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Head-to-head · DataOps Platforms

Apache Hop vs Kestra

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Apache Hop #3 in DataOps Platforms 9.0/10 Free plan Free plan✓ 0 of 4 features Visit Apache Hop
Kestra #6 in DataOps Platforms 8.7/10 Free plan Free plan✓ 0 of 4 features Visit Kestra

Apache Hop leads on 0 checks, Kestra on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreApache Hop · 9.0/10
  • Free planboth

Apache Hop scores higher on our rubric for dataops platforms: 9.0 against 8.7 out of 10; our editors rank them #3 and #6.

Apache Hop is the better fit for teams building visual ETL and data pipelines. Kestra is the better fit for teams wanting visual workflows and approval gates.

  • Apache Hop fits best

    Teams building visual ETL and data pipelines

  • Kestra fits best

    Teams wanting visual workflows and approval gates

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Side by side

Feature Apache Hop 9.0/10 Visit ↗ Kestra 8.7/10 Visit ↗
At a glance
Editor score 9.0 8.7
Ranking #3 in DataOps Platforms #6 in DataOps Platforms
Best for Teams building visual ETL and data pipelines Teams wanting visual workflows and approval gates
Pricing model Free Free
Starting price Free plan Free
Free plan ✓ ✓
Free trial — —
Deployment Cloud, Self-hosted, Desktop Cloud, Self-hosted
Platforms Windows, macOS, Linux Web
Support Community, Docs Email, Community, Docs · 24/7
Integrations 20 integrations 2,000+ integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Apache Hop 0/4 · Kestra 0/4
Workflow scheduling Not checked yet Not checked yet
Pipeline monitoring Not checked yet Not checked yet
Data quality checks Not checked yet Not checked yet
Version control Not checked yet Not checked yet
Specs
Deployment options Not checked yet Not checked yet
Supported integrations Not checked yet Not checked yet
Our review
Pros
  • 250+ transforms, 80+ workflow actions, and metadata-driven design
  • Runs locally, on servers, containers, Spark, Flink, and Dataflow
  • Supports CDC plus 40+ database dialects and named warehouse connectors
  • Choose a no-code editor or declarative YAML workflow definitions.
  • Use schedules, webhooks, polling, realtime, flow, and MCP triggers.
  • Track executions with logs, metrics, retries, replay, and approval gates.
Cons
  • Deployment spans several runtimes, adding architecture decisions
  • Scripting transforms require JavaScript, Java, Groovy, or Python skills
  • Support channels are centered on community resources and documentation
  • YAML definitions may be less approachable for teams avoiding configuration files.
  • Enterprise Edition and Kestra Cloud pricing details require a vendor conversation.
  • Deployment and governance options vary across the three offerings.
Our verdict

Apache Hop is an open-source data orchestration, engineering, integration, and ETL platform from the Apache Software Foundation. Its visual environment is designed for data engineers and teams that need repeatable pipelines and workflows…

Read the review →

Kestra orchestrates workflows for data engineers, software engineers, and platform teams. Flows can be created in a no-code editor or defined declaratively in YAML, then run across languages and infrastructure. The Open Source plan…

Read the review →
  1. Apache HopDataOps Platforms 9.0Free plan
  2. KestraDataOps Platforms 8.7Free plan

Strengths and trade-offs

  • Apache Hop — where it wins

    • 250+ transforms, 80+ workflow actions, and metadata-driven design
    • Runs locally, on servers, containers, Spark, Flink, and Dataflow
    • Supports CDC plus 40+ database dialects and named warehouse connectors

    Where it doesn't

    • Deployment spans several runtimes, adding architecture decisions
    • Scripting transforms require JavaScript, Java, Groovy, or Python skills
    • Support channels are centered on community resources and documentation

  • Kestra — where it wins

    • Choose a no-code editor or declarative YAML workflow definitions.
    • Use schedules, webhooks, polling, realtime, flow, and MCP triggers.
    • Track executions with logs, metrics, retries, replay, and approval gates.

    Where it doesn't

    • YAML definitions may be less approachable for teams avoiding configuration files.
    • Enterprise Edition and Kestra Cloud pricing details require a vendor conversation.
    • Deployment and governance options vary across the three offerings.

More comparisons

All DataOps platforms comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update