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AI ⇄ Database

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

Keep Azure OpenAI and Google Cloud Spanner 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 Spanner

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

Stacksync syncs Views, Databases, Tables, Rows in Google Cloud Spanner with Vector stores, Deployments, Models, Fine-tuning jobs in Azure OpenAI in real time. Rows created or changed in Google Cloud Spanner 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 Spanner, 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 Spanner 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 Run a two-way sync between Spanner and a SaaS tool so edits made by ops teams land back in the application database.
  • 04 Consolidate data from a globally distributed Spanner deployment into regional business systems.

Common sync patterns

Run the AI on current data

Rows created or changed in Google Cloud Spanner flow into Azure OpenAI as they happen, so embeddings, classifications, and prompts run on the latest records instead of a nightly snapshot.

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 Spanner, next to the source data your applications already query.

Keep derived data fresh as sources change

When a row in Google Cloud Spanner 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.

What you can sync between Azure OpenAI and Google Cloud Spanner

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 Spanner objects How this pairing syncs
Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Views Read-only projections useful for shaping data before it leaves Spanner. Assistants is specific to Azure OpenAI and Views to Google Cloud Spanner — each maps to any object or custom field on the other side.
Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Databases Top-level containers that scope schema and sync configuration. Vector stores is specific to Azure OpenAI and Databases to Google Cloud Spanner — 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. Tables Relational tables mapped one-to-one to sync targets. Deployments is specific to Azure OpenAI and Tables to Google Cloud Spanner — 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. Rows The unit of read and write in each sync cycle, keyed by primary key. Models is specific to Azure OpenAI and Rows to Google Cloud Spanner — 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. Interleaved tables Child rows physically co-located with parents; synced as related records. Fine-tuning jobs is specific to Azure OpenAI and Interleaved tables to Google Cloud Spanner — 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. Secondary indexes Used to make incremental read queries efficient on non-key columns. Files is specific to Azure OpenAI and Secondary indexes to Google Cloud Spanner — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Google Cloud Spanner

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

Google Cloud Spanner Azure OpenAI Sub-second propagation

DetectionChanges in Google Cloud Spanner are captured at the source via change data capture — no polling loop against its API. Change streams (log-style CDC), or timestamp-based polling queries.

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 Spanner 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 Spanner: Throughput is bounded by the instance's provisioned compute capacity rather than a fixed API quota.
What ships with Azure OpenAI ⇄ Google Cloud Spanner

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

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

Real-time

Real-time sync

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

Observability

Monitoring

Track your Azure OpenAI ⇄ Google Cloud Spanner 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 Spanner.

How the Azure OpenAI and Google Cloud Spanner 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 Spanner

Integration surface
gRPC/REST client API with SQL query surface (GoogleSQL and PostgreSQL-interface dialects)
Authentication
Google Cloud IAM (service accounts)
Change detection
Change streams (log-style CDC), or timestamp-based polling queries
Capabilities
read · write · CDC
Rate limits
Throughput is bounded by the instance's provisioned compute capacity rather than a fixed API quota.
How it works

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

    Choose tables

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

Azure OpenAI and Google Cloud Spanner 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 400 integrations available for Azure OpenAI and Google Cloud Spanner.

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