Scale AI is built for frontier-lab and government programmes at very high volume, sold through sales and priced by quote. That is the right fit for some buyers and the wrong one for many: a team that needs a few thousand labelled images, wants European data handling, or would rather not buy from a vendor part-owned by a model builder has good reasons to look elsewhere. Here are sixteen alternatives, grouped by what you are replacing Scale for.
The short list
Disclosure. Dataset Finder publishes this guide and is one of the options in it. We have kept to facts each company states or that were reported in the press, and linked our detailed comparison pages so you can check the trade-offs yourself.
Start with the row that matches your job. Expert networks compete with Scale on post-training data for frontier models; managed services label your own data at volume; platforms let your team do the labelling; Dataset Finder starts by checking whether the data already exists.
| Option | Type | Pricing | Best for |
|---|---|---|---|
| Dataset Finder (ours) | Dataset search + self-serve custom data | Free plan; Startup €9.90/month; Pro €29.90/month. Custom data paid in credits, with a public cost estimator. | Teams that want to check what already exists, then order the gaps self-serve |
| Surge AI | Managed service | Not published; sales-led | Frontier labs buying high-skill RLHF at scale |
| Mercor | Expert network | Not published; sales-led | AI labs that need credentialed domain experts fast |
| Turing | Expert network + managed service | Not published; sales-led | Code and reasoning post-training data |
| Invisible Technologies | Managed service + expert network | Not published; sales-led | A fully managed expert-data team |
| Snorkel AI | Managed service (expert data) | Not published; sales-led | Expert post-training data and evaluations |
| Labelbox | Platform + expert network | Free tier; Starter and Enterprise; hourly rates for labelling services | Your own team in a platform, or expert preference data |
| Toloka | Managed service + self-serve crowd | Not published; self-service priced per project | RL environments and evaluation, or small crowd tasks |
| Sama | Managed service | Not published; quote-based | High-volume image, video and LiDAR annotation |
| iMerit | Managed service + platform | Not published | Medical imaging and mobility at enterprise scale |
| Appen | Managed service + data catalogue | Not published | Multilingual, speech and search-relevance programmes |
| TELUS Digital | Managed service | Not published; sales-led | High-volume, multilingual and automotive programmes |
| CloudFactory | Managed workforce + platform | Not published | Ongoing human review for AI already in production |
| SuperAnnotate | Platform + services | Starter, Pro and Enterprise; prices not published | Running your own annotation and evaluation pipelines |
| Encord | Platform + Data-as-a-Service | Starter, Team and Enterprise; prices not published | Robotics, AV and medical-imaging teams with their own data |
| Kili Technology | Platform | Free trial; Grow and Enterprise on request | Regulated European teams labelling with their own experts |
Why teams look beyond Scale AI
Scale’s strengths are real: volume in post-training and evaluation data, FedRAMP High authorisation and DoD IL4 provisional authorisation, which few vendors have, and a contributor network run through Outlier. In June 2025 Meta made a minority investment that valued Scale at over $29 billion, and founder Alexandr Wang joined Meta; Francis deSouza became CEO in August 2026.
Reasons to compare alternatives are often practical rather than about quality: prices are not published, the offer is shaped for very large programmes, some buyers may prefer a supplier with no ownership link to another model developer, and smaller teams want to start with a test batch rather than a contract. Our Dataset Finder vs Scale AI page sets out the differences in detail.
If you need data that may already exist
Before commissioning anything, check whether an existing dataset covers most of the need. Fine-tuning and evaluation projects often need a few thousand well-chosen examples, and an open dataset with the right licence plus a small custom top-up can be much cheaper than building everything from scratch.
Dataset Finder (ours)
Dataset search + self-serve custom dataSearch tens of thousands of curated datasets in plain language, then order the data that does not exist yet from Innovatiana’s managed annotators in the same app, paid in credits.
- Each dataset card adds automated analysis on 30+ criteria, including licence, PII, bias and fine-tuning readiness.
- Custom requests are labelled by trained annotators and domain specialists under a dedicated Data Labeling Manager, not an open crowd.
- A test batch usually comes back within about 48 hours; large projects move to an Enterprise quote.
- Launched in August 2026, so the product is new; the annotation work is done by Innovatiana, the data-labelling company behind it.
Expert networks for RLHF and post-training data
These are Scale’s closest competitors on frontier work: preference data, SFT, rubrics, evaluations and RL environments written by vetted experts. Most of them sell through sales rather than published prices, and several raised large rounds in 2025 and 2026.
Surge AI
Managed servicePremium human data for frontier labs: RLHF, post-training data, RL environments and benchmarks.
- Reuters reported over $1 billion in 2024 revenue, without outside funding.
- Expert-heavy work aimed at the largest AI labs; Surge does not publish the size of its contractor network.
Mercor
Expert networkA marketplace that recruits and vets domain experts such as doctors, lawyers and engineers, and bills AI labs for the training data, evaluations and RL environments they build.
- Raised a $350M Series C at a $10 billion valuation in October 2025.
- Agreed in July 2026 to acquire Deeptune, a start-up building RL training environments for AI agents.
- Its CEO said gross annualised revenue passed $2 billion in June 2026 (a company figure, before contractor payouts).
Turing
Expert network + managed serviceA former remote-developer marketplace that now supplies coding, STEM and knowledge-work experts, datasets and RL environments to AI labs.
- Raised $111M at a $2.2 billion valuation in March 2025.
- Launched Turing Frontier in April 2026, giving AI labs access to identity- and background-verified professionals.
Invisible Technologies
Managed service + expert networkRLHF and SFT data, evaluations and RL environments for AI labs through its Meridial expert network, plus enterprise workflow automation.
- Raised about $100M at a valuation above $2 billion in September 2025.
- Says its Meridial network has 24,000+ vetted experts.
- Agreed in March 2026 to acquire WeCP, a technical assessment platform, to strengthen expert vetting.
Snorkel AI
Managed service (expert data)A Stanford AI Lab spin-out that now sells expert-built training data, RL environments, rubrics and evaluations to AI labs and enterprises.
- Raised a $350M Series E at a $3.5 billion valuation in September 2026.
- Launched Expert Data-as-a-Service in September 2025, with work done by vetted domain experts.
- Also offers off-the-shelf expert datasets with rubrics and difficulty tiers.
Labelbox
Platform + expert networkA labelling platform plus the Alignerr expert network, RL environments (Horizon), robotics data (Terra) and managed agents.
- A free platform tier for up to 30 users and 50 projects.
- Raised a Series E of over $100M, equity plus a credit facility, in October 2026; Labelbox says it is profitable.
Toloka
Managed service + self-serve crowdExpert data, RL environments and evaluation for AI labs, plus a self-service crowd platform with no minimums.
- Raised $72M in a round led by Bezos Expeditions in May 2025.
- Contributors in 100+ countries and experts in 90+ domains, Toloka says.
Managed annotation of your own data at volume
If what you need is labelling of images, video, LiDAR, audio or text you already hold, a managed workforce is the closer substitute. Look at who the annotators are (employees in delivery centres, a crowd, or experts), the certifications your procurement asks for, and whether a pilot is offered.
Sama
Managed serviceManaged annotation and model validation on its own platform, with a full-time workforce in secure delivery centres.
- Sama says it was one of the first AI companies certified as a B Corp, and has been recertified.
- Depth in automotive, retail and 3D sensor annotation; Sama reports 99% first-batch acceptance.
iMerit
Managed service + platformManaged annotation and GenAI data on its Ango Hub platform, with strong medical-imaging and autonomous-mobility work.
- EXL completed its acquisition of iMerit on 3 August 2026.
- Launched iMerit Scholars, an expert network for GenAI training, in July 2025.
Appen
Managed service + data catalogueCustom collection, annotation and evaluation, an off-the-shelf dataset catalogue and the ADAP platform.
- A crowd of over 1 million contractors in 170+ countries, Appen says, covering 500+ locales.
- Listed on the Australian Securities Exchange; revenue and other income rose 17% in the half year to June 2026.
TELUS Digital
Managed serviceThe AI data arm of TELUS Digital (formerly TELUS International): data collection, multimodal and 3D annotation, RLHF and red-teaming.
- TELUS completed its buy-out of TELUS Digital on 31 October 2025, taking it private.
- Says it has an AI community of over 1 million contributors and covers 500+ annotation languages and dialects.
CloudFactory
Managed workforce + platformA managed human-in-the-loop workforce, an AI platform and consulting, since 2010.
- Lists ISO 27001:2022, SOC 2 and a HIPAA attestation.
- Its homepage now leads with an AI platform and consulting rather than labelling alone.
Platforms where your own team labels
When the knowledge sits with your own experts, or the data cannot leave your environment, a platform is the alternative. Labelbox, above, also belongs here. Open-source tools such as Label Studio and CVAT are covered in our guide to data labelling companies.
SuperAnnotate
Platform + servicesAn annotation and evaluation platform for multimodal data, LLMs and agents, with annotators it can provide.
- Series B extended to $50M in July 2025, with Dell Technologies Capital.
- Evaluation tooling for LLMs and agents, including RLHF ranking and golden-dataset creation.
Encord
Platform + Data-as-a-ServiceA data platform for physical AI: curation, annotation and evaluation of video, LiDAR, DICOM and other multimodal data.
- Raised a $60M Series C in February 2026, bringing total funding to $110M.
- VPC and on-premise deployment options for Enterprise.
Kili Technology
PlatformA self-hostable labelling and evaluation platform for data written by your own experts, built in Paris.
- Self-hosted and on-premise deployment for regulated sectors.
- Customers named on its homepage include SAP, Safran, Michelin and Airbus.
How to choose
- Scope the volume first. A few thousand items rarely justify a sales-led programme; a self-serve order or a platform will be faster. The annotation cost estimator gives a first figure.
- Write the guidelines before the RFP. Vendors can only quote and deliver against a clear specification; the guidelines generator produces one you can send to all of them.
- Ask how quality is measured. Inter-annotator agreement, gold-standard checks and review rates should be stated, not implied. Check a pilot with the kappa calculator.
- Check who does the work and where. Employees, a crowd or an expert network have different costs, consistency and data-security profiles.
- Confirm data rights and handling. Ownership of the labels, NDAs, retention, and GDPR or EU AI Act documentation if you need them.
Sources
Facts were checked on 26 October 2026 against each company’s own site, press releases and filings, and reputable press for corporate events; figures a company reports about itself are marked as its own. Main sources: Dataset Finder, Surge AI, Mercor, Turing, Invisible Technologies, Snorkel AI, TELUS Digital. The comparison pages list every source for the vendors they cover.
Questions people ask
Who are Scale AI’s main competitors?
For frontier post-training data, Surge AI, Mercor, Turing, Invisible Technologies, Snorkel AI, Labelbox’s Alignerr network and Toloka. For managed annotation of your own data, Sama, iMerit, Appen, TELUS Digital and CloudFactory. For doing the labelling yourself, platforms such as Labelbox, SuperAnnotate, Encord and Kili Technology.
Is there a Scale AI alternative with public pricing?
Few vendors in this market publish prices. Labelbox has a free platform tier, open-source tools such as Label Studio and CVAT are free to self-host, and Dataset Finder publishes its plans and offers a free cost estimator for custom data paid in credits.
What is the best Scale AI alternative for a small project?
Usually not another sales-led vendor. Check first whether an open dataset covers most of the need, then order the rest self-serve or label it in a platform. A test batch lets you check quality before committing.
Did Meta buy Scale AI?
No. Meta made a minority investment in June 2025 that valued Scale at over $29 billion, and founder Alexandr Wang joined Meta. Scale remains a separate company; Francis deSouza became its CEO in August 2026.