• Over 100 pre-built connectors covering Azure, AWS, GCP, SaaS applications, on-premises databases, and legacy mainframes — eliminates most custom integration code
• Visual, code-free authoring through Data Factory Studio with Mapping Data Flows that compile to managed Spark jobs, making it accessible to non-developers while still scaling to large datasets
• SSIS Integration Runtime provides a lift-and-shift path for existing SQL Server Integration Services packages, a unique advantage for enterprises modernizing legacy Microsoft ETL estates
• Fully serverless with consumption-based pricing — no clusters to provision, patch, or scale, and the platform handles autoscaling of execution infrastructure
• Deep integration with the broader Azure ecosystem including Synapse Analytics, Data Lake Storage, Key Vault, Purview, Monitor, and managed identities for end-to-end governance and security
• Native CI/CD support via Azure DevOps and GitHub with ARM template publishing, enabling proper source control, code review, and multi-environment deployment workflows

⚠️ Consider This

• Pricing model is notoriously complex — pipeline orchestration, data movement (DIU-hours), data flow execution (vCore-hours), and integration runtime time are all metered separately, making cost forecasting difficult
• Mapping Data Flows have noticeable cluster startup latency (often 4-6 minutes per debug or job run) that makes iterative development slow and unsuitable for low-latency micro-batch workloads
• Streaming and true real-time processing are weak — ADF is fundamentally a batch and micro-batch tool; for sub-second event processing you need Azure Stream Analytics, Event Hubs, or Databricks Structured Streaming
• Strategic ambiguity between standalone ADF and Microsoft Fabric Data Factory creates uncertainty about long-term investment, with some new features landing in Fabric first
• Debugging complex pipelines and Mapping Data Flows can be painful — error messages from underlying Spark jobs are often opaque and require drilling into multiple monitoring panes to diagnose

What Users Say About Azure Data Factory

👍 What Users Love

✓Over 100 pre-built connectors covering Azure, AWS, GCP, SaaS applications, on-premises databases, and legacy mainframes — eliminates most custom integration code
✓Visual, code-free authoring through Data Factory Studio with Mapping Data Flows that compile to managed Spark jobs, making it accessible to non-developers while still scaling to large datasets
✓SSIS Integration Runtime provides a lift-and-shift path for existing SQL Server Integration Services packages, a unique advantage for enterprises modernizing legacy Microsoft ETL estates
✓Fully serverless with consumption-based pricing — no clusters to provision, patch, or scale, and the platform handles autoscaling of execution infrastructure
✓Deep integration with the broader Azure ecosystem including Synapse Analytics, Data Lake Storage, Key Vault, Purview, Monitor, and managed identities for end-to-end governance and security
✓Native CI/CD support via Azure DevOps and GitHub with ARM template publishing, enabling proper source control, code review, and multi-environment deployment workflows

👎 Common Concerns

⚠Pricing model is notoriously complex — pipeline orchestration, data movement (DIU-hours), data flow execution (vCore-hours), and integration runtime time are all metered separately, making cost forecasting difficult
⚠Mapping Data Flows have noticeable cluster startup latency (often 4-6 minutes per debug or job run) that makes iterative development slow and unsuitable for low-latency micro-batch workloads
⚠Streaming and true real-time processing are weak — ADF is fundamentally a batch and micro-batch tool; for sub-second event processing you need Azure Stream Analytics, Event Hubs, or Databricks Structured Streaming
⚠Strategic ambiguity between standalone ADF and Microsoft Fabric Data Factory creates uncertainty about long-term investment, with some new features landing in Fabric first
⚠Debugging complex pipelines and Mapping Data Flows can be painful — error messages from underlying Spark jobs are often opaque and require drilling into multiple monitoring panes to diagnose

Pricing FAQ

What is the difference between Azure Data Factory and Microsoft Fabric Data Factory?

Azure Data Factory is the standalone, mature PaaS service available as an independent Azure resource, billed on a granular pay-per-use model. Microsoft Fabric Data Factory is a re-imagined version embedded inside the Microsoft Fabric SaaS platform, sharing capacity-based pricing with the rest of Fabric (Power BI, Synapse, OneLake) and introducing new experiences like Dataflow Gen2 and Fabric pipelines. They share many concepts and connectors but are separate products with different pricing, governance, and integration models. Microsoft continues to invest in both, but new strategic features increasingly debut in Fabric first.

How does Azure Data Factory handle on-premises data sources?

ADF connects to on-premises and private-network data sources through the Self-Hosted Integration Runtime (SHIR), a lightweight agent installed on a Windows machine inside your network. The SHIR establishes outbound-only encrypted connections to the Azure Data Factory service, eliminating the need for inbound firewall rules or VPN tunnels. It supports clustering for high availability and load balancing across multiple nodes, and handles credential management locally so secrets never leave the network.

Can Azure Data Factory replace SQL Server Integration Services (SSIS)?

Yes, in two ways. First, ADF can natively rebuild SSIS workflows using its own pipeline and Mapping Data Flow capabilities, which is the recommended modernization path. Second, the SSIS Integration Runtime allows you to lift-and-shift existing SSIS packages into ADF with minimal changes, running them on managed Azure SSIS instances. This is unique to Azure and gives Microsoft-shop customers a gradual migration option rather than forcing a full rewrite.

How does Azure Data Factory pricing actually work?

ADF uses several separate consumption meters: pipeline orchestration (per activity run), data movement (per Data Integration Unit-hour for the Copy activity), data flow execution (per vCore-hour of the Spark cluster running Mapping Data Flows), SSIS Integration Runtime (per hour of provisioned compute), and inactive pipeline charges. Costs vary significantly based on workload patterns — a heavy data flow job can be far more expensive than a simple copy of the same data volume. Microsoft's pricing calculator and the cost analysis blade in Azure Cost Management are essential tools for forecasting.

Does Azure Data Factory support real-time or streaming data?

Not in the true streaming sense. ADF supports event-based triggers that fire pipelines in response to blob storage or custom events, and it can process micro-batches on tight schedules (down to 1 minute via tumbling windows), but it is not a stream processing engine. For sub-second latency, complex event processing, or continuous ingestion of high-velocity event streams, Microsoft recommends pairing ADF with Azure Event Hubs, Azure Stream Analytics, or Databricks Structured Streaming.

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More about Azure Data Factory

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Choose Your Plan

Pipeline Orchestration

~$1 per 1,000 activity runs (Azure IR); ~$1.50 per 1,000 (Self-Hosted IR)

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Data Movement (Copy Activity)

~$0.25 per DIU-hour (Azure IR); ~$0.10 per hour (Self-Hosted IR)

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Mapping Data Flows

~$0.193 per vCore-hour (General Purpose); ~$0.343 (Memory Optimized)

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SSIS Integration Runtime

From ~$0.33/hr (Standard D1 v2) up to several dollars/hr for larger nodes

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Inactive Pipelines

~$0.80 per inactive pipeline per month

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Pricing sourced from Azure Data Factory · Last verified March 2026

Is Azure Data Factory Worth It?

✅ Why Choose Azure Data Factory

• Over 100 pre-built connectors covering Azure, AWS, GCP, SaaS applications, on-premises databases, and legacy mainframes — eliminates most custom integration code
• Visual, code-free authoring through Data Factory Studio with Mapping Data Flows that compile to managed Spark jobs, making it accessible to non-developers while still scaling to large datasets
• SSIS Integration Runtime provides a lift-and-shift path for existing SQL Server Integration Services packages, a unique advantage for enterprises modernizing legacy Microsoft ETL estates
• Fully serverless with consumption-based pricing — no clusters to provision, patch, or scale, and the platform handles autoscaling of execution infrastructure
• Deep integration with the broader Azure ecosystem including Synapse Analytics, Data Lake Storage, Key Vault, Purview, Monitor, and managed identities for end-to-end governance and security
• Native CI/CD support via Azure DevOps and GitHub with ARM template publishing, enabling proper source control, code review, and multi-environment deployment workflows

⚠️ Consider This

• Pricing model is notoriously complex — pipeline orchestration, data movement (DIU-hours), data flow execution (vCore-hours), and integration runtime time are all metered separately, making cost forecasting difficult
• Mapping Data Flows have noticeable cluster startup latency (often 4-6 minutes per debug or job run) that makes iterative development slow and unsuitable for low-latency micro-batch workloads
• Streaming and true real-time processing are weak — ADF is fundamentally a batch and micro-batch tool; for sub-second event processing you need Azure Stream Analytics, Event Hubs, or Databricks Structured Streaming
• Strategic ambiguity between standalone ADF and Microsoft Fabric Data Factory creates uncertainty about long-term investment, with some new features landing in Fabric first
• Debugging complex pipelines and Mapping Data Flows can be painful — error messages from underlying Spark jobs are often opaque and require drilling into multiple monitoring panes to diagnose

What Users Say About Azure Data Factory

👍 What Users Love

✓Over 100 pre-built connectors covering Azure, AWS, GCP, SaaS applications, on-premises databases, and legacy mainframes — eliminates most custom integration code
✓Visual, code-free authoring through Data Factory Studio with Mapping Data Flows that compile to managed Spark jobs, making it accessible to non-developers while still scaling to large datasets
✓SSIS Integration Runtime provides a lift-and-shift path for existing SQL Server Integration Services packages, a unique advantage for enterprises modernizing legacy Microsoft ETL estates
✓Fully serverless with consumption-based pricing — no clusters to provision, patch, or scale, and the platform handles autoscaling of execution infrastructure
✓Deep integration with the broader Azure ecosystem including Synapse Analytics, Data Lake Storage, Key Vault, Purview, Monitor, and managed identities for end-to-end governance and security
✓Native CI/CD support via Azure DevOps and GitHub with ARM template publishing, enabling proper source control, code review, and multi-environment deployment workflows

👎 Common Concerns

⚠Pricing model is notoriously complex — pipeline orchestration, data movement (DIU-hours), data flow execution (vCore-hours), and integration runtime time are all metered separately, making cost forecasting difficult
⚠Mapping Data Flows have noticeable cluster startup latency (often 4-6 minutes per debug or job run) that makes iterative development slow and unsuitable for low-latency micro-batch workloads
⚠Streaming and true real-time processing are weak — ADF is fundamentally a batch and micro-batch tool; for sub-second event processing you need Azure Stream Analytics, Event Hubs, or Databricks Structured Streaming
⚠Strategic ambiguity between standalone ADF and Microsoft Fabric Data Factory creates uncertainty about long-term investment, with some new features landing in Fabric first
⚠Debugging complex pipelines and Mapping Data Flows can be painful — error messages from underlying Spark jobs are often opaque and require drilling into multiple monitoring panes to diagnose

Pricing FAQ