Decision comparison
Apache Airflow vs CloudQuery
These two tools solve different problems, so most teams are not choosing one instead of the other. Apache Airflow is the better fit for general-purpose data pipeline orchestration: complex schedules, task dependencies, retries and multi-step workflows across many data systems. CloudQuery is the better fit for cloud infrastructure visibility: asset inventory, compliance monitoring, security posture and cost data pulled from cloud APIs into a SQL database. We compared them on primary use case, setup effort, integration coverage, pricing model and support options, using each project's documentation, pricing page and GitHub data. The non-obvious point is that they combine well: Airflow can schedule CloudQuery syncs and run the downstream SQL checks, so a team that already runs Airflow gains cloud inventory without writing extraction code.
Used together. These are normally used together rather than chosen between. The comparison explains what each one does in the stack.
These are different kinds of product — Workflow Orchestrator and ELT Platform.
Quick Comparison
| Decision factor | Apache Airflow | CloudQuery |
|---|---|---|
| Ease of Setup | Requires configuring DAGs, metadata database, executor, and webserver — significant initial effort for production deployments | Quick CLI-based setup with declarative config files — connect cloud accounts and start syncing data in minutes |
| Primary Use Case | General-purpose workflow orchestration for scheduling and monitoring complex multi-step data pipelines | Cloud asset inventory and ELT framework specializing in extracting infrastructure, security, and compliance data |
| Scalability | Highly scalable modular architecture using message queues to orchestrate arbitrary numbers of distributed workers | Scales horizontally across cloud accounts and regions with parallel syncs and incremental data extraction |
| Integration Ecosystem | Provider packages with ready-made operators and hooks for AWS, Google Cloud, Microsoft Azure, databases and SaaS platforms. Any system with a Python client can be reached by writing a custom operator. | Source plugins for AWS, GCP, Azure, Kubernetes and security and FinOps tools; the pricing page lists 50+ cloud, security, and FinOps sources on the Free plan. Missing sources can be built with the CloudQuery SDK so they share the same schema. |
| Learning Curve | Steep learning curve requiring solid Python skills and understanding of DAG concepts and Airflow internals | Lower barrier to entry with SQL-based querying, natural language search, and declarative configuration approach |
| Community & Support | Apache Software Foundation project with 47,000+ GitHub stars, an active Slack workspace and mailing lists. Support is community-based for the open-source release; paid support comes from managed providers such as Astronomer or Amazon MWAA. | Open-source CLI with 6,500+ GitHub stars, written in Go and released often (CLI v6.49.2 shipped in October 2026). Paid support plans (Silver, Gold, Platinum) set initial response targets from under 48 hours down to 1 hour for business-critical issues, with 24/7 coverage on Platinum. |
| Pricing | Free and open-source under the Apache License 2.0 | The CloudQuery CLI is open source under MPL 2.0 and free to self-host. The hosted platform bills on usage, which CloudQuery describes as priced solely on what you consume; no rate is published, and a free trial is offered. |
Apache Airflow
- Ease of Setup:
- Requires configuring DAGs, metadata database, executor, and webserver — significant initial effort for production deployments
- Primary Use Case:
- General-purpose workflow orchestration for scheduling and monitoring complex multi-step data pipelines
- Scalability:
- Highly scalable modular architecture using message queues to orchestrate arbitrary numbers of distributed workers
- Integration Ecosystem:
- Provider packages with ready-made operators and hooks for AWS, Google Cloud, Microsoft Azure, databases and SaaS platforms. Any system with a Python client can be reached by writing a custom operator.
- Learning Curve:
- Steep learning curve requiring solid Python skills and understanding of DAG concepts and Airflow internals
- Community & Support:
- Apache Software Foundation project with 47,000+ GitHub stars, an active Slack workspace and mailing lists. Support is community-based for the open-source release; paid support comes from managed providers such as Astronomer or Amazon MWAA.
- Pricing:
- Free and open-source under the Apache License 2.0
CloudQuery
- Ease of Setup:
- Quick CLI-based setup with declarative config files — connect cloud accounts and start syncing data in minutes
- Primary Use Case:
- Cloud asset inventory and ELT framework specializing in extracting infrastructure, security, and compliance data
- Scalability:
- Scales horizontally across cloud accounts and regions with parallel syncs and incremental data extraction
- Integration Ecosystem:
- Source plugins for AWS, GCP, Azure, Kubernetes and security and FinOps tools; the pricing page lists 50+ cloud, security, and FinOps sources on the Free plan. Missing sources can be built with the CloudQuery SDK so they share the same schema.
- Learning Curve:
- Lower barrier to entry with SQL-based querying, natural language search, and declarative configuration approach
- Community & Support:
- Open-source CLI with 6,500+ GitHub stars, written in Go and released often (CLI v6.49.2 shipped in October 2026). Paid support plans (Silver, Gold, Platinum) set initial response targets from under 48 hours down to 1 hour for business-critical issues, with 24/7 coverage on Platinum.
- Pricing:
- The CloudQuery CLI is open source under MPL 2.0 and free to self-host. The hosted platform bills on usage, which CloudQuery describes as priced solely on what you consume; no rate is published, and a free trial is offered.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Apache Airflow | CloudQuery |
|---|---|---|
| Docker Hub pulls(Product adoption) | 1.7B | Not available |
| GitHub commits, 90d(Product adoption) | 2.7k | 467 |
| GitHub stars(Product adoption) | 47,000+ | 6,500+ |
| Search interest(Market interest) | 1 | Unavailable |
| Hacker News mentions, 90d(Community interest) | 2 | Not available |
| PyPI weekly downloads(Product adoption) | 1.5M | Not available |
| Stack Overflow questions(Community interest) | 10.6k | Not available |
| Product Hunt comments(Community interest) | Not available | 1 |
| Product Hunt reviews(Community interest) | Not available | 0 |
| Product Hunt votes(Community interest) | Not available | 7 |
As of November 9, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Apache Airflow
November 9, 2026Package vulnerabilities
PyPI · apache-airflow@3.4.0
0 vulnerabilities
across 1 package
Repository security score
github.com/apache/airflow
7.6/10
CloudQuery
November 9, 2026Package vulnerabilities
Not available
Repository security score
github.com/cloudquery/cloudquery
5.3/10
Interface Preview
Apache Airflow
CloudQuery
Feature Comparison
| Feature | Apache Airflow | CloudQuery |
|---|---|---|
| Core Architecture | ||
| Programming Language | Python | Go |
| Configuration Approach | Python DAG code | Declarative YAML + SQL |
| Web UI Dashboard | Web UI for DAG runs, task status and logs | Platform UI with saved queries and dashboards |
| Query Interface | Not a query tool; tasks call other systems | Full SQL plus natural-language AI search |
| Data Pipeline Capabilities | ||
| Workflow Scheduling | Cron, timetables and data-aware scheduling across DAG tasks | Sync runs on a schedule; no task dependency graph |
| Cloud API Extraction | Via provider operators and custom Python | Native source plugins with incremental sync |
| Custom Transformations | Any Python code or triggered SQL/dbt tasks | SQL in the destination database |
| Infrastructure & Deployment | ||
| Self-Hosted Option | Full support | Yes (CLI) |
| Managed Cloud Offering | Via Astronomer/MWAA | CloudQuery Platform |
| Kubernetes Support | KubernetesExecutor | K8s source plugin |
| Paid Plan Pricing | $0 software; pay for infrastructure or a managed service | Free plan $0 up to 1M rows; Enterprise custom quote |
| Security & Compliance | ||
| Compliance Monitoring | Not built-in | Continuous |
| Security Posture Assessment | Not built-in | Built-in CSPM |
| Role-Based Access Control | Full support | Full support |
| Licensing & Ecosystem | ||
| License | Apache-2.0 | MPL-2.0 |
| GitHub Stars | 47,000+ | 6,500+ |
| Source Connectors | Provider packages with operators and hooks for major clouds, databases and SaaS | 50+ cloud, security, and FinOps sources |
Core Architecture
Programming Language
Configuration Approach
Web UI Dashboard
Query Interface
Data Pipeline Capabilities
Workflow Scheduling
Cloud API Extraction
Custom Transformations
Infrastructure & Deployment
Self-Hosted Option
Managed Cloud Offering
Kubernetes Support
Paid Plan Pricing
Security & Compliance
Compliance Monitoring
Security Posture Assessment
Role-Based Access Control
Licensing & Ecosystem
License
GitHub Stars
Source Connectors
How they fit together
These two tools solve different problems, so most teams are not choosing one instead of the other. Apache Airflow is the better fit for general-purpose data pipeline orchestration: complex schedules, task dependencies, retries and multi-step workflows across many data systems. CloudQuery is the better fit for cloud infrastructure visibility: asset inventory, compliance monitoring, security posture and cost data pulled from cloud APIs into a SQL database. We compared them on primary use case, setup effort, integration coverage, pricing model and support options, using each project's documentation, pricing page and GitHub data. The non-obvious point is that they combine well: Airflow can schedule CloudQuery syncs and run the downstream SQL checks, so a team that already runs Airflow gains cloud inventory without writing extraction code.
What each one handles
Use Apache Airflow for:
Choose Apache Airflow if you are a data engineering team that needs a general-purpose workflow orchestrator. It schedules multi-step ETL and ELT pipelines with task dependencies, retries, alerting and backfills, and it reaches databases, cloud services and APIs through provider packages or custom Python operators. Because DAGs are Python code, you can generate pipelines dynamically and keep them in version control. The cost is operational: you run a scheduler, webserver, metadata database and workers, or pay a managed service. Airflow loses when the job is cloud inventory or compliance: it has no built-in asset discovery, normalized cloud schema or security checks, so you would rebuild what CloudQuery ships.
Use CloudQuery for:
Choose CloudQuery if you are a platform engineering, DevOps or security team that needs one inventory of multi-cloud infrastructure. It syncs resources from AWS, GCP, Azure, Kubernetes and security and FinOps tools into a database you already run, such as Postgres, Snowflake or BigQuery, and supports continuous compliance monitoring, security posture assessment and audit-ready reports. Configuration is declarative YAML, and querying is SQL or natural language, so less Python skill is needed. The Free plan covers up to 1M rows; beyond that, Enterprise pricing is a custom quote. CloudQuery loses when you need general orchestration: it runs syncs, not dependency graphs of arbitrary tasks, so pair it with a scheduler for wider workflows.
These roles reflect the available product evidence. Most teams run both; which one owns a given job depends on your stack and team.
Frequently Asked Questions
Can Apache Airflow and CloudQuery be used together?
Yes, Apache Airflow and CloudQuery complement each other well in a modern data stack. You can use Airflow as the orchestration layer to schedule and trigger CloudQuery syncs on a regular cadence, ensuring your cloud asset inventory stays fresh. Airflow handles the workflow scheduling, dependency management, and alerting, while CloudQuery handles the specialized extraction of cloud infrastructure data. This combination is particularly powerful for teams that already run Airflow for their data pipelines and want to add cloud visibility without building custom extraction logic.
Which tool is better for cloud compliance and security monitoring?
CloudQuery is the clear winner for cloud compliance and security monitoring. It was purpose-built for this use case with native support for continuous compliance monitoring, security posture assessment (CSPM), and audit-ready reporting across AWS, GCP, Azure, and 50+ cloud, security, and FinOps sources. Because the data lands in your own database, compliance checks are SQL queries you can run across every account. Apache Airflow can orchestrate compliance workflows, but it does not include built-in compliance scanning, asset discovery, or security assessment capabilities — you would need to build all of that logic yourself using custom operators.
What are the main differences in pricing between Apache Airflow and CloudQuery?
Apache Airflow is free and open-source under the Apache License 2.0, so the software is $0; you pay for the servers that run it or for a managed service such as Astronomer or Amazon MWAA. CloudQuery's CLI is open source under MPL 2.0 and free to self-host. Its hosted platform has a Free plan at $0 with up to 1M rows and an Enterprise plan with custom pricing above 1M rows, billed on rows synced per year; no per-row rate is published. Paid support plans (Silver, Gold, Platinum) add response targets down to 1 hour for business-critical issues. In short, Airflow's costs grow with compute and operations time, while CloudQuery's grow with the volume of data you sync.
How do the communities and integrations of Apache Airflow and CloudQuery differ?
Apache Airflow is an Apache Software Foundation project with 47,000+ GitHub stars, an active Slack workspace and provider packages that cover major clouds, databases and SaaS platforms. CloudQuery has 6,500+ GitHub stars and focuses its integrations on cloud infrastructure: its pricing page lists 50+ cloud, security, and FinOps sources, and its SDK lets you build missing sources that join the same schema. If you need to connect many kinds of data systems, Airflow covers more ground. If you need detailed cloud asset, security and cost data, CloudQuery's source plugins return it already normalized.
How hard is it to set up each tool?
CloudQuery installs with a script, a precompiled binary, the official Docker image or Homebrew, and a sync needs only a YAML file naming the source and destination. Apache Airflow needs a metadata database, an executor, a scheduler and a webserver before the first DAG runs, and production setups add workers and monitoring. A managed service such as Astronomer or Amazon MWAA removes most of that setup work.