Scale AI

Scale AI provides data labeling, model fine-tuning, and evaluation services to help enterprises build and deploy accurate, production-ready machine learning models.

Enterprise LLM Platforms

Specialization:
Data Labeling, AI Training Data, Computer Vision, Natural Language Processing, Sensor Fusion, 3D Sensor Data, Synthetic Data Generation.

Company Type:
Startup

Total Financing Amount:
$15.9B

Founded Date:
June 2016

Table Of Contents

• Description
• Customers
• Problem They Solve
• Pros
• Cons
• Investors

Description

Scale’s Data Engine manages large-scale data labeling across formats like 3D, images, text, and audio using APIs to distribute tasks efficiently. It blends AI pre-labeling with expert human review to create high-quality training data for model development. The GenAI Platform supports fine-tuning and reinforcement learning (RLHF) by routing enterprise data through models from OpenAI, Anthropic, and Meta. AI teams use curation tools to find valuable unlabeled data with active learning. Developers can also deploy pre-built apps, like Donovan, built for defense analytics. All results feed into evaluation tools that benchmark model performance against custom enterprise goals.

Customers

Meta
Time
Cisco
Opensea
Cohere
Microsoft
Adept

What Problem Does Scale AI Solve?

AI teams often face delays getting the high-quality labeled data required to train machine learning models, spending months on manual annotation or dealing with inconsistent results. This bottleneck delays product launches, increases development costs, and results in poorly performing AI applications. Scale AI provides professional data labeling and annotation services that deliver consistent, accurate training data at scale, allowing teams to focus on model development instead of data preparation.

Pros

• High-Quality Data Labeling:
  Scale AI provides precision crowd-verified annotations—including vision, text, and sensor data—accelerating model training.
• End-to-End Pipeline Support:
  Offers management tools, workforce orchestration, and review mechanisms to maintain labeling accuracy at scale.
• Vertical-Specific Solutions:
  Tailors annotation workflows to sectors like autonomous vehicles, geospatial, and enterprise search use cases.

Cons

• Cost Sensitivity:
  High-fidelity labeling at scale can be expensive and may affect project budgets for continuous data annotation.
• Workflow Rigidity:
  Predefined pipelines may lack flexibility for emerging use cases or unstructured data types.
• Data Security Responsibility:
  While secure, enterprises are accountable for handling sensitive data during annotation workflows.

Investors

Tiger Global Management
Wellington Management
Meta
Y Combinator
DFJ Growth
Human Capital
Ruttenberg Gordon
Accel
Guillermo Rauch
Next Play Ventures
Intel Capital
Index Ventures
Thrive Capital
Founders Fund
Spark Capital
Qualcomm
Cisco Investments
Bradley Horowitz
Durable Capital Partners
Coatue
Kevin Systrom
Scott Belsky
Dragoneer Investment
Amazon
Geodesic Capital
Bryan Rosenblatt
NVIDIA
Greenoaks
Adam D'Angelo
Nat Friedman
Justin Kan
Drew Houston
Charlie Cheever
Outlander Fund
Mike Krieger
Greg Brockman
Junipero Capital
AMD Ventures
Ilya Sukhar
Raison
ServiceNow Ventures
Alspace Ventures
WCM Investment Management
Quantra Ventures

Last updated: November 13, 2025