Decision comparison
Astronomer vs Fivetran
Astronomer and Fivetran solve fundamentally different problems in the data pipeline space. Astronomer excels at orchestrating complex, code-driven workflows using Apache Airflow, while Fivetran dominates automated, no-code data ingestion with its massive connector library. Many data teams use both tools together, with Fivetran handling extraction and loading while Astronomer orchestrates the broader pipeline.
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 | Astronomer | Fivetran |
|---|---|---|
| Best For | Data engineers building complex orchestration workflows with Apache Airflow | Teams needing automated, no-code data ingestion from hundreds of sources |
| Architecture | Managed Airflow platform with Astro Engine, Kubernetes-based execution | Fully managed ELT platform with 700+ pre-built connectors |
| Pricing Model | Developer tier free, usage-based pricing with rates including $0.00, $0.13, $0.35, $0.42, $2.40 | Fivetran is consumption-priced on monthly active rows and quoted through its own estimator. The plans are Free, Standard, Enterprise and Business Critical. The Free plan covers up to 500,000 monthly active rows for connections, 3,500 for activations and 5,000 model runs. Fivetran advertises savings of up to 22% on an annual contract. No per-plan price is published. |
| Ease of Use | Requires Python and DAG knowledge; powerful CLI and browser-based IDE | No-code setup; pipelines deployable in minutes with automatic schema management |
| Scalability | Elastic auto-scaling workers, multi-AZ deployments, 2.5x concurrency vs alternatives | 500+ GB/hr throughput; 9.1+ petabytes synced monthly across customer base |
| Community/Support | 9/10 rating on TrustRadius; 1-hour support SLA; backed by Apache Airflow community | 8.4/10 rating on TrustRadius; 54 reviews; extensive documentation and REST API |
Astronomer
- Best For:
- Data engineers building complex orchestration workflows with Apache Airflow
- Architecture:
- Managed Airflow platform with Astro Engine, Kubernetes-based execution
- Pricing Model:
- Developer tier free, usage-based pricing with rates including $0.00, $0.13, $0.35, $0.42, $2.40
- Ease of Use:
- Requires Python and DAG knowledge; powerful CLI and browser-based IDE
- Scalability:
- Elastic auto-scaling workers, multi-AZ deployments, 2.5x concurrency vs alternatives
- Community/Support:
- 9/10 rating on TrustRadius; 1-hour support SLA; backed by Apache Airflow community
Fivetran
- Best For:
- Teams needing automated, no-code data ingestion from hundreds of sources
- Architecture:
- Fully managed ELT platform with 700+ pre-built connectors
- Pricing Model:
- Fivetran is consumption-priced on monthly active rows and quoted through its own estimator. The plans are Free, Standard, Enterprise and Business Critical. The Free plan covers up to 500,000 monthly active rows for connections, 3,500 for activations and 5,000 model runs. Fivetran advertises savings of up to 22% on an annual contract. No per-plan price is published.
- Ease of Use:
- No-code setup; pipelines deployable in minutes with automatic schema management
- Scalability:
- 500+ GB/hr throughput; 9.1+ petabytes synced monthly across customer base
- Community/Support:
- 8.4/10 rating on TrustRadius; 54 reviews; extensive documentation and REST API
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 | Astronomer | Fivetran |
|---|---|---|
| GitHub commits, 90d(Developer adoption) | 60 | 19 |
| GitHub stars(Developer adoption) | 1,000+ | 134 |
| Search interest(Market interest) | 0 | 1 |
| Product Hunt comments(Community interest) | 0 | 9 |
| Product Hunt rating(Community interest) | Unavailable | 5.0/5 |
| Product Hunt reviews(Community interest) | 0 | 1 |
| Product Hunt votes(Community interest) | 6 | 85 |
| PyPI weekly downloads(Ecosystem adoption) | 1.8M | Not available |
| Stack Overflow questions(Community interest) | 25 | 22 |
| Hacker News mentions, 90d(Community interest) | Not available | 3 |
| PyPI weekly downloads(Developer adoption) | Not available | 29.0k |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Astronomer
September 21, 2026Package vulnerabilities
PyPI · apache-airflow@3.3.2
0 vulnerabilities
across 1 package
Repository security score
Not available
Fivetran
September 21, 2026Package vulnerabilities
PyPI · fivetran-connector-sdk@2.12.1
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
Astronomer
Feature Comparison
| Feature | Astronomer | Fivetran |
|---|---|---|
| Data Movement & Ingestion | ||
| Pre-built Connectors | Not available natively; relies on Airflow providers and custom code | 700+ fully managed connectors for SaaS, databases, and files |
| Change Data Capture (CDC) | Not available as built-in feature; requires custom DAG implementation | Log-based replication for efficient, low-impact database syncs |
| Automatic Schema Management | Not available; schema handling is manual within DAG code | Automatic schema evolution with 22.2M+ changes handled monthly |
| Reverse ETL (rELT) | Not available as a built-in feature | Sync enriched data from warehouse back into business applications |
| Orchestration & Workflow Management | ||
| DAG-Based Workflow Orchestration | Full Apache Airflow DAG support with Python-based pipeline authoring | Not available; focused on connector-driven pipelines, not arbitrary workflows |
| Pipeline Lineage | Task-level lineage tracing upstream and downstream dependencies | Not available as a standalone lineage feature |
| dbt Integration | Native dbt orchestration turning dbt projects into DAGs | Built-in dbt Core integration with Quickstart data models |
| Custom Code Execution | Full Python support; run any operator, sensor, or custom logic | Connector SDK for building custom connectors to niche sources |
| Infrastructure & Operations | ||
| Deployment Model | Managed cloud, private cloud, and deployments-as-code via Terraform | Fully managed SaaS with hybrid deployment option available |
| Auto-Scaling | Elastic auto-scaling workers based on task queue depth | Automatic scaling handled transparently by the managed platform |
| Disaster Recovery | One-click cross-region failover with automatic data replication | Idempotent pipelines restart from last successful state |
| High Availability | Multi-AZ deployments with automatic failover and 99.5% uptime SLA | 99.97% uptime with fully managed infrastructure |
| Security & Compliance | ||
| Compliance Certifications | SOC 2 Type II, HIPAA, SSO/SCIM, and RBAC | SOC 1 and SOC 2, GDPR, HIPAA BAA, ISO 27001, PCI DSS Level 1, HITRUST |
| Encryption & Networking | Network isolation with dedicated clusters and air-gapped support | SSH tunnels, VPN tunnels, customer-managed keys, private networking |
| Access Controls | SAML-based SSO, SCIM, and role-based access control | Role-based access control with custom roles on Enterprise tier |
| Observability & AI | ||
| Data Quality Monitoring | Built-in checks for volume, completeness, schema consistency | Reliability dashboards with logs and alerts for sync health |
| AI-Powered Features | Airflow AI Assistant, AI-powered root cause analysis agent | Not available as a distinct AI feature set |
| Data Product SLAs | Set freshness targets, track performance, alert before deadlines | Not available as a built-in SLA management feature |
Data Movement & Ingestion
Pre-built Connectors
Change Data Capture (CDC)
Automatic Schema Management
Reverse ETL (rELT)
Orchestration & Workflow Management
DAG-Based Workflow Orchestration
Pipeline Lineage
dbt Integration
Custom Code Execution
Infrastructure & Operations
Deployment Model
Auto-Scaling
Disaster Recovery
High Availability
Security & Compliance
Compliance Certifications
Encryption & Networking
Access Controls
Observability & AI
Data Quality Monitoring
AI-Powered Features
Data Product SLAs
How they fit together
Astronomer and Fivetran solve fundamentally different problems in the data pipeline space. Astronomer excels at orchestrating complex, code-driven workflows using Apache Airflow, while Fivetran dominates automated, no-code data ingestion with its massive connector library. Many data teams use both tools together, with Fivetran handling extraction and loading while Astronomer orchestrates the broader pipeline.
What each one handles
Use Astronomer for:
Choose Astronomer when you need full workflow orchestration with Python-based DAGs, complex multi-step pipelines, custom logic execution, and deep observability across your entire data platform.
Use Fivetran for:
Choose Fivetran when your primary need is reliable, automated data ingestion from SaaS applications and databases into your warehouse, with minimal engineering effort and fast time-to-value.
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 Astronomer and Fivetran be used together?
Yes, and this is a common pattern. Fivetran handles automated data extraction and loading from hundreds of sources, while Astronomer orchestrates the broader pipeline including transformations, custom logic, and cross-system dependencies. Astronomer can trigger and monitor Fivetran syncs as part of a larger DAG workflow.
Which tool is better for teams without strong Python skills?
Fivetran is the clear choice for teams without deep coding expertise. Its no-code interface allows analysts and less technical users to set up data pipelines in minutes. Astronomer requires Python proficiency to author Airflow DAGs and is designed primarily for data engineers.
How do the pricing models compare between Astronomer and Fivetran?
Astronomer uses usage-based pricing tied to compute resources consumed. Its Developer plan offers a free trial, with deployments starting at $0.35/hr and scale-to-zero compute. Fivetran uses a monthly active rows (MAR) model with a free tier of 500,000 MAR, a Standard tier, and Enterprise tiers with custom pricing. Both offer free starting points for small teams.
Which tool offers better security and compliance coverage?
Both tools provide enterprise-grade security. Fivetran holds a broader set of certifications including SOC 1, SOC 2, GDPR, HIPAA BAA, ISO 27001, PCI DSS Level 1, and HITRUST. Astronomer covers SOC 2 Type II, HIPAA, and SSO/SCIM. Fivetran also offers hybrid deployment for data that cannot leave your environment.