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AI ⇄ Data warehouse

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

Keep Azure OpenAI and Google Cloud Platform 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 Platform

Flow Azure OpenAI data into Google Cloud Platform in real time — no exports, no schedulers, no custom scripts.

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

Google Cloud Platform holds the raw records the business runs on; Azure OpenAI turns those records into embeddings, scores, labels, and summaries. The two meet wherever a warehouse row needs to be enriched by a model and the result needs somewhere durable to live. Most teams stitch that meeting together with export scripts and a queue, then spend their time keeping the glue alive.

The payoff is that model output stops living in a separate place from the data it describes. Once results sit in Google Cloud Platform, they join against billing, product, and usage tables already there, so you can report on quality, cost, and coverage without moving anything by hand.

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 Publish change events to Pub/Sub so downstream services react to record updates as they happen.

Common sync patterns

Keep an index in step with the source

As records change in Google Cloud Platform, matching Usage and quota, Assistants, Vector stores, Deployments in Azure OpenAI are inserted, updated, or removed, so what Azure OpenAI serves reflects the warehouse instead of a stale snapshot.

One place to analyze AI results

Combine Azure OpenAI's output with the tables already in Google Cloud Platform to report on model quality, cost, and coverage without exporting anything to a spreadsheet.

History that outlives a run

A continuously synced copy in Google Cloud Platform preserves every generated result, so outputs stay auditable even as they are overwritten or expire inside Azure OpenAI.

What you can sync between Azure OpenAI and Google Cloud Platform

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 Platform objects How this pairing syncs
Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Cloud SQL databases Managed Postgres, MySQL, and SQL Server instances synced like ordinary relational databases. Vector stores is specific to Azure OpenAI and Cloud SQL databases to Google Cloud Platform — 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. Cloud Storage objects Staging area for file-based bulk loads into BigQuery and other services. Deployments is specific to Azure OpenAI and Cloud Storage objects to Google Cloud Platform — 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. Pub/Sub topics Event streams used to move change events between systems in near real time. Models is specific to Azure OpenAI and Pub/Sub topics to Google Cloud Platform — 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. Firestore documents Document data read and written through the Firestore API for app-facing syncs. Fine-tuning jobs is specific to Azure OpenAI and Firestore documents to Google Cloud Platform — 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. Spanner tables Strongly consistent relational tables accessed via SQL for transactional workloads. Files is specific to Azure OpenAI and Spanner tables to Google Cloud Platform — 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. BigQuery datasets Namespaces that group tables; syncs target tables within a dataset. Batch jobs is specific to Azure OpenAI and BigQuery datasets to Google Cloud Platform — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Google Cloud Platform

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

Google Cloud Platform Azure OpenAI Sub-second propagation

DetectionGoogle Cloud Platform pushes changes as they happen — webhook events backed by change data capture. Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery.

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 Platform 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 Platform: Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own limits.
What ships with Azure OpenAI ⇄ Google Cloud Platform

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

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

Real-time

Real-time sync

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

Observability

Monitoring

Track your Azure OpenAI ⇄ Google Cloud Platform 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 Platform.

How the Azure OpenAI and Google Cloud Platform 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 Platform

Integration surface
Per-service REST and gRPC APIs; BigQuery speaks SQL and Cloud SQL exposes standard database wire protocols
Authentication
IAM service accounts with OAuth 2.0 tokens
Change detection
Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery tables
Capabilities
read · write · CDC · webhooks
Rate limits
Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own limits
How it works

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

    Choose tables

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

Azure OpenAI and Google Cloud Platform 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 512 integrations available for Azure OpenAI and Google Cloud Platform.

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