Apache Airflow VS Split Fee
Compare Apache Airflow VS Split Fee and see what are their differences
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Split Fee
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Split Fee is a platform for UK recruitment agencies to collaborate on permanent placements.
Every agency has candidates they can't place and vacancies they can't fill. Another agency almost certainly has what you need โ but finding them, trusting them, and making the collaboration work has always been the hard part.
Split Fee solves this. Post your candidates and vacancies to the platform. Our matching algorithm finds opportunities across every agency on the network โ by skills, location, salary, and seniority. Candidate and client data stays anonymised until both sides agree to work together. When a placement is made, we handle the fee split and invoicing automatically.
No more posting in social media groups and hoping someone replies. No more sharing candidate details with strangers on trust alone. No more chasing invoices from agencies you barely know.
45% for you. 45% for them. 10% platform fee โ only when a placement is made.
Half of a placement fee is infinitely better than none of it.
Apache Airflow Split FeeNo features have been listed yet.
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.
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Split Fee's answer:
A next-generation serverless platform, built for AWS.
Split Fee's answer:
Split Fee is the first purpose-built platform for split fee recruitment in the UK. Instead of relying on LinkedIn groups, WhatsApp messages, and manual agreements, agencies upload their candidates and vacancies and the platform automatically matches them across agencies.
Candidate identities are revealed gradually; anonymised at first, then progressively disclosed as both sides accept, so agencies can collaborate without the risk of circumvention.
Split Fee's answer:
Most "split fee networks" are just directories or social groups where agencies post and hope someone responds. Split Fee is an actual matching engine. It scores candidate-vacancy pairs, handles the legal agreements (non-circumvention, self-billing), automates invoicing and fee splits, and manages the entire placement lifecycle from match to payment. Everything that normally requires trust, phone calls, and spreadsheets is handled by the platform.
Split Fee's answer:
UK recruitment agencies; from boutique firms with a handful of consultants to mid-sized agencies with specialist sector coverage. Any agency that has either strong candidates without the right vacancies, or client vacancies they can't fill from their own candidate pool. Split Fee turns those dormant assets into placements.
Split Fee's answer:
Abbie, our founder, knew that although split fee arrangements have existed in recruitment for decades, the process has always been manual: find a partner agency, negotiate terms, trust them with your candidate data, chase invoices. Most agencies avoid it because the overhead and risk outweigh the reward. Abbie built Split Fee to remove that friction entirely, with automated matching, progressive identity disclosure, built-in legal agreements, and automated billing, making split placements as straightforward as direct ones.
Based on our record, Apache Airflow seems to be more popular. It has been mentiond 80 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
General orchestrators โ 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, audit trails, human approval. - Source: dev.to / 14 days 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 / 10 months ago
Doing ingestion or data processing with Airflow, a very popular open-source platform for developing and running workflows, is a fairly common setup. DataHub's automatic lineage extraction works great with Airflow - provided you configure the Airflow connection to DataHub correctly. - Source: dev.to / 12 months ago
Apache Airflow represents the open-source workflow orchestration approach to MongoDB ETL. By combining Airflow's powerful scheduling and dependency management with a Python library like PyMongo, you can build highly customized ETL workflows that integrate seamlessly with MongoDB. - Source: dev.to / 12 months ago
You appear to be making the mistake of assuming that the only valid definition for the term "workflow" is the definition used by software such as https://airflow.apache.org/ https://www.merriam-webster.com/dictionary/workflow thinks the word dates back to 1921. There no reason Anthropic can't take that word and present their own alternative definition for it in the context of LLM tool usage, which is what they've... - Source: Hacker News / about 1 year ago
Make.com - Tool for workflow automation (Former Integromat)
Unbench - Beyond recruitment, Unbench became a dynamic matchmaking platform, efficiently connecting companies with top specialists.
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
Pushwoosh - Mobile-inspired customer engagement platform for high achievers
Pipefy - Pipefy is a process management software that empowers anyone to create and automate efficient workflows on their own without code.
Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.