Earlier this year, Meta’s $15B investment in Scale AI raised new questions about vendor neutrality in model data and evaluation workflows. With Scale now closely tied to Meta, many teams are reassessing how they manage training data, labeling pipelines, and LLM evaluation. The search is shifting toward independent, enterprise-grade platforms that combine secure data infrastructure with expert-led managed services.
This article explores five alternatives to Scale AI – what they offer, how they handle sensitive data, and how they scale labeling and evaluation without conflicts of interest.
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 are receiving an influx of interest from AI model providers looking for “neutral” partners.
Scale AI’s main offering is their workforce of freelance, and contracted annotators, managed by engagement leads.
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 platform for creating and managing large-scale multimodal AI datasets. It gives teams full control over their data pipelines through customizable annotation tools, workflow automation, and integrated vendor management.
Beyond the platform, SuperAnnotate offers managed services that combine dedicated project support with domain-expert labeling through SME Careers – a global network of skilled subject-matter experts. This model brings flexibility and reliability to enterprise AI operations, ensuring each dataset reflects accurate human expertise at scale.
Strengths
- Fully customizable platform for any multimodal use cases
- Full transparency and control over data workflows
- One platform for internal teams, domain experts, QA, and automation
- Managed services with vetted subject-matter experts through SME Careers
Weaknesses
- Not the best fit for very small teams or one-off projects
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.
