A few weeks ago, Meta invested $15 billion in Scale AI and bought nearly half of the company. Scale’s biggest customers today include Microsoft, OpenAI, Google, xAI, and Cohere – the very companies Meta is racing against in the AGI race. Unsurprisingly, Google and OpenAI are already cutting ties with the company, and looking for Scale AI alternatives. Others may follow.
So what now?
If you're one of those teams, you’re likely asking the same question: Who can we trust with our most sensitive model data and evaluation workflows? This article breaks down five alternatives to Scale AI, what they offer, their strengths and weaknesses.
Scale AI
Scale AI was founded in 2016 by Alexander Wang, with a recent valuation of $29B. Their top customers were OpenAI, Google, Microsoft, Department of Defense. Just a week following the Meta investment, two of these major players, OpenAI and Google, pulled back. They announced plans to move away from Scale AI for dataset creation. Scale AI competitors said they received an influx of interest from AI model providers looking for “neutral” partners.
Scale AI’s main offering is their workforce of freelance and annotators, managed by engagement leads on the internal Scale annotation editors.
While Scale handles end-to-end annotation projects, including labeling, workforce management, etc, it often operates more like a black box. Customers send in their data, but don’t get much visibility into how it’s being handled. That lack of control and real-time feedback can affect the long-term quality and outcomes of AI projects.
Strengths
- Strong record of service
- Widespread annotation workforce
Weaknesses
- Limited quality control measures
- High pricing threshold
- Lack of comprehensive technology internally
- Half-owned by Meta
SuperAnnotate
SuperAnnotate is the #1 data labeling tool on G2 and a leading enterprise software provider for creating and managing large-scale multimodal AI datasets. It’s the only platform on the market that is fully customizable, letting teams build the exact annotation tools and workflows they need.
Superannotate also lets customers unify data, teams, and vendors in one place, avoiding the patchwork of separate solutions that often slows enterprise AI projects. In addition to the platform, SuperAnnotate provides consulting to help teams design workflows they can fully control and easily track – something that’s hard to do with most other vendors.
Strengths
- Fully customizable platform for any multimodal use cases
- Full transparency and control over data workflows
- One platform for internal teams, vendors, QA, and automation
Weaknesses
- Not the best fit for very small teams or one-off projects
- Better suited for teams who want control, not full outsourcing
Labelbox
Labelbox was founded in 2018 and has raised $189M in funding. Top customers include Google cloud for LLM evaluation. Labelbox has a fixed UI and offers basic tools for labeling and evaluation, including model-assisted pipelines. They’re leaning heavily into offering fully managed services alongside the platform, which is pulling their focus away from further developing the core labeling software.
Strengths
- Built-in foundation model pre-labeling
- Offers fully managed services alongside the platform
- Some workflow customization
Weaknesses
- Pricing changes and tight bundling with services
- Requires engineering effort for automation
- Limited customization and data ingestion issues
Snorkel
Snorkel was founded in 2015 as a Stanford research project and is known for its programmatic approach to labeling, mainly in NLP and document classification use cases. More recently, they’ve started offering an Expert Data Service, adding a human-in-the-loop option alongside their software.
The platform works well for structured tasks but requires engineering effort to set up. It's more suited for teams with internal ML expertise and less flexible for broader or multimodal use cases.
Strengths
- Supports programmatic labeling for NLP and document data
- Now offers expert-labeled data as a service
Weaknesses
- High pricing (typically starts around $500K)
- Rigid setup, limited flexibility across use cases
- Requires engineering effort and weak supervision knowledge
- Few public reviews and limited visibility into customer experience
Dataloop
Dataloop is an Israel-based company founded in 2017. It has raised $50M and serves customers like Ford and CloudFactory. The company started in computer vision and has since expanded to cover a broader range of annotation workflows.
Strengths
- Developer-friendly SDK
- Robust documentation
Weaknesses
- Poor annotator experience
- Static UI and reported performance issues
- Workflows aren’t flexible and often need technical help to adjust
- Less intuitive for non-technical users
Encord
Encord is a London-based startup founded in 2020 by two founders with backgrounds in finance and physics. They’ve raised funding to focus on AI-assisted labeling, with a strong presence in the medical and video annotation space. Key customers include Cedars-Sinai and Stanford Medicine.
Strengths
- Strong tools for video-based workflows, including in-video tracking and rendering
- AI-assisted labeling with SAM 2 integration
- Encord Index helps with reviewing edge cases, visualizing data, and fine-grained search
Weaknesses
- Not built for full customization across different data types
- Doesn’t focus on multi-team project coordination
- Less flexible for complex or multimodal GenAI projects
Final Thoughts
Meta’s $15B investment in Scale, and the decision to bring its CEO into the superintelligence lab, has created tension with Scale’s biggest customers. OpenAI and Google are already moving away, and others may not be far behind. When your data infrastructure is tied to a direct competitor, it’s hard to ignore the risks.
If you’re rethinking your setup, this is a good time to explore other options. Whether you need more transparency, flexibility, or simply a neutral partner, there are strong alternatives to consider.
You can always talk to our team at SuperAnnotate if you want to see how it might fit your workflow.
