Your first AWS Glue test job cost $0.66. Why is your production bill now $8,500/month? This disconnect between expected and actual costs is the central challenge facing data teams evaluating AWS Glue's serverless ETL service. While the base ETL list price is straightforward, actual AWS Glue spend can combine multiple separate pricing components, including ETL compute, crawlers, interactive sessions, Data Catalog usage, DataBrew, Data Quality, and adjacent AWS service charges.
Important: Prices may vary based on your Region, so treat the dollar figures below as reference pricing, not universal global rates.
AWS Glue's pay-as-you-go model promises you only pay for what you use—no upfront fees, no idle charges. But for organizations accustomed to predictable monthly budgets, the variable nature of consumption-based pricing creates forecasting challenges that fixed-fee data pipelines can eliminate entirely.
Key Takeaways
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AWS Glue charges by feature: ETL jobs, interactive sessions, crawlers, Data Catalog, DataBrew, and Data Quality each have distinct pricing models
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ETL jobs and interactive sessions are billed at $0.44 per DPU-hour, charged per second with a 1-minute minimum
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Flex execution reduces costs by 34% ($0.29/DPU-hour) for non-urgent batch jobs that can tolerate delayed start times
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The Data Catalog provides 1 million free objects and 1 million free requests monthly, then charges $1 per 100,000 objects beyond the free tier
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Development Endpoints left running can add significant monthly costs—a common first-month oversight
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Right-sizing DPU allocation can reduce costs significantly, but the optimal worker configuration depends on your workload, runtime, and worker type rather than a universal rule of thumb. AWS documents a minimum of 2 DPUs for ETL jobs and a default allocation of 10 DPUs.
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Fixed-fee alternatives like Integrate.io offer unlimited data volumes at $1,999/month, providing budget certainty regardless of workload growth
Understanding the Core Components of AWS Glue Pricing in 2026
AWS Glue pricing is structured by feature rather than as a single flat rate. Your bill typically combines multiple cost components depending on which services you use.
Core Pricing Model
AWS Glue charges:
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An hourly rate, billed by the second, for ETL jobs, interactive sessions, crawlers, and certain Data Catalog compute tasks
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A monthly fee for Data Catalog metadata storage and access
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Per-session pricing for DataBrew interactive sessions
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Per-minute/node-hour based pricing for DataBrew jobs
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No additional charge for the AWS Glue Schema Registry
What Are Data Processing Units (DPUs)?
DPUs are the primary billing unit for AWS Glue ETL jobs and interactive sessions. Each DPU provides 4 vCPU and 16 GB memory. The standard rate is $0.44 per DPU-hour, billed per second with a 1-minute minimum per job run.
AWS provides examples:
Your cost scales primarily with:
Standard worker types include:
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G.1X: 1 DPU per worker (4 vCPU, 16 GB RAM)
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G.2X: 2 DPUs per worker (8 vCPU, 32 GB RAM)
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G.4X: 4 DPUs per worker (16 vCPU, 64 GB RAM)
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G.8X: 8 DPUs per worker (32 vCPU, 128 GB RAM)
How Data Catalog Pricing Works
The Glue Data Catalog serves as your centralized metadata repository. AWS provides generous limits:
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First 1 million objects stored: Free
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First 1 million requests: Free
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Beyond free tier: $1 per 100,000 objects, $1 per million requests
AWS defines metadata objects broadly, including:
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Tables
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Table versions
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Partitions
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Partition indexes
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Statistics
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Databases
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Catalogs
AWS examples show that storing 1 million metadata objects and making 1 million metadata requests in a month costs $0. If requests rise to 2 million, with the first million free, the extra 1 million requests cost $1.
Data Catalog Maintenance and Statistics
The Data Catalog also charges for managed compute used for:
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Apache Iceberg table optimization/compaction
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Column-level statistics generation
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Materialized view auto-refresh
For each of these, AWS lists:
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$0.44 per DPU-hour
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Billed per second
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1-minute minimum per run
Examples from AWS:
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Statistics job: 10 minutes, 1 DPU = $0.07
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Iceberg compaction: 30 minutes, 2 DPUs = $0.44
Data Catalog-Related Extra Charges
The Data Catalog itself does not replace storage or downstream compute charges:
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If your data is in Amazon S3, you still pay standard S3 storage, requests, and data transfer
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If your data is in Amazon Redshift, you still pay standard Redshift storage
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If Redshift Serverless compute is used to filter/query table results from other engines, those Redshift Serverless charges also apply
AWS also states there are no separate charges for using Lake Formation permissions with the Data Catalog.
Crawler Pricing
AWS Glue Crawlers are billed at $0.44 per DPU-hour. AWS's example shows:
This is relevant when you use Glue to discover schemas or detect new tables and partitions in your data sources.
Strategies for Reducing ETL Job Duration
Job bookmarks prevent reprocessing previously loaded data, reducing runtime and costs significantly. Enable them on day one for any incremental workload.
Additional optimization tactics:
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Use partitioned data in S3 (date-based partitioning is most common)
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Convert source files to Parquet format for faster processing
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Enable auto-scaling to match worker count to actual workload
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Set appropriate timeout values to prevent runaway jobs
Monitoring ETL Expenses
AWS Cost Explorer filtered by the Glue service reveals your top 5 most expensive jobs. Weekly reviews help identify optimization opportunities before costs increase.
Understanding DataBrew Pricing
AWS splits DataBrew pricing into interactive sessions and jobs.
DataBrew Interactive Sessions
AWS lists $1.00 per 30-minute interactive session. Examples indicate this is session-based rather than pure second-by-second compute billing:
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A short return within the same 30-minute window counts as 1 session = $1.00
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Extended usage across multiple windows counts as multiple sessions, for example 3 sessions = $3.00
DataBrew Jobs
AWS lists DataBrew job pricing at $0.48 per node-hour. The example given is:
DataBrew job cost is mainly driven by:
Data Quality Pricing
AWS Glue Data Quality pricing depends on how you use it.
Data Quality for Cataloged Datasets
For datasets cataloged in the Data Catalog, AWS says:
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Recommendation tasks and evaluation tasks use provisioned DPUs
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There is a minimum of 2 DPUs
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There is a 1-minute minimum billing duration
Examples:
Data Quality Inside ETL Jobs
If you embed data quality checks in AWS Glue ETL jobs, the cost shows up as:
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Increased runtime
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Increased DPU usage
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Or both
AWS's example:
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ETL job with data quality, 20 minutes at 6 DPUs = $0.88
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With Flex execution, the same workload is shown at $0.58, reflecting the lower $0.29 per DPU-hour Flex rate
Anomaly Detection and Retraining
For anomaly detection in AWS Glue ETL:
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AWS says you incur 1 extra DPU per statistic for anomaly detection time
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Average detection time is stated as roughly 10–20 seconds per statistic
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There is a 1-second minimum
AWS's example with 20 statistics adds about $0.037 in anomaly-detection cost on top of the base ETL job, bringing the total to $0.917. Retraining is also billed at 1 DPU per statistic for retraining time, and AWS's example for excluding one anomalous statistic costs about $0.00185.
Data Quality Storage and Other Related Costs
AWS says:
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There is no charge to store gathered statistics
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Storage is capped at 100K statistics per account, retained for 2 years
However, related infrastructure still costs extra:
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S3 storage, requests, and transfer for temporary files/results/shuffle data are billed at standard S3 rates
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If you use the Data Catalog, normal Data Catalog rates also apply
Zero-ETL Pricing
AWS says there is no separate charge for the zero-ETL integration feature itself, but that does not mean the workflow is free. For supported application-source zero-ETL integrations, AWS charges for ingestion based on the volume of source data received, with a 1 MB minimum billable volume per ingestion request, and you also pay for the destination services involved such as Redshift Serverless or AWS Glue compute depending on the target architecture.
Application-Source Zero-ETL into Redshift or SageMaker Lakehouse
For application-supported zero-ETL sources:
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AWS charges for the ingestion of application source data
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Billing is based on the volume of data received
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Each ingestion request has a 1 MB minimum billable volume
Then, depending on the destination:
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If written to Amazon Redshift, you also pay based on Redshift pricing
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If written to SageMaker Lakehouse, the processing charge depends on the storage type:
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Redshift managed storage uses Redshift Serverless compute pricing
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S3-backed storage uses AWS Glue compute per DPU-hour, billed per second with a 1-minute minimum
DynamoDB Zero-ETL into SageMaker Lakehouse
For DynamoDB zero-ETL:
Leveraging the AWS Pricing Calculator for Accurate Cost Estimates
The AWS Pricing Calculator helps project costs before deployment. Effective estimation requires understanding your workload characteristics:
Input variables to gather:
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Number of ETL jobs per day/week/month
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Average job duration (test with sample data first)
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Required worker count (start conservative)
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Sync frequency requirements (impacts streaming vs. batch choice)
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Data Catalog object counts
Common Pitfalls in Cost Estimation
Organizations frequently underestimate costs by:
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Using default 10 workers when 3-5 would suffice
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Forgetting crawler costs (charged at same DPU-hour rate)
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Ignoring cross-region data transfer fees
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Missing CloudWatch logging charges
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Leaving Development Endpoints running after testing
Integrating Calculator Results into Budgeting
Build a 30% buffer into initial estimates. Production workloads often exceed test conditions due to data volume variations, schema changes, and business growth.
The Impact of Data Volume and Throughput on AWS Glue Costs
Unlike fixed-fee platforms offering unlimited data volumes, AWS Glue costs scale directly with data processed.
How Data Processing Volume Affects DPU Consumption
Processing 1 billion rows requires more DPUs and longer runtimes than 1 million rows. The relationship isn't linear—well-optimized jobs with partitioned data scale more efficiently than basic implementations.
Cost implications of frequently changing data:
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CDC workloads with constant updates increase job frequency
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Schema evolution triggers Data Catalog updates and potential reprocessing
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Late-arriving data may require backfill jobs
Strategies for Efficient Data Handling
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Filter source data before extraction (reduce bytes processed)
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Use incremental loads via job bookmarks
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Compress and convert to columnar formats (Parquet, ORC)
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Implement data lifecycle policies to archive historical data
Understanding Development Endpoint Pricing
Development Endpoints enable interactive Spark development but don't auto-stop. Unlike Interactive Sessions (which idle-timeout), endpoints run until manually terminated.
Set CloudWatch alarms for:
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Endpoints active longer than 4 hours
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Unexpected endpoint creation
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Total endpoint costs exceeding thresholds
This single oversight can account for significant unplanned monthly costs for many organizations.
Key Factors for Predicting AWS Glue Costs for Data Lakes and Warehouses
Large-scale data projects require comprehensive cost modeling beyond individual job estimates.
Estimating Costs for Large-Scale Data Projects
Data lake management involves:
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Crawler costs for schema discovery across hundreds of tables
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ETL job costs for transformation pipelines
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Data quality validation runs
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Orchestration overhead (Glue Workflows or Step Functions)
Integrating Glue with S3 and Redshift
Glue's native Redshift connector optimizes loads, but cross-region configurations add data transfer costs. Keep source S3 buckets, Glue jobs, and Redshift clusters in the same region to avoid data egress charges.
Data Quality Monitoring
Automated data quality checks prevent reprocessing when issues reach downstream systems. Integrate.io's free Data Observability platform provides:
Catching quality issues early prevents expensive remediation cycles.
Free and No-Extra-Charge Items
From AWS's pricing page, the main "free" or "no extra charge" items are:
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First 1 million Data Catalog metadata objects: free
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First 1 million Data Catalog accesses/requests: free
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AWS Glue Schema Registry: no additional charge
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Lake Formation permissions used with Data Catalog: no separate charge
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Statistics storage for Data Quality: no charge, subject to limits
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Zero-ETL integration itself: no extra fee, though source/target resources still cost money
Why Integrate.io Delivers Predictable Costs for Data Pipeline Investments
For organizations where budget certainty matters as much as capability, Integrate.io addresses the core challenges of consumption-based pricing models.
Fixed-Fee Simplicity
Integrate.io charges a flat $1,999/month for:
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Unlimited data volumes
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Unlimited pipelines
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Unlimited connectors (150+)
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Full platform access (ETL, ELT, CDC, Reverse ETL, API Management)
No DPU calculations, no surprise bills, no cost governance overhead.
Real-Time Capabilities
Integrate.io delivers 60-second CDC replication on every plan—not reserved for enterprise tiers.
Low-Code Accessibility
While Glue requires PySpark proficiency, Integrate.io's 220+ drag-and-drop transformations enable business analysts to build production pipelines. This reduces data engineer bottlenecks and accelerates time-to-value.
Enterprise-Grade Security Included
Every Integrate.io customer receives SOC 2, GDPR, HIPAA compliance plus:
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30-day white-glove onboarding
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Dedicated Solution Engineer access
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24/7 support
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CISSP-certified security team guidance
For data teams seeking cost predictability without sacrificing capability, Integrate.io offers an alternative to AWS Glue's variable pricing model.
Practical Takeaway
AWS Glue pricing is not a single flat SKU. Your bill usually comes from a combination of:
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Compute consumption measured in DPUs or node-hours
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Runtime duration
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Data Catalog object count and request volume
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Adjacent AWS service charges such as S3, Redshift, Athena, EMR, or DynamoDB exports
The most important planning variables are:
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How long jobs run
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How many DPUs or nodes they consume
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How heavily you use the Data Catalog
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Which downstream storage/query services are involved
Frequently Asked Questions
What are the main cost drivers for AWS Glue?
The primary AWS Glue cost drivers are DPU-hours for ETL jobs, interactive sessions, crawler executions, Data Catalog storage and request volume beyond the free tier, DataBrew usage if applicable, and any related AWS charges such as S3, Redshift, DynamoDB export, CloudWatch, and cross-region data transfer. For ETL specifically, cost is driven by DPU allocation, runtime, and execution class.
How can I reduce my AWS Glue ETL costs?
Enable Flex execution for non-urgent batch jobs (34% savings). Right-size worker counts—most jobs need 2-5 workers, not the default 10. Use job bookmarks to avoid reprocessing data. Convert source files to Parquet format. Set job timeouts and disable unused Development Endpoints.
What are the charges for the AWS Glue Data Catalog?
The Data Catalog provides 1 million free objects and 1 million free requests monthly. Beyond these limits, pricing is $1 per 100,000 objects stored and $1 per million requests. Most organizations stay within free tier limits unless managing very large data lakes.
How does Integrate.io's pricing compare to AWS Glue for ETL operations?
Integrate.io charges a fixed $1,999/month for unlimited data volumes, pipelines, and connectors. AWS Glue costs vary based on job frequency, duration, and worker allocation—moderate usage runs $800-2,000/month for compute alone, before factoring in management time.
