Windmill vs AWS Glue
Detailed side-by-side comparison to help you choose the right tool
Windmill
App Deployment
Open-source developer platform turning scripts into workflows, UIs, and webhooks. Self-host free or use cloud from $8/user/month. Replace commercial platforms like Retool with claimed 13x faster execution than Airflow.
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CustomAWS Glue
App Deployment
AWS Glue is a serverless data integration service for discovering, preparing, and combining data for analytics, machine learning, and application development. It supports ETL workflows, data cataloging, and scalable data processing on AWS.
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CustomFeature Comparison
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Windmill - Pros & Cons
Pros
- ✓Open-source eliminates licensing fees: potential savings of hundreds of thousands annually vs commercial per-seat platforms for large teams
- ✓Vendor reports 13x faster execution than Airflow, with a Rust-based engine designed to reduce compute costs
- ✓Script-to-UI automation reduces internal tool development time from weeks to days
- ✓Self-hosting option provides complete control over data, security, and customization
- ✓No vendor lock-in: full code access and multiple deployment options
- ✓Enterprise-grade security with SOC2 compliance and audit logs included
- ✓Multi-language support: Python, TypeScript, Go, Bash scripts become workflows instantly
Cons
- ✗Self-hosting requires DevOps expertise and ongoing infrastructure management overhead
- ✗Smaller ecosystem compared to established platforms like Retool or Zapier
- ✗Learning curve for teams transitioning from commercial no-code platforms
- ✗Cloud pricing ($8/user/month) can become costly for large teams compared to self-hosting
- ✗Enterprise features and professional services add significant cost premium
- ✗Documentation and community support less mature than established alternatives
AWS Glue - Pros & Cons
Pros
- ✓Fully serverless with no infrastructure to provision, patch, or scale manually
- ✓Deep native integration with the AWS ecosystem (S3, Redshift, Athena, Lake Formation)
- ✓Always-free Data Catalog tier lowers the barrier for metadata management
- ✓Glue 4.0 significantly improved cold start times (up to 2.7x faster) and performance
- ✓Supports both batch and streaming ETL in a single service
- ✓DataBrew enables non-technical users to participate in data preparation
- ✓Auto-scaling adjusts DPUs dynamically to match workload, reducing over-provisioning
Cons
- ✗Cold start latency for Spark jobs can reach several minutes, making it unsuitable for low-latency or interactive workloads
- ✗Debugging Spark-based jobs can be complex—error messages are often opaque and require Spark expertise
- ✗VPC networking configuration for accessing private data sources adds operational complexity
- ✗Per-DPU-hour pricing can become expensive for long-running or always-on pipelines compared to reserved EMR clusters
- ✗Limited language support—primarily PySpark and Scala, with Ray support still maturing
- ✗Job orchestration capabilities are basic compared to dedicated tools like Apache Airflow or Step Functions
- ✗Vendor lock-in to AWS; migrating Glue-dependent pipelines to another cloud requires significant rework
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