Software Alternatives, Accelerators & Startups

TFlearn VS Split Fee

Compare TFlearn VS Split Fee and see what are their differences

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TFlearn logo TFlearn

TFlearn is a modular and transparent deep learning library built on top of Tensorflow.

Split Fee logo Split Fee

Split Fee connects UK recruitment agencies to collaborate on permanent placements. Share candidates and vacancies, match automatically, and split the fee.
Not present
  • Split Fee Login screen
    Login screen //
    2026-03-16
  • Split Fee Dashboard page
    Dashboard page //
    2026-03-16

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.

Split Fee

$ Details
freemium ยฃ97.0 / Monthly
Release Date
2026 March
Startup details
Country
United Kingdom
Founder(s)
Abbie Taylor
Employees
1 - 9

TFlearn features and specs

  • User-Friendly Interface
    TFlearn provides a higher-level API that simplifies the process of building and training deep learning models, making it easier for beginners to use TensorFlow.
  • Modular Design
    It offers modular abstraction layers, allowing users to construct neural networks using pre-defined blocks which are easy to stack and customize.
  • Integration with TensorFlow
    TFlearn is built on top of TensorFlow, providing the flexibility and performance benefits of TensorFlow while enhancing its usability.
  • Pre-built Models
    It includes a range of pre-built models and algorithms for common machine learning tasks like classification and regression, facilitating quick experimentation.

Possible disadvantages of TFlearn

  • Lack of Updates
    TFlearn has not been actively maintained or updated in recent years, which may lead to compatibility issues with the latest versions of TensorFlow.
  • Limited Flexibility
    While TFlearn offers a simplified API, it may not offer the same level of customization and flexibility as using TensorFlow's core API directly.
  • Smaller Community
    As a niche library, TFlearn has a smaller user community, which could result in less community support and fewer resources compared to more popular libraries like Keras.
  • Performance Limitations
    Though built on top of TensorFlow, the added abstraction layers in TFlearn could potentially lead to minor performance overhead compared to pure TensorFlow implementations.

Split Fee features and specs

No features have been listed yet.

Analysis of Split Fee

Overall verdict

  • Split Fee (split-fee.com) appears to be a niche referral/fee-splitting platform aimed at connecting professionalsโ€”likely in real estate, legal, or brokerage-type industriesโ€”to share commissions on referred deals. Without independent reviews or verified track record widely available, it should be approached with due diligence, but the concept itself addresses a real market need for structured referral partnerships.

Why this product is good

  • Provides a structured framework for professionals to formally split fees or commissions on referred business
  • Can help expand referral networks beyond one's immediate contacts or region
  • May reduce disputes over referral agreements by formalizing terms upfront
  • Potentially useful for professionals who receive occasional out-of-market or out-of-expertise leads they want to monetize

Recommended for

  • Real estate agents or brokers looking to refer out-of-area clients
  • Legal or financial professionals wanting to formalize referral fee arrangements
  • Freelancers or consultants who want to monetize leads outside their expertise
  • Small firms seeking to expand reach through partner referral networks

TFlearn videos

Face Recognition using Deep Learning | Convolutional-Neural-Network | TensorFlow | TfLearn

Split Fee videos

No Split Fee videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to TFlearn and Split Fee)
OCR
100 100%
0% 0
Recruitment Solutions
0 0%
100% 100
Data Science And Machine Learning
Recruitment
0 0%
100% 100

Questions & Answers

As answered by people managing TFlearn and Split Fee.

Which are the primary technologies used for building your product?

Split Fee's answer:

A next-generation serverless platform, built for AWS.

What makes your product unique?

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.

Why should a person choose your product over its competitors?

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.

How would you describe the primary audience of your product?

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.

What's the story behind your product?

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.

User comments

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Social recommendations and mentions

Based on our record, TFlearn seems to be more popular. It has been mentiond 2 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.

TFlearn mentions (2)

  • Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
    TFLearn โ€“ Deep learning library featuring a higher-level API for TensorFlow. - Source: dev.to / almost 4 years ago
  • Base ball
    Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBIโ€™s, and walkโ€™s are all taken into account and passed through layers. Thereโ€™s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called... - Source: dev.to / over 5 years ago

Split Fee mentions (0)

We have not tracked any mentions of Split Fee yet. Tracking of Split Fee recommendations started around Mar 2026.

What are some alternatives?

When comparing TFlearn and Split Fee, you can also consider the following products

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Unbench - Beyond recruitment, Unbench became a dynamic matchmaking platform, efficiently connecting companies with top specialists.

Clarifai - The World's AI

DeepPy - DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming.

Microsoft Cognitive Toolkit (Formerly CNTK) - Machine Learning

Merlin - Merlin is a deep learning framework written in Julia, it aims to provide a fast, flexible and compact deep learning library for machine learning.