AI visibility report
Astronomer ranks #8 in Data Engineering & ETL/ELT Pipelines AI search.
Outside the top three on 24 of the 25 prompts buyers actually ask.
Integrate.io is cited on 14 of those losses.
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Start free trial#8 among 12 vendors · still absent from 91.3% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Grok
Google AI Mode
ChatGPT
Perplexity
Bing Copilot
Gemini SearchVisible, but narrative can improve. Astronomer ranks #8 on presence but #12 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
Where Astronomer is losing
Prompts where competitors are visible and Astronomer is not.
These prompt-level losses are the first prompts to track and repair.
Where Astronomer is winning
No clear strengths identified yet.
Where Astronomer is losing5
Which ELT platforms have the largest library of pre-built source connectors covering SaaS apps, databases, and event streams?
Competitors on 6 platforms
Track this promptI'm evaluating ETL platforms for a company starting its modern data stack — which tools are fastest to onboard and connect to a cloud warehouse?
Competitors on 5 platforms
Track this promptWhat are the easiest ELT tools to get data flowing from a SaaS CRM into a cloud data warehouse in under a day with no custom code?
Competitors on 5 platforms
Track this promptWhat ELT platforms give data engineers the best debugging experience when a pipeline fails mid-run with partial data loaded?
Competitors on 5 platforms
Track this promptWhat ELT platforms handle schema drift and evolving source schemas automatically without breaking existing pipelines?
Competitors on 5 platforms
Track this prompt
Track Astronomer daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Astronomer is a New York-based DataOps company and the primary commercial steward of Apache Airflow, the open-source workflow orchestration standard downloaded over 30 million times per month and used by 80,000+ organizations. Its flagship product, Astro, is a fully managed cloud platform that enables data engineering teams to build, deploy, observe, and govern data and AI pipelines at scale without managing Kubernetes infrastructure. Astro is available as a multi-tenant hosted service or as Astro Private Cloud for single-tenant deployments in a customer's own cloud environment. The company also develops Cosmos, a widely adopted open-source library for running dbt Core projects inside Airflow, and contributes the majority of Airflow's core engineering. Founded in 2015 and Series D-funded with $376M raised, Astronomer serves enterprises across financial services, gaming, retail, healthcare, and manufacturing.
Astro by Astronomer is a fully managed DataOps platform built on Apache Airflow that abstracts away infrastructure complexity, enabling data engineers to write DAGs and deploy pipelines with enterprise-grade observability, CI/CD integration, and AI-assisted operations. It includes Astro Private Cloud for regulated environments, the Cosmos dbt integration, an Airflow MCP server for agentic workflows, and a proprietary Astro Executor for reliability and concurrency.
Key Facts
- Founded
- 2015
- HQ
- New York, NY, USA
- Founders
- Greg Neiheisel, Ry Walker, Tim Brunk
- Employees
- 251-500
- Funding
- ~$376M
- Customers
- 800+
- Valuation
- ~$1.2B (as of 2022 Series C)
- Status
- Private (Series D)
Target users
Key Capabilities10
- Fully managed Apache Airflow orchestration (Astro cloud and Astro Private Cloud)
- Proprietary Astro Executor for higher concurrency and automatic failure recovery
- Deployments-as-Code with Terraform, CLI, and API support
- Native data observability, task-level lineage, and AI-powered root cause analysis
- Zero-downtime Airflow upgrades with 90-day rollback history
- Cosmos open-source tool for running dbt Core projects as Airflow DAGs
- Local development via Astro CLI and in-browser Astro IDE
- Airflow MCP server for agentic/AI pipeline control
- Multi-cloud deployment across AWS, GCP, and Azure
- Enterprise governance: RBAC, audit logging, SAML SSO, private networking
Key Use Cases7
- ETL/ELT pipeline orchestration at enterprise scale
- MLOps and AI pipeline management (model training, validation, deployment)
- LLMOps and generative AI data workflows
- Data observability and SLA monitoring
- Cloud migration from legacy schedulers (e.g., Oozie, Cron)
- Operational analytics and automated reporting pipelines
- Multi-team data platform governance and self-service pipeline deployment
Astronomer customer outcomes
50% reduction in troubleshooting time; 90% reduction in educator manual data analysis workload; 6M+ data transformations
Migrated from Google Cloud Composer to Astro, gaining full pipeline visibility and scaling AI-driven education insights. Engineers can now pinpoint and resolve failures in half the previous time.
9,000+ data assets centralized; data discovery reduced from days to minutes
Replaced a mix of self-hosted OSS Airflow and Luigi with Astro, centralizing data operations under a unified control plane and dramatically improving data discoverability.
Migration completed in ~12 weeks; supported data engineering teams scaled from 25 to 50+
Migrated business-critical workflows from Oozie to Airflow via Astronomer Professional Services, enabling self-service pipeline deployment and scaling across engineering teams.
Recent Trend
How AI describes Astronomer3
Apache Airflow / Astronomer (Task-Based Approach via Cosmos) ---------------------------------------------------------------- By default, classic Airflow forces you to run dbt using a heavy `BashOperator` . However, the ecosystem evolved to solve this...
Looking for an orchestration platform that integrates with my existing transformation layer — which tools support running SQL models as pipeline steps?
Apache Airflow (Astronomer): The industry standard for complex task coordination.
Which ETL platforms have strong SLAs and automatic retry logic so data teams get alerted before business stakeholders notice pipeline delays?
How it handles local dev: If you use Airflow, the Astro CLI (maintained by Astronomer) is the industry standard for local work.
Looking for a data orchestration platform with a great local development workflow — which tools let you test DAGs or workflows locally before deploying?
Most cited sources8
3Intro to Machine Learning Orchestration (& Use with Airflow)
astronomer.io·Product Page
- 2
Astronomer: The Best Place to Run Apache Airflow®
astronomer.io·Documentation
- 1
Data quality and Airflow | Astronomer Documentation
astronomer.io·Documentation
- L1
Apache Airflow vs Dagster vs Prefect: choosing a Python-native orchestration platform
llms.astronomer.io·Comparison
- 1
10 Best Practices for Modern Data Orchestration with Airflow
astronomer.io·Blog Post
- 1
Run Airflow locally
astronomer.io·Documentation
Alternatives in Data Engineering & ETL/ELT Pipelines6
Astronomer positions as the definitive enterprise-grade managed Apache Airflow platform, differentiating on open-source stewardship (18 of Airflow's top committers and 10 PMC members on staff), a purpose-built execution engine (Astro Executor), and a full DataOps lifecycle—from local development through CI/CD, deployment, observability, and AI-assisted root-cause analysis.
- Against cloud-native managed Airflow services (AWS MWAA, GCP Composer), Astronomer emphasizes faster version adoption, superior developer experience, and enterprise observability.
- Against code-first orchestrators such as Dagster and Prefect, it competes on Airflow's ecosystem breadth (1,200+ providers), open-source lock-in avoidance, and organizational familiarity.
- Its Cosmos open-source tool for dbt-in-Airflow further broadens appeal in the modern data stack.
Reviews
Praised
- Simplifies Airflow deployment and management
- Eliminates need for dedicated DevOps/infrastructure team
- Intuitive and clean UI
- Strong customer support and responsiveness
- CI/CD integration with GitHub and Bitbucket
- Auto-scaling and reliability
- Excellent documentation and onboarding
- Built-in observability and pipeline monitoring
Criticized
- High cost, especially for smaller teams
- Steep learning curve for advanced features
- Less customizable than self-hosted Airflow
- Vendor lock-in concerns
- Some features/operators missing or limited vs. OSS Airflow
- Limited DAG run filtering (e.g., by logical date or user)
- Incomplete infrastructure-as-code (IAC) controls for user access
- Alert functionality less effective in some configurations
Users on G2 (4.5/5, 136 reviews) consistently praise Astro for simplifying Airflow management, eliminating the need for dedicated infrastructure/DevOps resources, and delivering strong customer support and documentation. The intuitive UI, CI/CD integrations with GitHub/Bitbucket, and automatic scaling are frequently highlighted. Criticisms center on pricing being high for smaller teams, less flexibility than self-hosted Airflow, occasional discrepancies between local and managed environments, and a steep learning curve for advanced features.
Pricing
Astro uses consumption-based pricing measured in Astro Units (AU), with four tiers: Developer (pay-as-you-go, free 14-day trial, smallest deployments from $0.35/hr), Team (pay-as-you-go or annual, dedicated clusters from $2.40/hr, includes private networking and audit logging), Business, and Enterprise (custom agreements, remote execution agents, SAML SSO). Buyers can subscribe via AWS, Azure, or GCP marketplaces. Self-hosted Astronomer Software starts around $25,000–$50,000/year for small deployments. Annual commitments typically yield 15-25% discounts; mid-market Astro deployments commonly run $30,000–$80,000/year. Enterprise support and professional services packages range from $15,000 to $100,000+.
Limitations
- Pricing can be prohibitive for smaller teams, with minimum monthly commitments and cloud networking pass-through costs adding 10-20% above base AU rates.
- The managed platform is less customizable than self-hosted Airflow, with some operators and configurations unavailable or behaving differently in the hosted environment.
- New users face a steep learning curve for advanced features such as worker queues, Kubernetes Executor billing, and infrastructure-as-code workflows.
- Some reviewers cite limited granularity in DAG run filtering and incomplete IAC controls for user access.
- Vendor lock-in concerns arise from dependency on Astronomer's proprietary executor and ecosystem tooling.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Bing Copilot | Perplexity | Google AI Mode | Gemini Search | ChatGPT | Grok |
|---|---|---|---|---|---|---|
Capability2/5 cited (40%) | ||||||
Which data orchestration tools support complex multi-step pipelines with branching logic, sensors, and cross-team dependencies? | ||||||
I need a reverse ETL tool to sync data warehouse segments back to a CRM and ad platforms — which platforms do this best? | ||||||
Which data pipeline tools support real-time streaming ingestion alongside batch loads from the same platform? | ||||||
What ETL platforms have built-in data quality checks and can alert the team when row counts or null rates deviate from expected ranges? | ||||||
What ELT platforms handle schema drift and evolving source schemas automatically without breaking existing pipelines? | ||||||
Developer Experience1/5 cited (20%) | ||||||
Looking for a data orchestration platform with a great local development workflow — which tools let you test DAGs or workflows locally before deploying? | ||||||
Which data pipeline tools offer code-first transformation layers that data engineers can version-control and test like software? | ||||||
What ELT platforms give data engineers the best debugging experience when a pipeline fails mid-run with partial data loaded? | ||||||
What ETL platforms do analytics engineers prefer when they want SQL-based transformations with testing and documentation built in? | ||||||
Which data pipeline tools have the best observability and data lineage views so you can trace where a bad value came from? | ||||||
Integrations & Ecosystem2/5 cited (40%) | ||||||
What data pipeline tools integrate natively with major cloud data warehouses for automatic schema management and optimized load performance? | ||||||
Which ETL tools have an open API and SDK so we can build custom connectors for internal data sources quickly? | ||||||
Which ELT platforms have the largest library of pre-built source connectors covering SaaS apps, databases, and event streams? | ||||||
Looking for an orchestration platform that integrates with my existing transformation layer — which tools support running SQL models as pipeline steps? | ||||||
What data engineering platforms work well in a multi-cloud setup where sources span one cloud and the warehouse is on another? | ||||||
Performance & Reliability3/5 cited (60%) | ||||||
Which ETL platforms have strong SLAs and automatic retry logic so data teams get alerted before business stakeholders notice pipeline delays? | ||||||
What data orchestration tools scale reliably to thousands of concurrent tasks without degrading scheduler performance? | ||||||
Which ELT platforms can sync billions of rows per day from a high-volume transactional database without impacting source system performance? | ||||||
What data pipeline tools handle late-arriving data and backfilling years of historical records reliably without manual intervention? | ||||||
Which ELT platforms maintain low-latency incremental syncs so dashboards reflect source data within minutes rather than hours? | ||||||
Setup & First Run2/5 cited (40%) | ||||||
I'm evaluating ETL platforms for a company starting its modern data stack — which tools are fastest to onboard and connect to a cloud warehouse? | ||||||
What are the easiest ELT tools to get data flowing from a SaaS CRM into a cloud data warehouse in under a day with no custom code? | ||||||
What data orchestration tools have the best getting-started experience for a data engineer moving from manually scheduled SQL scripts? | ||||||
Which data pipeline platforms can a small data team of 2 get running with managed connectors for 20+ sources without building custom integrations? | ||||||
Which open-source ETL tools can be self-hosted on a single VM and are easy to configure without deep infrastructure knowledge? | ||||||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Airbyte | 38.0% | 18.6% | 6.7% | 4.7% | 33.3% | #19.1 | +0.31 |
| 2 | Integrate.io | 35.3% | 18.1% | 0.0% | 34.0% | 31.3% | #23.1 | +0.30 |
| 3 | Fivetran | 24.0% | 18.1% | 8.7% | 9.3% | 22.7% | #32.5 | +0.26 |
| 4 | Dagster | 21.3% | 12.1% | 4.0% | 6.0% | 13.3% | #26.6 | +0.30 |
| 5 | Matillion | 20.7% | 7.9% | 2.7% | 0.0% | 16.7% | #22.6 | +0.24 |
| 6 | Hevo Data | 20.0% | 6.9% | 1.3% | 2.7% | 18.0% | #17.3 | +0.42 |
| 7 | dbt | 14.7% | 6.6% | 2.0% | 10.7% | 14.0% | #22.6 | +0.27 |
| 8 | Astronomer | 8.7% | 2.8% | 4.7% | 2.7% | 6.7% | #33.5 | +0.22 |
| 9 | Meltano | 7.3% | 4.9% | 2.0% | 3.3% | 7.3% | #28.6 | +0.48 |
| 10 | Rivery | 6.0% | 1.4% | 0.0% | 2.0% | 6.0% | #16.6 | +0.37 |
| 11 | Hightouch | 2.7% | 2.1% | 0.7% | 2.0% | 2.7% | #30.6 | +0.40 |
| 12 | Census | 2.0% | 0.4% | 0.0% | 0.0% | 2.0% | #38.7 | +0.30 |
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