Scale AI vs Surge AI

Scale AI turns raw data into the labeled, ranked, and human-reviewed datasets that models train on. It started in self-driving and grew into one of the largest data providers for large language models.

The core of the business is what Scale calls its Data Engine. You send data, Scale runs it through a mix of automated pre-labeling, a managed annotator workforce, and layered quality checks, and you get back training-ready data with the review trail attached. For language models, that increasingly means human feedback: comparing model answers, writing ideal responses, and rating outputs on accuracy and safety.

The work is split across a few brands:

• Outlier, the network of contributors who annotate and rate data for LLMs
• Remotasks, the workforce that handles computer vision and autonomous-vehicle labeling
• The Scale GenAI Platform, for enterprises building on top of foundation models
• A government and defense arm that does evaluation and data work in secure settings

Teams reach for Scale when volume and program management matter as much as the labeling itself. A frontier lab uses it to run a continuous RLHF pipeline. An AV company uses it for lane, object, and sensor-fusion labeling across millions of frames. A large enterprise uses the platform to fine-tune and evaluate a model on its own documents.

In June 2025 Meta took a 49 percent non-voting stake in Scale, in a deal that valued the company near 29 billion dollars, and founder Alexandr Wang moved to Meta to lead its AI efforts. The stake is worth knowing about if your team competes with Meta or wants a data partner with no ties to a specific model lab.

Pricing is enterprise, quoted per program based on data volume, task complexity, and how much human review and management you need.

Best for teams running large or ongoing labeling and RLHF programs who want a vendor that can staff, manage, and QA the whole thing. Less of a fit for a small one-off project or a team that wants a self-serve tool with a published price, where Labelbox or V7 are easier to start with. Some teams also weigh Scale's Meta ownership when they want a labeling partner that sits outside any one frontier lab.

Surge AI produces the human data that language models learn from: preference comparisons, written responses, and careful ratings of model output. The focus is quality and skill rather than raw crowd volume.

Where a lot of labeling grew out of drawing boxes on images, Surge grew up around text and reasoning. The tasks its annotators handle look more like knowledge work: judging which of two answers is better, writing a correct response to a hard prompt, checking a model's math or code, and flagging unsafe or wrong output. That fits how modern LLMs are actually tuned.

A typical engagement covers work like:

• Ranking model responses for reinforcement learning from human feedback
• Writing gold-standard answers for fine-tuning and evaluation sets
• Red-teaming and safety review to surface harmful or incorrect responses
• Domain tasks in code, math, and specialized writing that need real expertise

The company was started in 2020 by Edwin Chen, an engineer who worked on data and ML at Google and Meta, and it grew without much outside funding while passing a billion dollars in revenue. It runs a large network of vetted contributors and leans on internal tooling to keep quality high, and it counts leading AI labs among its customers.

Because the annotators are screened for skill, Surge tends to come up when the bottleneck is the difficulty of the judgments, not the number of items. A team training a reasoning model needs people who can actually tell a good proof from a plausible-looking wrong one.

Pricing is enterprise and quoted per engagement, based on task difficulty, the expertise required, and volume.

Best for frontier and applied LLM teams who need high-skill human feedback and evaluation data they can trust. Less suited to a team that mainly needs cheap high-volume image or video annotation, where a managed-workforce vendor like Appen or a platform like Labelbox fits better.