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git-sizer VS AWAI

Compare git-sizer VS AWAI 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?
AWAI

Group conversations, sorted by what people said and why they said it.

Rating
0 reviews
Pricing
Freemium $11.99 / Monthly (Plus plan (group analysis is priced separately))
Alternatives Is it good?
VulnGuard AI
AI-powered smart contract vulnerability scanner. Detect 36+ security vulnerabilities in Solidity code instantly. Used by Web3 teams globally. 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?

AWAI might be a bit more popular than git-sizer. We know about 1 link to it since March 2021 and only 1 link to git-sizer.

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

Base details

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

git-sizer
AWAI
Website github.com awai.live
Pricing —
Freemium $11.99 / Monthly (Plus plan (group analysis is priced separately)) Official pricing
Platforms —
Web SaaS
Company — Startup from Japan · 1 - 9 employees · 2026
Listed in
Git Code Collaboration Git Tools
Communication Business Communication Team Communication Meeting Tools Customer Feedback AI +3

About git-sizer and AWAI

In their own words, as submitted to SaaSHub.

git-sizer
AWAI

No description of git-sizer yet.

AWAI has two ways of working, built on the same analysis engine. Group analysis replaces a fixed questionnaire. You write what you want to ask in plain prose and share an invite link; the people answering need no account. Each person talks with the AI on their own, and the AI follows up to draw...

Read more about AWAI

Features and specs

What each product offers, as listed by its team.

git-sizer 5 features
AWAI 10 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.
  • Group analysis
    Each respondent talks with the AI one-on-one from an invite link. No account needed on their side.
  • Opinions and reasons, grouped separately
    Answers are regrouped by opinion, by the reason underneath it, and by how many people arrived there. A view only one person raised stays as an outlier.
  • Live meeting analysis
    What people say collects under topics while the conversation is still going, and stays sorted by topic afterward.
  • Per-participant language
    Each person picks the language they speak and the language they read. The same meeting is shown to each in their own.
  • Reports from a plain-language question
    Put a question to the analyzed material and it becomes a report. Export to PDF and edit it by hand at no extra cost.
  • Respondents per group analysis
    Up to 100 (self-serve)
  • Participants per meeting
    Up to 12
  • Interview depth
    15, 30, 45 or 60 minutes (does not change the price)
  • Reports per project
    Up to 50
  • Public demo
    No signup required

Analysis

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

git-sizer
AWAI

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 AWAI 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
AWAI
100% 100%
Git
0% 0%
0% 0%
Customer Feedback
100% 100%
100% 100%
Code Collaboration
0% 0%
0% 0%
Communication
100% 100%

Questions & Answers

As answered by people managing git-sizer and AWAI.

What makes your product unique?

AWAI's answer:

Two things, and both come from how the analysis is built.

First, opinions and the reasons underneath them are gathered separately. Because of that, a reason shared by people who disagree still shows through, and a view only one person raised stays visible as an outlier instead of being averaged away.

Second, the same engine handles a group of one-on-one AI conversations and a live meeting between people. A meeting is sorted by topic while it is still running, and each participant reads it in the language they chose.

Both end in the same place: put a question to the material in ordinary prose and it becomes a report, exportable to PDF and editable by hand at no extra cost.

Why should a person choose your product over its competitors?

AWAI's answer:

Most tools in this space run AI interviews and hand back themes and sentiment. AWAI differs in three concrete ways.

It separates opinions from the reasons behind them, so you can see when people who disagree are working from the same reason — usually the thing that decides whether a decision holds.

It covers meetings as well as one-on-one interviews on the same engine, so work that starts as a survey and continues as a discussion stays in one place.

Reports are not a fixed deliverable. You put a plain-language question to the analyzed material and get a report angled for that reader — one for leadership, one for the floor, one as requirements for a vendor — up to 50 per project.

What AWAI does not have: a dedicated sentiment-analysis feature, and multimedia collection. Group analysis is text; audio is handled on the meeting side.

How would you describe the primary audience of your product?

AWAI's answer:

HR and organization-development teams collecting employee opinions; executives and team leads who need decisions backed by the reasoning behind them; multilingual or distributed teams that meet across languages; consultants and researchers gathering qualitative feedback at scale.

What's the story behind your product?

AWAI's answer:

AWAI is a Japanese word — an old reading of the character for "interval" — meaning the space between two things. It names what the product looks at: the structure that shows up between one thought and another, and the common ground that appears between one person's thinking and another's.

The starting point was a shift that came with AI. Tools, techniques and knowledge — the things outside a person — became easy to produce, and once many people hold the same ones, they stop being what sets anyone apart. What AI can genuinely extend is the thinking on the inside.

Conversations are where that thinking lives, but they are hard to read afterward: things get said in the order they occur to people, not in the order that makes sense. AWAI takes conversations that have already happened and sorts them by what was said and why, so the shape of the thinking becomes something you can look at.

Which are the primary technologies used for building your product?

AWAI's answer:

Frontend: React, TypeScript, Vite, and Three.js (react-three-fiber) for the 3D views. Backend: Python, FastAPI, SQLAlchemy, and PostgreSQL with pgvector for embeddings. AI: Google Gemini — structured output for the analysis pipeline, embeddings for grouping, and the Live API for meetings. Real-time meetings: LiveKit. Auth: Firebase Auth. Infrastructure: Google Cloud Run and Cloudflare.

User comments

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

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Social recommendations and mentions

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

git-sizer 1 mention
AWAI 1 mention
  • 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
  • I measured Gemini Live Translate in 3 languages: same rhythm, 2x the characters
    The tool is called AWAI: people in different languages sit in the same meeting and talk. The subtitles I measured here are on that screen. - Source: dev.to / 22 days ago
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