Software Alternatives, Accelerators & Startups

Evidently AI VS Split Fee

Compare Evidently AI VS Split Fee and see what are their differences

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Evidently AI logo Evidently AI

Open-source monitoring for machine learning models

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.
  • Evidently AI Landing page
    Landing page //
    2023-08-19
  • 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

Evidently AI features and specs

  • Automated Monitoring
    Evidently AI provides automated monitoring of machine learning models, which helps in identifying performance degradation or drift, ensuring models remain accurate and reliable over time.
  • User-Friendly Interface
    The platform offers a user-friendly interface that allows practitioners with varying levels of expertise to easily navigate through features and monitor models effectively.
  • Comprehensive Reporting
    Evidently AI generates detailed reports that include key metrics and insights about model performance, making it easier to communicate findings with stakeholders.
  • Integration Capabilities
    It can be integrated seamlessly with existing data pipelines and machine learning infrastructures, allowing for more streamlined workflows.
  • Open Source
    As an open-source tool, Evidently AI enables greater flexibility and customization, allowing users to modify and extend its features to suit specific needs.

Possible disadvantages of Evidently AI

  • Limited Advanced Features
    While Evidently AI covers basic and intermediate monitoring needs well, it may lack some of the more advanced features offered by other specialized commercial platforms.
  • Dependency Management
    Being open-source, managing dependencies and ensuring compatibility with other tools or libraries can sometimes be challenging and may require additional effort.
  • Resource Intensive
    The tool may require significant computational resources for large scale models or big datasets, which could be a limitation for some users.
  • Initial Setup Complexity
    Initial setup and configuration of the platform might be complex for users without a strong technical background, potentially causing a steeper learning curve.

Split Fee features and specs

No features have been listed yet.

Analysis of Evidently AI

Overall verdict

  • Yes, Evidently AI is a solid choice for monitoring and understanding machine learning models.

Why this product is good

  • User-Friendly: Evidently AI offers an intuitive interface that simplifies the process of monitoring machine learning models.
  • Comprehensive Dashboards: It provides detailed dashboards that help in tracking and understanding model performance over time.
  • Open-Source: As an open-source tool, it allows users to customize and extend its functionality, ensuring it meets specific needs.
  • Automated Reporting: The platform automates the creation of reports, saving time and reducing manual effort in analyzing model outputs.
  • Community Support: Being open-source, it has a community that contributes to its growth and provides support, making it reliable and up-to-date.

Recommended for

  • Data Scientists: To streamline model monitoring and gain insights into model performance.
  • Machine Learning Engineers: To automate the reporting and monitoring process, ensuring models perform optimally.
  • Organizations: That need a scalable and customizable solution for machine learning model reporting and monitoring.
  • Companies Looking for Open-Source Solutions: Those who prefer open-source tools for flexibility and cost-effectiveness.

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

Evidently AI videos

How to Monitor Machine Learning Models (Evidently AI)

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 Evidently AI and Split Fee)
AI
100 100%
0% 0
Recruitment Solutions
0 0%
100% 100
Developer Tools
100 100%
0% 0
Recruitment
0 0%
100% 100

Questions & Answers

As answered by people managing Evidently AI 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, Evidently AI 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.

Evidently AI mentions (2)

  • [D] Using MLFlow for model performance tracking
    It is doable. However the main focus of MLFlow is in experiment tracking. I would suggest for you to look into another monitoring tools such evidentlyai . You can track more things than performance (e.g.data drift). Which may be helpful in a production setting. Source: almost 4 years ago
  • Five Data Quality Tools You Should Know
    Evidently is an open-source Python library that analyzes and monitors machine learning models. It generates interactive reports based on Panda DataFrames and CSV files for troubleshooting models and checking data integrity. These reports show model health, data drift, target drift, data integrity, feature analysis, and performance by segment. - Source: dev.to / over 4 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 Evidently AI and Split Fee, you can also consider the following products

Langfuse - Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.

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

LangSmith - Build and deploy LLM applications with confidence

Helicone AI - Open-source LLM Observability for Developers

Openlayer - Test, fix, and improve your ML models

LangChain - Framework for building applications with LLMs through composability