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ifttt
Pipefy
Microsoft Power Automate
Kissflow
Process Street
Cloudpipes
Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Langfuse
LangSmith
Helicone AI
Galileo AI
PromptLayer
Future AGI
LangChain
Rapidly ship AI without guesswork

Which is more popular?
Based on our record, Apache Airflow seems to be a lot more popular than Braintrust.dev. While we know about 81 links to Apache Airflow, we've tracked only 3 mentions of Braintrust.dev.
Website, pricing, platforms and company facts side by side.
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| Website | airflow.apache.org | braintrust.dev |
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What each product offers, as listed by its team.

Possible disadvantages
Possible disadvantages
An editorial look at what each product does well and who it suits.

Overall verdict
Why this product is good
Recommended for
Apache Airflow is recommended for data engineers, data scientists, and IT professionals who need to automate and manage workflows. It is particularly suited for organizations handling large-scale data processing tasks, requiring integration with various systems, and those looking to deploy machine learning pipelines or ETL processes.
No analysis of Braintrust.dev yet.
Walkthroughs and reviews on video.
Airflow Tutorial for Beginners - Full Course in 2 Hours 2022
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How often each product is chosen within a category, 0–100% relative to the other.

Share your experience with using Apache Airflow and Braintrust.dev. For example, how are they different and which one is better?
External articles and on-site reviews we used to compare the two products.

While Apache Airflow continues to be a popular tool for data orchestration, the alternatives presented here offer a range of features and benefits that may better suit certain projects or team preferences. Whether you...
Apache Airflow is a workflow streamlining solution aiming at accelerating routine procedures. This article provides a detailed description of Apache Airflow as one of the most popular automation solutions. It also...
In a nutshell, you gained a basic understanding of Apache Airflow and its powerful features. On the other hand, you understood some of the limitations and disadvantages of Apache Airflow. Hence, this article helped...
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Recommendations tracked on public social media and blogs since March 2021.

Everything so far ran because you typed it. Apache Airflow runs it on a schedule, retries it, and keeps a record of every run. It doesn't touch the data itself: its tasks talk to Spark through the same Connect server you've been using,... - Source: dev.to / 1 day ago
General orchestrators — Airflow, Prefect, AWS Step Functions, Azure Logic Apps. These treat Each LLM call as just another task in a DAG, and give you the heavyweight reliability Machinery: durable state, scheduling, checkpointing,... - Source: dev.to / 3 months ago
There is a lot of stuff for Python which follows the "express computation as a dag" approach, especially Apache Airflow https://airflow.apache.org/. - Source: Hacker News / about 1 year ago
Braintrust focuses on evaluation-driven development: the idea that monitoring LLM applications means continuously scoring outputs against quality criteria, not just tracking latency and error rates. It's an eval platform first, with... - Source: dev.to / 4 months ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / 4 months ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline — the metadata.userId pattern is the universal part. - Source: dev.to / 4 months ago
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Build and deploy LLM applications with confidence
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