Scale AI is a data infrastructure company that helps businesses build high-quality training data for AI applications.
Founded in 2016 by Alexandr Wang when he was just 19 years old, Scale AI has become a critical partner for companies developing AI systems across industries like autonomous vehicles, robotics, and generative AI.
Scale AI provides human-powered data labeling and annotation services, which are essential for training machine learning models.
Continue reading to find the latest Scale AI statistics in 2025.
Here's a quick breakdown of what we'll cover:
- Scale AI Stats (Highlights)
- Scale AI Revenue
- Scale AI Valuation
- Scale AI Funding
- Scale AI Investors
- Scale AI Competitors
- Scale AI Ownership
- Scale AI IPO
- Scale AI Employees
Scale AI Stats (Highlights)
- Scale AI generated $870 million in revenue in 2024.
- Scale AI is projected to reach $2 billion in revenue in 2025.
- Scale AI has a valuation of $13.8 billion as of May 2024.
- Scale AI has raised a total of $1.6 billion in funding to date.
- Scale AI founder Alexandr Wang owns 15% of the company.
- Scale AI has approximately 900 employees worldwide.
Scale AI Revenue
Scale AI generated $870 million in revenue in 2024, a 14.5% increase from $760 million in 2023.

Scale AI is projected to more than double its revenue to $2 billion in 2025, representing a 130% increase from 2024.
Here's a table showing Scale AI's annual revenue by year (2022-2025):
Year | Scale AI Revenue |
---|---|
2022 | $250 million |
2023 | $760 million |
2024 | $870 million |
2025 (Projected) | $2 billion |
Scale AI Valuation
Scale AI has a valuation of $13.8 billion as of its Series F funding round in May 2024.

Recent reports suggest the company is seeking a valuation as high as $25 billion in a potential future funding round or tender offer.
Here's a chart showing Scale AI's valuation by year (2019-2025):

Here's a table showing Scale AI's valuation by year (2019-2025):
Date | Funding Round | Scale AI Valuation |
---|---|---|
August 2019 | Series C | >$1 billion |
December 2020 | Series D | $3.5 billion |
April 2021 | Series E | $7 billion |
May 2024 | Series F | $13.8 billion |
Potential Future | (Discussed Target) | Up to $25 billion |
Source:
ReutersScale AI Funding
Scale AI has raised a total of $1.6 billion in funding across 7 rounds, with its most recent Series F round securing $1 billion in May 2024.

Scale AI's Series F round ($1 billion) in 2024 is 62.5% of all funding the company has raised since its founding.
Here's a chart showing Scale AI's funding by year (2016-2024):

Here's a table of Scale AI's funding history:
Date | Round | Amount Raised | Valuation | Lead Investor(s) |
---|---|---|---|---|
August 2016 | Seed | $120K | Not disclosed | Y Combinator |
May 2017 | Series A | $4.5M | Not disclosed | Accel |
August 2018 | Series B | $18M | Not disclosed | Index Ventures |
August 2019 | Series C | $100M | >$1B | Founders Fund |
December 2020 | Series D | $155M | $3.5B | Tiger Global |
April 2021 | Series E | $325M | $7B | Dragoneer, Tiger Global |
May 2024 | Series F | $1B | $13.8B | Accel |
Source:
TechCrunchScale AI Investors
Scale AI investors include: Amazon, Meta, Nvidia, Intel Capital, AMD Ventures, Cisco Investments, Accel, Index Ventures, Elad Gil, Nat Friedman, Adam D'Angelo, Justin Kan, and Drew Houston.

Here's a table showing some of the key investors in Scale AI categorized by type:
Investor Type | Scale AI Investors |
---|---|
Venture Capital | Accel, Index Ventures, Founders Fund, Tiger Global, Dragoneer, Coatue, Thrive Capital, Y Combinator |
Corporate Investors | Amazon, Meta (Facebook), Nvidia, Intel Capital, AMD Ventures, Cisco Investments, ServiceNow Ventures |
Angel Investors | Elad Gil, Nat Friedman, Kevin Systrom, Mike Krieger, Adam D'Angelo, Justin Kan, Drew Houston |
Source:
The SaaS NewsScale AI Competitors
Scale AI competitors include: Labelbox, SuperAnnotate, V7, Snorkel AI, and Encord. All of which are in the AI data infrastructure space.

Here are the main competitors to Scale AI with some more information on each:
Company | Focus Area | Competitive Overlap |
---|---|---|
Labelbox | Data labeling, model training, ML pipelines | One of the most direct alternatives; used by major enterprise clients |
SuperAnnotate | Image/video annotation, team collaboration | Scales annotation pipelines with strong collaboration tooling |
V7 | AI-assisted labeling for computer vision | Competes directly on automation and quality of vision data pipelines |
Snorkel AI | Programmatic labeling, weak supervision | Popular in academia and enterprise for automating labeling at scale |
Encord | Active learning, visual data management | Focused on safety-critical applications like medical and robotics data |
Scale AI Ownership
Scale AI founder Alexandr Wang owns 15% of the company, which at the current valuation of $13.8 billion is worth approximately $2.1 billion.

Wang founded Scale AI in 2016 at age 19 after dropping out of MIT, making him one of the youngest self-made billionaires in the world.
Source:
ObserverScale AI IPO
Scale AI is currently not looking to IPO. Scale AI founder Alexandr Wang has stated:
"I think our goal is to build a super sustainable business where we don't need to keep raising outside capital…we don't really need to keep raising outside capital, beyond this."

Despite investor interest in taking the company public, Wang appears focused on building a sustainable business model that doesn't rely on continuous fundraising or public market capital.
Source:
TechCrunchScale AI Employees
Scale AI has approximately 900 employees worldwide as of 2025.

The company has significantly expanded its workforce to support its rapid growth and the increasing demand for AI data infrastructure services.
Source:
Scale AIConclusion
Scale AI is in a good position for more growth as the AI industry continues to expand rapidly.
As AI adoption continues to increase rapidly across various industries, Scale AI's data infrastructure and annotation services will likely remain in high demand.
This is particularly true for training and improving large language models, computer vision systems, and other AI applications that require high-quality labeled data.