Software Alternatives, Accelerators & Startups

Floyd VS Split Fee

Compare Floyd VS Split Fee and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Floyd logo Floyd

Heroku for deep learning

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.
  • Floyd Landing page
    Landing page //
    2023-03-20
  • 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

Floyd features and specs

  • Ease of Use
    Floyd provides a user-friendly interface that simplifies the process of training and deploying machine learning models, making it accessible for beginners.
  • Collaboration
    The platform supports collaboration features, allowing teams to work together on projects seamlessly, facilitating better communication and productivity.
  • Managed Infrastructure
    Floyd handles the underlying infrastructure, freeing users from maintenance and setup tasks, and enabling them to focus on model development.
  • Resource Scalability
    The service allows easy scaling of computational resources according to project needs, which is beneficial for handling large datasets and complex models.
  • Experiment Tracking
    It offers robust tools for experiment tracking, helping users to log, compare, and reproduce experiments effectively.

Possible disadvantages of Floyd

  • Cost
    Operating on Floyd might be expensive for individual users or small teams, especially at scale, compared to setting up their own infrastructure.
  • Dependency on Internet
    Since Floyd is cloud-based, it requires a stable internet connection, which might be a limitation in areas with poor connectivity.
  • Learning Curve for Advanced Features
    While easy to start with, mastering some advanced features might require more time and learning, which could be a barrier for some users.
  • Limited Offline Access
    Being a cloud-based platform, offline access to projects and data might be restricted, potentially disrupting workflows during downtime.
  • Integration Limitations
    The platform may have limitations in integrating with certain third-party tools or systems, which could create challenges for users with specific requirements.

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

Floyd videos

How to: Floyd Bed and Purple Mattress + Review (Not Sponsored)

More videos:

  • Review - Floyd Bed Frame Setup and Review - Is it Supportive Enough?
  • Review - FLOYD (FLAT PACK) REVIEW/UNBOXING | THE SOFA + THE COFFEE TABLE + THE FLOYD BED | APARTMENT BUNDLE

Split Fee videos

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

Add video

Category Popularity

0-100% (relative to Floyd and Split Fee)
AI
100 100%
0% 0
Recruitment Solutions
0 0%
100% 100
Chatbots
100 100%
0% 0
Recruitment
0 0%
100% 100

Questions & Answers

As answered by people managing Floyd 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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What are some alternatives?

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

Paperspace - GPU cloud computing made easy. Effortless infrastructure for Machine Learning and Data Science

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

Amazon Machine Learning - Machine learning made easy for developers of any skill level

Hugging Face - The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.

Azure Machine Learning Service - Build and deploy machine learning models in a simplified way with Azure Machine Learning service. Make machine learning more accessible with automated capabilities.

Google Cloud Machine Learning - Google Cloud Machine Learning is a service that enables user to easily build machine learning models, that work on any type of data, of any size.