CodeSandbox vs AWS Glue
Detailed side-by-side comparison to help you choose the right tool
CodeSandbox
🟡Low CodeApp Deployment
CodeSandbox is a cloud development and code-execution platform — now part of Together AI — built around the Sandbox SDK and Firecracker microVMs with 2-second startup for AI agents and dev environments.
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FreeAWS 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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CodeSandbox - Pros & Cons
Pros
- ✓2-second VM startup on Firecracker microVMs is best-in-class for many AI agent workloads
- ✓Sandpack and Storybook integrations have unmatched distribution across JS/React docs and learning sites
- ✓Together AI ownership ties the SDK to a clear model/inference + agent infrastructure path
Cons
- ✗Pricing page now blocks crawlers (HTTP 403) — pricing transparency dropped after the Together AI acquisition
- ✗No public MCP server yet — agent integrations go through the CodeSandbox SDK directly
- ✗Modal Labs and E2B can be cheaper per second for pure Python eval workloads without browser IDE needs
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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