Airbyte vs AWS Glue at a glance
| Airbyte | AWS Glue |
|---|
| Category | Data movement (ELT and CDC) platform | Serverless Spark ETL and data catalog |
| Prebuilt connectors | 700+, including SaaS APIs, databases, files and unstructured sources | AWS-native sources (S3, RDS, DynamoDB, Redshift, Kinesis), JDBC, plus native and AWS Marketplace connectors for selected SaaS |
| Building a pipeline | Configure source and destination in UI, API or Terraform | Write or generate PySpark or Scala jobs, or use Glue Studio |
| Custom sources | No-code Connector Builder with AI assist, low-code CDK, Python CDK | Custom Spark code or custom connectors |
| Schema changes | Detected and propagated automatically | Crawlers and job logic you manage |
| Change data capture | Log-based CDC on every plan | Via job bookmarks, zero-ETL integrations or AWS DMS |
| Destinations | Any warehouse, lakehouse, database or vector store, on any cloud | Primarily AWS (S3, Redshift, SageMaker Lakehouse) |
| Pricing model | Volume-based (Standard, Plus) or capacity-based Data Workers (Pro, Enterprise Flex) | $0.308 per DPU-hour on Glue 6.0, $0.44 on 5.1 and earlier (us-east-1); zero-ETL app ingestion $1.50/GB |
| Free option | Airbyte Core (self-hosted) | No free DPU-hours for jobs or crawlers |
| Deployment | Self-hosted (Core), managed cloud, hybrid or on-prem (Enterprise Flex) | AWS-managed only |
| Multi-cloud | Yes | AWS-centric |
| Open source | Yes | No |
| Who can use it | Analysts (UI) and engineers (API, Terraform, PyAirbyte) | Data engineers with Spark skills |
About Airbyte
Airbyte is the open standard for data movement, used by 18% of the Fortune 500. It replicates data from 700+ sources into warehouses, lakehouses, databases and AI context stores, and runs self-hosted, fully managed, or hybrid inside your own boundary.
About AWS Glue
AWS Glue is Amazon's serverless data integration service. It runs Apache Spark, Python shell and Ray jobs, catalogs data with crawlers and the Glue Data Catalog, and offers zero-ETL integrations into S3, Redshift and SageMaker Lakehouse. Glue 6.0 shipped in August 2026 with a newer Spark runtime, Apache Iceberg v3 support and 30% lower DPU rates.
Glue is built to process data with Spark. For ingestion from SaaS apps and APIs, that means writing or generating jobs that handle authentication, pagination, rate limits, incremental state and schema changes. Every source becomes code to maintain.
Airbyte connectors ship with that logic built in and maintained for you. You configure a source and destination, and syncs run with incremental loads, CDC and schema propagation. When a source is missing, the Connector Builder creates one from API docs with AI assist.
Many AWS teams use both: Airbyte to land data in S3, Redshift or Iceberg tables, and Glue or dbt to transform it.
Pricing: compute time vs. capacity
Glue bills per DPU-hour, per second with a one-minute minimum. In us-east-1, Spark jobs cost $0.308 per DPU-hour on Glue 6.0 and $0.44 on Glue 5.1 and earlier. Crawlers, interactive sessions and data quality add DPU time, streaming jobs bill continuously, and zero-ETL application ingestion adds $1.50 per GB.
That makes cost depend on how well each job is sized and tuned. Over-provisioned workers, frequent crawlers and always-on streaming are common sources of surprise bills.
Airbyte Core is free to self-host. Cloud starts at $20/month (Standard) and $189/month (Plus). Pro and Enterprise Flex price on Data Workers, dedicated compute capacity, so cost doesn't depend on job tuning or row counts.
Multi-cloud and deployment
Glue runs only in AWS and is designed around AWS destinations.
Airbyte writes to Snowflake, BigQuery, Databricks, Redshift, Postgres, S3, Iceberg lakehouses and vector stores, on any cloud. It runs four ways on one codebase: free self-hosted Core, managed cloud, hybrid with an Airbyte-managed control plane, or fully on-prem with Enterprise Flex. You can also run it inside your own AWS account.
Who builds and runs it
Glue is a tool for data engineers comfortable with Spark, job tuning and AWS IAM.
Airbyte serves the whole team. Analysts add sources in the UI, while engineers manage pipelines as code with the Airbyte API, Terraform provider and PyAirbyte, orchestrated by Airflow, Dagster or Prefect.
Where AWS Glue leads
Glue is a strong fit for heavy Spark transformation over large S3 data lakes, and its Data Catalog is the default metastore for Athena, EMR and Redshift Spectrum.
When to choose Airbyte vs AWS Glue
| Your situation | Better fit |
|---|
| You need SaaS and API data (Salesforce, HubSpot, Stripe, Zendesk) | Airbyte |
| You don't want to maintain Spark ingestion code | Airbyte |
| You load into non-AWS destinations, or several clouds | Airbyte |
| Analysts need to add sources without engineers | Airbyte |
| You want cost that doesn't depend on job tuning | Airbyte (capacity-based) |
| You need CDC from production databases with minimal setup | Airbyte |
| You run heavy Spark transformations on an S3 data lake | AWS Glue |
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