Software Alternatives & Startups
Register | Login

git-sizer VS Prolyz

Compare git-sizer VS Prolyz and see what are their differences

git-sizer

Compute various size metrics for a Git repository, flagging those that might cause problems - github/git-sizer

Rating
0 reviews
Alternatives Is it good?
Prolyz

Prolyz turns raw data into decisions you can defend: real-time CDC and ETL, governed catalog and lineage, built-in OLAP reporting and causal AI. Data stays private and local.

Rating
0 reviews
Alternatives Is it good?
Zoviz
Zoviz: Professional Logos, Perfectly Personalized Your Brand. Your Story sponsored
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.

Which is more popular?

Based on our record, git-sizer seems to be more popular. It has been mentioned 1 time since March 2021.

social mentions
1 vs 0
Git popularity
100% vs 0%

Base details

Website, pricing, platforms and company facts side by side.

git-sizer
Prolyz
Website github.com prolyz.com
Platforms —
Web Self Hosted Linux Cloud +1
Company — Startup from the United Kingdom · 1 - 9 employees · 2026
Listed in
Git Code Collaboration Git Tools
Data Integration Business Intelligence Data Governance Web Service Automation ETL API Tools Stream Processing Data Dashboard Data Visualization Analytics Data Analytics SaaS +9

About git-sizer and Prolyz

In their own words, as submitted to SaaSHub.

git-sizer
Prolyz

No description of git-sizer yet.

Prolyz is your company's Decision Brain a data intelligence platform that turns raw enterprise data into decisions you can defend. Four governed layers work as one: Data governs with catalog, lineage, and quality; Sync moves data in real time across 50+ connectors; Report analyzes at scale with...

Read more about Prolyz

Features and specs

What each product offers, as listed by its team.

git-sizer 5 features
Prolyz 5 features
  • Comprehensive Repository Analysis
    git-sizer analyzes many different dimensions of a Git repository including commit count, tree size, blob size, history depth, and reference counts, providing a holistic view of repository health and potential scaling issues.
  • Easy to Use
    The tool is simple to run with minimal setup—just execute it within a git repository—and it produces clear, human-readable output that highlights potential problem areas without requiring complex configuration.
  • Identifies Performance Bottlenecks
    It helps identify specific issues that could degrade Git performance, such as excessively large blobs, deep history, large trees, or too many references, which is valuable before migrating or scaling repositories.
  • Open Source and Maintained by GitHub
    Being an official GitHub project, it benefits from credibility, community trust, and ongoing maintenance, and it is well documented with clear explanations of what each metric means.
  • Useful for Pre-Migration Checks
    It's particularly helpful for teams migrating repositories to new platforms or consolidating repos, as it flags potential issues that could cause problems during migration or with hosting providers' limits.

Possible disadvantages

  • No Automatic Remediation
    git-sizer only identifies and reports issues but does not offer any built-in tools or automated processes to fix problems like large blobs or excessive history depth—users must use separate tools like BFG Repo-Cleaner or git-filter-repo.
  • Output Can Be Overwhelming for Beginners
    While detailed, the output includes many metrics and threshold levels that may be confusing for users unfamiliar with Git internals, requiring some learning curve to fully interpret results.
  • Limited to Local Analysis
    The tool analyzes a local clone of the repository, so it requires users to have a full local copy of the repo (or at least enough history) to get accurate results, which can be time-consuming for very large repositories.
  • No Real-Time Monitoring
    It functions as a one-time analysis tool rather than providing continuous or real-time monitoring of repository health, requiring manual reruns to track changes over time.
  • Command-Line Only Interface
    The tool lacks a graphical user interface, which may be less accessible for users who prefer visual dashboards or are less comfortable with command-line tools.
  • Real-time CDC
    Log-based change data capture streams every insert, update and delete the moment it is committed, across 50+ connectors.
  • Built-in OLAP analytics
    ClickHouse engine included: interactive reports and ML forecasting on live data, with no separate warehouse to stand up.
  • Causal AI
    Causal inference (Pearl, DoWhy) explains why a number moved, not just that it did, with the reasoning attached.
  • Privacy-first local AI
    All AI reasoning runs on a local model inside your environment. Neither data nor metadata leaves it. GDPR and KVKK aligned.
  • Governed catalog & lineage
    Every asset cataloged with column-level lineage, quality scoring, quarantine rules and domain ownership.

Analysis

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

git-sizer
Prolyz

Overall verdict

  • git-sizer is a solid, focused open-source tool that effectively analyzes Git repositories to identify size and structural issues that could cause performance problems or hosting limits, making it a valuable diagnostic utility for repository maintenance.

Why this product is good

  • Quickly identifies large blobs, deep histories, and other repository bloat issues that impact performance
  • Simple command-line tool with no complex setup or dependencies required
  • Provides clear, actionable metrics about repository size and structure
  • Backed by GitHub, ensuring credibility and ongoing relevance to Git ecosystem needs
  • Helps proactively catch issues before they cause problems with hosting platforms or clone/fetch performance
  • Open source and actively maintained with community input

Recommended for

  • Repository administrators managing large or growing codebases
  • Teams migrating repositories to new hosting platforms with size limits
  • Developers troubleshooting slow clone, fetch, or checkout operations
  • DevOps engineers auditing repository health before major infrastructure changes
  • Organizations enforcing repository size policies or best practices
  • Anyone dealing with repositories that have accumulated large binary files or excessive history over time

No analysis of Prolyz yet.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
git-sizer
Prolyz
100% 100%
Git
0% 0%
0% 0%
Business Intelligence
100% 100%
100% 100%
Code Collaboration
0% 0%
0% 0%
Data Integration
100% 100%

Questions & Answers

As answered by people managing git-sizer and Prolyz.

What makes your product unique?

Prolyz's answer:

Three things that rarely ship together in one platform:

  • Built-in OLAP. A ClickHouse engine is part of the product, so interactive analysis and ML forecasting run on live data with no separate warehouse to stand up.
  • Causal AI, not correlation. Prolyz Agent uses causal inference (Pearl's framework via DoWhy) to isolate why a number moved, and returns the reasoning with the answer.
  • Privacy-first local AI. All reasoning runs on a model inside your environment. Neither data nor metadata leaves it, which is what makes on-premise and air-gapped deployments possible.

Underneath, four governed layers hand data to each other: Data (catalog, lineage, quality), Sync (real-time CDC and ETL), Report (OLAP analytics) and Agent (decisions).

Why should a person choose your product over its competitors?

Prolyz's answer:

It depends on what you already run, and we try to be honest about that.

  • Against pipeline tools (Fivetran, Airbyte): they move data into a warehouse you still have to buy, model and report on. Prolyz moves it and then governs, analyses and explains it in one platform.
  • Against enterprise suites (Informatica): they are deeper in master data management, but reporting and reasoning live in other vendors' tools. Prolyz ships built-in OLAP and causal AI.
  • Against BI (Power BI, Tableau): a dashboard shows what changed. Prolyz Agent traces why, with causal inference rather than correlation.

And one hard requirement others rarely meet: everything, including the AI, can run on-premise or air-gapped, with no data or metadata leaving your environment.

How would you describe the primary audience of your product?

Prolyz's answer:

Data and analytics teams in mid-market and enterprise organisations that run business-critical systems (SAP, CRM, operational databases) and are expected to answer questions faster than a nightly batch allows.

Typical buyers: heads of data, BI and analytics leads, data engineers and CTOs. The strongest fit is regulated sectors, finance, insurance, healthcare, public sector and manufacturing, where data has to stay on-premise or in-country and every number needs an audit trail.

Common starting point: a team that already has dashboards, but where the question "why did this change?" still takes days.

What's the story behind your product?

Prolyz's answer:

Prolyz was founded in 2026 by two engineers who had spent years building data platforms for enterprises, and kept watching the same thing happen: the pipelines worked, the dashboards were accurate, and nobody could say why a number moved. Answering that took days of exports, meetings and guesswork.

The second pattern was regulatory. In finance, insurance and the public sector, the answer to "can we send this data to a cloud model?" was simply no, which ended most AI projects before they started.

So Prolyz was built around two commitments: reason about causes rather than correlations, and do it entirely inside the customer's environment. The company is registered in London as Prolyz Ltd, with engineering in Istanbul.

Which are the primary technologies used for building your product?

Prolyz's answer:

  • Services: .NET 8 microservices behind a YARP gateway, OAuth 2.0 / OIDC via OpenIddict, RabbitMQ and MassTransit for events.
  • Data path: PostgreSQL for OLTP, log-based CDC into ClickHouse for OLAP, Redis for cache, Qdrant for vector search.
  • AI: Python and FastAPI with DoWhy for causal inference and local LLMs (Ollama) so nothing leaves the customer's environment; MCP servers pull external context.
  • Frontend: React and Next.js.
  • Operations: Docker and Kubernetes with Helm, OpenTelemetry, Prometheus, Grafana, Loki and Tempo.

Deployment: managed SaaS, hybrid, or fully on-premise and air-gapped, from the same codebase.

User comments

Share your experience with using git-sizer and Prolyz. For example, how are they different and which one is better?

Log in or Post with

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

git-sizer 1 mention
Prolyz 0 mentions
  • how to keep github repos small?
    Also there’s a cool project from GitHub you can use to help understand the size of git’s objects in your git repo https://github.com/github/git-sizer. This might help you determine what the best cloning strategy could be. Source: almost 5 years ago

Tracking Prolyz since Aug 2026.

Loading...