the source for log-based change capture. Fine-tuning jobs is specific to Azure OpenAI and Transaction logs to Google Cloud SQL — 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. Instances The managed MySQL, PostgreSQL, or SQL Server server a sync connects to. Files is specific to Azure OpenAI and Instances to Google Cloud SQL — 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. Databases Scope the tables included in a sync configuration. Batch jobs is specific to Azure OpenAI and Databases to Google Cloud SQL — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Google Cloud SQL

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 Cloud SQL 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 Cloud SQL as a row-level write, with types converted between the two schemas.

Google Cloud SQL Azure OpenAI Sub-second propagation

DetectionChanges in Google Cloud SQL are captured at the source via change data capture — no polling loop against its API. Engine-dependent log-based CDC: MySQL binlog, PostgreSQL logical replication, SQL Server change tracking.

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 Cloud SQL 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 Cloud SQL: Constrained by instance size and connection limits rather than API quotas.
What ships with Azure OpenAI ⇄ Google Cloud SQL

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

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

Real-time

Real-time sync

Changes in Azure OpenAI or Google Cloud SQL 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 Cloud SQL 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 Cloud SQL record.

Observability

Monitoring

Track your Azure OpenAI ⇄ Google Cloud SQL 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 Cloud SQL.

How the Azure OpenAI and Google Cloud SQL 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 Cloud SQL

Integration surface
Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management
Authentication
Database credentials; IAM database authentication is available for MySQL and PostgreSQL
Change detection
Engine-dependent log-based CDC: MySQL binlog, PostgreSQL logical replication, SQL Server change tracking; polling as a fallback
Capabilities
read · write · CDC
Rate limits
Constrained by instance size and connection limits rather than API quotas.
How it works

How to connect Azure OpenAI to Google Cloud SQL — 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 Cloud SQL 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 Cloud SQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Azure OpenAI and Google Cloud SQL 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 Cloud SQL
    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 Cloud SQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Azure OpenAI and Google Cloud SQL 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 408 integrations available for Azure OpenAI and Google Cloud SQL.

Popular · 7 of 408
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