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Databases · head to head

Apache Airflow vs Traceloop

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Traceloop logo

Traceloop

Logging

LLM reliability platform with open-source observability and evaluation

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Airflow self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job; Traceloop free tier limited to 50k spans per month and 24-hour retention, restricting production use
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Traceloop covers Open-source SDK (OpenLLMetry).
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which Apache Airflow and Traceloop actually diverge.

Attributes where Apache Airflow and Traceloop differ
AttributeApache AirflowTraceloop
Pricing modelOpen source, no licence fee; managed services billed separatelyFreemium with pay-as-you-go enterprise option
PlatformsLinux, Docker, Kubernetes, Self-hostedCloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby
CategoryDatabasesLogging

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Airflow

  • Pipelines as Python
  • Web UI
  • Cloud provider packages
  • Jinja templating
  • Retries and dependencies
  • Extensible operators

Only in Traceloop

  • Open-source SDK (OpenLLMetry)
  • Multi-provider support
  • Observability platform integration
  • Framework support
  • Monitoring dashboard
  • Evaluation system
  • Deployment flexibility

What people use each for

The jobs each tool is most often brought in to do.

Apache Airflow

  • Scheduling nightly ETL where step order and retries matternot Traceloop
  • Coordinating machine learning training and evaluation runsnot Traceloop
  • Orchestrating dbt runs alongside extraction and loadingnot Traceloop
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot Traceloop

Traceloop

  • Monitoring LLM application performance in productionnot Apache Airflow
  • Instrumenting LLM apps with minimal code overheadnot Apache Airflow
  • Continuous evaluation and quality scoring of LLM outputsnot Apache Airflow
  • Debugging LLM application issues with full trace visibilitynot Apache Airflow
  • Integrating observability data into existing monitoring stacksnot Apache Airflow

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Airflow

  • Self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job
  • Built for scheduled batch work, and a poor fit for event-driven or sub-minute latency pipelines
  • Because DAGs are Python that the scheduler parses continuously, expensive top-level code in a DAG file slows the whole scheduler
  • Local development and testing of DAGs is awkward compared with newer orchestrators designed with it in mind

Traceloop

  • Free tier limited to 50k spans per month and 24-hour retention, restricting production use
  • Company acquisition by ServiceNow creates uncertainty about future roadmap
  • Requires integration with separate observability platforms for visualization
  • Less feature-rich than dedicated LLM evaluation platforms

Pricing, plan by plan

Apache Airflow

Free
  • Apache AirflowFree
    • Full scheduler and web UI
    • All provider packages
    • No task or DAG limits

Traceloop

Free
  • FreeFree
    • 50,000 spans per month
    • Up to 5 seats
    • 24-hour data retention
  • Enterprise$undefined/custom
    • Unlimited spans per month
    • Unlimited seats
    • Custom data retention

Which should you pick?

Choose Apache Airflow if

  • You need pipelines as python.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want web ui.
Apache Airflow review

Choose Traceloop if

  • You need open-source sdk (openllmetry).
  • You want to start without paying.
  • You work on Cloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby.
  • You also want multi-provider support.
Traceloop review

Questions people ask

Is Apache Airflow or Traceloop better?
Neither clearly leads. Apache Airflow starts at Free and Traceloop at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Traceloop?
Apache Airflow starts at Free and Traceloop at Free.
Does Apache Airflow or Traceloop run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Traceloop runs on Cloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby.
Can I use Apache Airflow for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Airflow best used for?
Apache Airflow is most often used for scheduling nightly etl where step order and retries matter, coordinating machine learning training and evaluation runs, orchestrating dbt runs alongside extraction and loading, replacing a sprawl of cron jobs with dependencies and visible run history. Of those, scheduling nightly etl where step order and retries matter and coordinating machine learning training and evaluation runs are not what Traceloop is typically brought in for.
What can Apache Airflow do that Traceloop cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Traceloop covers Open-source SDK (OpenLLMetry), Multi-provider support, Observability platform integration, Framework support.

Answered from the vendors’ own pages

Apache Airflow: Is Apache Airflow free?

Yes. Airflow is open source under the Apache Software Foundation with no licence fee. Costs are the infrastructure to run it, or a managed service such as Google Cloud Composer or Amazon MWAA.

Traceloop: Is OpenLLMetry open-source?

Yes, OpenLLMetry is Traceloop's open-source SDK built on OpenTelemetry standards. It allows teams to instrument LLM applications with just 2 lines of code and send data to 25+ observability platforms.

Source
Apache Airflow: What language are Airflow workflows written in?

Python. A workflow is a Python file, so standard language features including loops and datetime handling can generate tasks dynamically, with no XML or command-line configuration.

Traceloop: What is the impact of ServiceNow acquisition?

Traceloop is joining ServiceNow, representing a strategic acquisition that will broaden enterprise adoption and integration capabilities. Current operations continue with free and enterprise options available.

Source
Apache Airflow: Is Airflow suitable for real-time pipelines?

Not really. Airflow is designed for scheduled batch orchestration. Event-driven or sub-minute work is better served by a streaming platform such as Kafka or a purpose-built streaming engine.

Traceloop: How many LLM providers and frameworks does Traceloop support?

Traceloop supports 20+ LLM providers including OpenAI and Anthropic, and integrates with frameworks like LangChain and LlamaIndex. It can send data to 25+ observability platforms.

Source
Apache Airflow: What are the main alternatives to Airflow?

Dagster and Prefect are the two most commonly weighed against it, both newer and both designed around the local development and testing experience Airflow is criticised for.

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