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AI ⇄ Database

Azure OpenAI to SQL Server integration — real-time data sync

Keep Azure OpenAI and SQL Server 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 SQL Server

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

Stacksync syncs Tables, Views, Columns, Primary and Unique Keys in SQL Server with Assistants, Vector stores, Deployments, Models in Azure OpenAI in real time. Rows created or changed in SQL Server 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 SQL Server, 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 SQL Server 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 Sync the Deployments inventory (model, version, TPM capacity) into Postgres so platform teams track every Azure OpenAI deployment across subscriptions in SQL.
  • 02 Land Fine-tuning jobs with their status, base model, and result Files in a warehouse to power MLOps dashboards without per-viewer API calls.
  • 03 Mirror on-premises ERP data held in SQL Server into cloud CRM and support systems
  • 04 Feed a cloud warehouse from SQL Server continuously using native CDC instead of SSIS batch jobs

Common sync patterns

One record, one identifier

Each item in Azure OpenAI carries the key of the row in SQL Server it came from, so results resolve back to the exact record with nothing orphaned or duplicated.

Run the AI on current data

Rows created or changed in SQL Server 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 SQL Server, next to the source data your applications already query.

What you can sync between Azure OpenAI and SQL Server

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 SQL Server objects How this pairing syncs
Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Stored Procedures T-SQL logic that can validate or post-process synced rows. Assistants is specific to Azure OpenAI and Stored Procedures to SQL Server — 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 Instance-level databases that scope a sync's reads and writes. Vector stores is specific to Azure OpenAI and Databases to SQL Server — 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. Schemas Namespaces (dbo and custom) used to organize synced tables. Deployments is specific to Azure OpenAI and Schemas to SQL Server — 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. Tables The primary sync target; rows map to records in connected systems. Models is specific to Azure OpenAI and Tables to SQL Server — 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. Views Read-side projections used as outbound sync sources. Fine-tuning jobs is specific to Azure OpenAI and Views to SQL Server — 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. Columns Field-level mapping targets with T-SQL types. Files is specific to Azure OpenAI and Columns to SQL Server — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and SQL Server

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

SQL Server Azure OpenAI Sub-second propagation

DetectionChanges in SQL Server are captured at the source via change data capture — no polling loop against its API. SQL Server Native Change Data Capture (CDC).

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 SQL Server 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.
  • SQL Server: No API rate limits; throughput depends on instance resources, licensing tier, and connection limits.
What ships with Azure OpenAI ⇄ SQL Server

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Azure OpenAI or SQL Server 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 SQL Server record.

Observability

Monitoring

Track your Azure OpenAI ⇄ SQL Server 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 SQL Server.

How the Azure OpenAI and SQL Server 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.

SQL Server

Integration surface
SQL over the TDS wire protocol (Tabular Data Stream), via ODBC/JDBC/ADO.NET drivers
Authentication
Database credentials entered as a connection string or as parameters (host/user/password) in the Create New Sync page
Change detection
SQL Server Native Change Data Capture (CDC); a DBA runs a one-time setup script with sysadmin privileges to enable CDC and create Stacksync wrapper procedures
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput depends on instance resources, licensing tier, and connection limits
SQL Server setup guide
How it works

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

    Choose tables

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

Azure OpenAI and SQL Server 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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→ SECURITY WITH BENEFITS

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 SQL Server.

Popular · 8 of 512
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