We used Oden to analyze product sites, documentation, G2 reviews, Reddit threads, AWS Marketplace listings, and recent news so you don’t have to wade through marketing fluff over the last six months. If you’re trying to choose a visual AI platform for large-scale images and video, the options can look very similar on the surface but behave very differently in practice. This guide compares Coactive.ai, Scale AI, Labelbox, and V7 on ratings, cost structures, core features, and real user feedback. By the end, you should have a short list that fits your data volume, governance needs, and budget.

Which visual AI platform has the best ratings?

Note: Ratings are snapshots and change over time. Sample sizes vary a lot, so treat them as directional, not definitive.

Takeaways

• V7 Darwin and Labelbox have the most robust review bases in this group; both are long-standing data-labeling platforms with dozens of G2 reviews. Source: G2 – V7 Darwin, G2 – Labelbox.
• Coactive’s rating is solid, but with only 16 reviews it’s still an emerging player compared to Labelbox/V7; expect more variability and less “community knowledge.” Source: G2 – Coactive.
• Scale AI’s 5.0 rating is based on a single G2 review, so it’s not statistically meaningful; you’ll need to rely more on references, pilots, and contractual guarantees. Source: G2 – Scale AI.
• For statistically significant comparisons, treat V7 and Labelbox as the most “battle-tested” on public review platforms, then layer in your domain and compliance needs.

How much do visual AI platforms really cost?

Public pricing is sparse—especially for Scale AI and Coactive—but we can piece together how each platform typically charges.

Cost patterns – what this means in practice

• Coactive and Scale AI are firmly enterprise-priced. Coactive’s published Tier 1 contract at $75k/year is a good mental anchor; Scale AI typically works on even larger, multi-hundred-thousand or multi-million dollar contracts with AI labs and governments. Source: AWS Marketplace – Coactive AI Platform, Scale AI company overview, Scale resources.
• Labelbox and V7 Darwin offer clearer “start small” paths. Both expose free tiers and have more accessible on-ramps (LBUs for Labelbox, file/seat-based plans for V7), even though serious production use still requires custom quotes. Source: Labelbox pricing, G2 – V7 Darwin, AWS Marketplace – V7 Darwin Starter.
• Billing complexity grows with your stack. Once you add human labeling services (Scale’s Data Engine, Labelbox Alignerr, V7 labeling services) and GenAI evaluation or RLHF, your total cost of ownership often dwarfs the base platform subscription. Source: Labelbox labeling services, V7 labeling services, Label Your Data – Scale AI vs competitors.

Pricing also varies by region, usage profile, implementation scope, and contract terms. Always double-check current prices with each vendor's calculator or sales team.

What are the key features of each platform?

Coactive.ai

Core positioning: A multimodal AI platform (MAP) focused on search, tagging, and analytics for large-scale image, video, and audio libraries.

Key Features:

• Multimodal AI Platform for visual content. Single platform to manage and activate catalogs across images, video, and audio with ingestion, embeddings, and lineage tracking. Source: Coactive – Platform, Coactive – Overview.
• High-throughput ingestion and preprocessing. Coactive cites a customer ingesting up to 2,000 video hours per hour while reducing costs by 30–60% vs traditional pipelines, using automatic shot and audio segmentation. Source: Coactive – Platform, Coactive – Overview.
• Dynamic tagging and rich metadata. “Dynamic Tags” let you tag content with natural-language prompts at frame, shot, and video level, without training a custom model up front. Source: Coactive – Platform.
• Foundation-model agnostic. Open catalog of multimodal foundation models plus “bring your own” via AWS Bedrock, Azure AI, and Databricks; supports chaining models for deeper analysis. Source: Coactive – Platform.
• Semantic search across modalities. Unified semantic search lets users query visual, audio, and transcript signals with natural language or SQL to find specific scenes, objects, and concepts. Source: Coactive – Platform.
• Enterprise-grade analytics and evaluation. Autumn ’25 release adds Auto Evaluator for precision/recall/F1 on visual tags and claims 81% zero-shot tagging accuracy with up to 60% lower ingestion costs compared to earlier baselines. Source: Coactive Autumn ’25 press release.

Best For:

• Media, entertainment, and UGC-heavy firms with massive video/image archives and limited existing metadata. Source: Coactive homepage, Coactive Autumn ’25 press release.
• Teams prioritizing search, personalization, content moderation, and analytics over manual labeling. Source: Coactive homepage.
• Organizations comfortable with enterprise contracts (often via AWS Marketplace) rather than self-serve SaaS. Source: AWS Marketplace – Coactive AI Platform.

Scale AI

Core positioning: A full-stack AI data infrastructure and GenAI platform spanning data labeling, evaluation, and agentic applications for enterprises and governments.

Key Features:

• Data Engine for multimodal labeling. End-to-end data operations—collection, curation, and annotation across images, video, text, audio, and 3D/LiDAR—plus synthetic data and RLHF workflows. Source: Scale docs – Overview, Encyclopedia summary of Scale products, .
• GenAI Platform for agents and RAG. Scale GenAI Platform helps teams build and control agents that reason over enterprise data, use tools, and run advanced RAG pipelines, with deployment in customers’ own VPCs. Source: Scale – GenAI Platform, GenAI Platform docs.
• Model evaluation and safety. Independent evaluation lab (SEAL) and tools like “Scale Evaluation” benchmark foundation models, used by major AI labs and at events like DEF CON red-teaming. Source: Scale AI – Wikipedia, Scale resources.
• Enterprise and defense focus. Deep relationships with the U.S. Department of Defense, federal agencies, and frontier labs (OpenAI, Meta, Microsoft, etc.), with contracts up to $250M+ in public sector. Source: Scale AI – Wikipedia, Scale – Y Combinator profile.
• Human-in-the-loop at scale. Large global contractor workforce (Remotasks, Outlier) for complex computer-vision and language tasks, combined with automated QA and consensus scoring. Source: Scale AI – Wikipedia, Label Your Data – Scale AI quality.

Best For:

• Enterprises and governments building full-stack AI programs (data, RLHF, evaluation, and applications) rather than “just” a visual search engine. Source: Scale resources, Encyclopedia summary of Scale products.
• Organizations with very large, multimodal datasets (vision, sensor fusion, text) and complex safety or defense requirements. Source: Scale AI – Wikipedia.
• Teams that value custom contracts, concierge support, and integrated evaluation over transparent SaaS pricing. Source: Label Your Data – Scale AI pricing.

Labelbox

Core positioning: A “data factory for AI teams” combining data-labeling software, GenAI alignment tools, and managed labeling services (Alignerr).

Key Features:

• Annotate – full-featured labeling platform. Supports computer vision, NLP, multimodal chat, and LLM evaluation with 10+ editors, model-assisted labeling, and customizable workflows. Source: Labelbox docs – Get started, Annotate overview.
• Catalog and vector search. Centralizes datasets from 25+ cloud sources with vector and traditional search, cluster-based curation, and bulk classification for zero-shot labeling. Source: Labelbox docs – Get started, Bulk classification.
• Post-training and RLHF support. Built-in flows for RLHF, supervised fine-tuning, multimodal evaluations, preference ranking, and red teaming. Source: Labelbox docs – Get started, Labelbox homepage.
• Alignerr labeling services. Managed services and Alignerr Connect marketplace for expert AI trainers (3% acceptance rate), with a quality guarantee and ability to redo labels free if quality drops below agreed thresholds. Source: Labelbox labeling services, Labeling services docs.
• Free tier and education program. Free platform tier for up to 30 users and 50 projects, plus free access for qualified educational institutions doing non-commercial research. Source: Labelbox pricing.

Best For:

• Teams that need a general-purpose training data platform with both software and optional managed services. Source: Labelbox homepage, Labelbox docs – Get started.
• Organizations building RLHF and post-training pipelines on top of frontier LLMs and multimodal models. Source: Labelbox homepage.
• Orgs that value a transparent usage-based model (LBUs) and a generous free tier for experimentation. Source: Labelbox pricing.

V7 (Darwin)

Core positioning: A specialist computer-vision data labeling platform with strong AI-assisted annotation, medical imaging support, and compliance for regulated industries.

Key Features:

• AI-assisted annotation (Auto-Annotate, SAM/SAM2). Tools to accelerate pixel-accurate segmentation, object detection, and tracking, often cited as enabling large time savings in annotation workflows. Source: Label Your Data – V7 Darwin, Autolabelling.com – V7 Labs profile.
• Rich support for medical and scientific imaging. Handles DICOM, NIfTI, and whole-slide images with 3D rendering, MPR, and crosshair tools, making it popular among healthcare and life-science users. Source: Label Your Data – V7 Darwin, G2 – V7 Darwin features.
• Advanced quality controls and workflows. Multi-stage review, consensus workflows, labeler/task/data quality metrics often >90% in G2 feature ratings. Source: G2 – V7 Darwin features, G2 – V7 Darwin.
• Compliance and security. SOC 2, HIPAA, and ISO 27001 certifications, plus GDPR alignment, explicitly marketed for sensitive data. Source: V7 Darwin pricing/security FAQ.
• Flexible pricing and deployment. Custom quotes based on platform fee, seats, and data usage; AWS Marketplace lists a Starter plan at $9,000/year for 50k files and 3 seats. Source: V7 Darwin pricing, AWS Marketplace – V7 Darwin Starter.

Best For:

• Computer-vision teams, particularly in healthcare, manufacturing, and robotics, needing advanced annotation tools and medical file support. Source: TechCrunch – V7 funding story, Label Your Data – V7 Darwin.
• Organizations that want high-accuracy labeling with AI assistance and rigorous quality metrics. Source: G2 – V7 Darwin, G2 – V7 Darwin features.
• Teams comfortable with a visual-first workflow (images, video, documents) and less need for language-heavy RLHF tooling. Source: Autolabelling.com – V7 Labs profile.

What are the strengths and weaknesses of each platform?

Coactive.ai

Strengths:

• Excellent for making unstructured visual data actionable. G2 reviewers highlight intuitive natural-language search and automated tagging that turn huge image/video datasets into searchable assets. Source: G2 – Coactive.
• Significant operational gains for content-heavy teams. One reviewer notes Coactive “saves hours of doing manual sorting” by extracting detailed information from images and videos. Source: G2 – Coactive.
• Enterprise-grade ingestion and cost claims. Coactive cites customers ingesting 2,000 video hours/hour with 30–60% cost reductions versus traditional pipelines, and Autumn ’25 claims 50% higher zero-shot tagging accuracy. Source: Coactive – Platform, Coactive Autumn ’25 press release.
• Model-agnostic and future-proof stance. Support for many foundation models plus BYOM via Bedrock/Azure/Databricks reduces lock-in risk. Source: Coactive – Platform.

Weaknesses:

• Limited customization and export pain points. A 2025 G2 review calls out constrained customization for niche use cases and “painful” exporting that only provides simple static reports. Source: G2 – Coactive.
• Small review base and ecosystem. With just 16 G2 reviews and a relatively new platform, there’s less community knowledge and fewer off-the-shelf integrations compared with Labelbox or V7. Source: G2 – Coactive.
• Enterprise-first pricing. Published Tier 1 contract at $75k/year (plus overages) will be overkill for smaller teams that just want a labeling or MLOps tool. Source: AWS Marketplace – Coactive AI Platform.

Scale AI

Strengths:

• Breadth and depth across the AI lifecycle. Scale combines data labeling, data curation, evaluation, RLHF, and GenAI application tooling, positioning itself as a full-stack AI infrastructure provider. Source: Scale docs – Overview, Encyclopedia summary of Scale products.
• Proven at extreme scale and complexity. Scale reports hundreds of millions in annual revenue and serves frontier labs, major automakers, and the U.S. government, including DoD contracts and AI Safety Institute partnerships. Source: Scale AI – Wikipedia, Scale – Y Combinator profile, FedScoop – US AI Safety Institute collaboration.
• Strong evaluation and safety specialization. Initiatives like Humanity’s Last Exam and Scale Evaluation make it a go-to partner for rigorous benchmarking and red-teaming. Source: Scale AI – Wikipedia.

Weaknesses:

• Opaque, enterprise-only pricing. Independent comparisons note that Scale publishes no pricing and typically requires volume commitments, custom contracts, and no standard free pilot—creating friction for smaller teams. Source: Label Your Data – Scale AI pricing, Autolabelling.com – Scale profile.
• Worker-experience concerns. Reddit threads and lawsuits detail inconsistent workloads, payment glitches, and exposure to disturbing content among contractors, even though some contributors report good pay on certain projects. Source: Reddit – Scale AI post legit?, Reddit – Scale AI lawsuit, Scale AI – Wikipedia.
• Reputation volatility post-Meta deal. Meta’s 49% stake, follow-on layoffs (around 14% of staff and hundreds of contractors), and client concerns about data sharing have raised questions about long-term independence and focus. Source: Axios – Meta deal, The Verge – layoffs, .

Labelbox

Strengths:

• Highly capable, flexible labeling platform. G2 users praise Labelbox for making image, video, and text labeling “simple and fast,” with a variety of tools that help teams work collaboratively. Source: G2 – Labelbox.
• Strong GenAI and RLHF focus. Positioning as a “data factory for AI teams” emphasizes RLHF, rubric-based evaluations, and agentic trajectories, aligning Labelbox with frontier-model workflows. Source: Labelbox homepage, Labelbox docs – Get started.
• Blend of software and services. Alignerr services and Alignerr Connect allow teams to outsource complex labeling to vetted experts, with a formal quality guarantee. Source: Labelbox labeling services, Labeling services docs.
• Accessible free tier. Up to 30 users and 50 projects on the free tier make it easy for new teams to test workflows before committing. Source: Labelbox pricing.

Weaknesses:

• Learning curve and performance issues. G2 reviewers mention that the richness of tools can be overwhelming for new users and that visualization or output can be slow on large datasets. Source: G2 – Labelbox.
• Contributor experience complaints. A 2025 Reddit post describes an Alignerr/Labelbox contributor who was removed from a project and deactivated without payment for logged hours, alleging poor recourse for contractors. Source: Reddit – Alignerr and Labelbox experience.
• Cost can add up at scale. While LBUs seem cheap per unit, heavy multimodal use plus services can quickly become a large line item, especially once you add RLHF and complex QA. Source: Labelbox pricing calculator, Labelbox labeling services.

V7 (Darwin)

Strengths:

• Outstanding G2 satisfaction and quality metrics. V7 Darwin scores 4.8/5 with more than 50 reviews and quality features (labeler, task, data quality, pre-labeling) often in the mid-90% range. Source: G2 – V7 Darwin, G2 – V7 features.
• Best-in-class for complex CV workflows. Users praise its advanced annotation tools, workflow design, and automation, particularly for medical imaging and dense computer-vision tasks. Source: G2 – V7 Darwin, Label Your Data – V7 Darwin.
• Enterprise-grade compliance. SOC 2, HIPAA, and ISO 27001 support make V7 attractive for regulated industries. Source: V7 Darwin pricing/security FAQ.

Weaknesses:

• Some missing UX features and complexity. G2 reviewers mention wanting features like splitting existing polygons and better in-app file/folder manipulation, plus occasional navigation complexity. Source: G2 – V7 Darwin.
• Visual-first scope. While it does support some language tasks, the platform and marketing are heavily optimized for vision workloads; teams wanting deep RLHF for LLMs might need complementary tools. Source: Autolabelling.com – V7 Labs profile, Label Your Data – V7 Darwin.
• Pricing transparency still limited. Despite a published AWS Starter plan and free plan on G2, most enterprise deals still require custom quotes, making TCO estimation a sales conversation. Source: AWS Marketplace – V7 Darwin Starter, V7 Darwin pricing.

How do these platforms position themselves?

• Coactive.ai markets itself as a “Multimodal AI Platform (MAP)” to “maximize video & images with AI search and automated metadata,” aimed at helping media and content-rich enterprises search, classify, and monetize their content with dynamic tagging, semantic search, and analytics. Source: Coactive homepage, Coactive – Platform, Coactive – Overview.

• Scale AI positions itself as full-stack “data-centric infrastructure to accelerate the development of AI,” with pillars around the Data Engine, GenAI Platform, and evaluation/safety lab, targeting frontier labs, big tech, defense, and government customers that need industrial-scale data and evaluation. Source: Scale AI – Y Combinator profile, Scale docs – Overview, Scale resources.

• Labelbox calls itself “the data factory for AI teams,” emphasizing post-training alignment, RLHF, frontier data services, and a combined software + services approach for labs working on AGI-grade problems and Fortune 500 enterprises. Source: Labelbox homepage, Labelbox docs – Get started.

• V7 (through Darwin) describes itself as “the data labeling platform and services provider” for AI developers, focusing on accelerating computer-vision model training with AI-assisted labeling and robust workflows for industries like healthcare, retail, and manufacturing. Source: V7 seller page – G2, V7 Darwin on AWS Marketplace, Autolabelling.com – V7 Labs profile.

Which platform should you choose?

Choose Coactive.ai If:

1. You primarily need search and understanding, not raw labeling. Your main pain is finding and analyzing content across huge media libraries, and you want dynamic tagging plus semantic search rather than running large manual labeling teams. Source: Coactive – Platform, Coactive Autumn ’25 release.
2. You operate at petabyte scale. You’re ingesting thousands of video hours per hour or similar, and your budget supports a $75k+/year contract and additional usage fees. Source: Coactive – Platform, AWS Marketplace – Coactive AI Platform.
3. You want model flexibility with minimal ops burden. You’d like to mix and match foundation models and BYOM options without maintaining your own multimodal pipeline. Source: Coactive – Platform.
4. Your stakeholders include non-technical users. Marketing, editorial, or content ops teams need to self-serve with natural-language search and intuitive tagging rather than living in notebooks. Source: G2 – Coactive, AWS Marketplace – Coactive AI Platform.
5. You’re okay with a younger ecosystem. You want cutting-edge multimodal search and are willing to accept fewer third-party integrations and a smaller customer community than Labelbox/V7.

Choose Scale AI If:

1. You’re building a full-stack AI program for a large enterprise or government. You need data collection, labeling, RLHF, model evaluation, and GenAI applications under one vendor with deep experience in sensitive and defense use cases. Source: Scale AI – Wikipedia, Scale resources.
2. You have very large, heterogeneous datasets. Your workloads span language, images, video, 3D sensors, and perhaps synthetic data generation, and you want a partner already operating at that complexity. Source: Encyclopedia summary of Scale products, .
3. Rigorous evaluation and safety are non-negotiable. You need independent, battle-tested evaluation infrastructure (like SEAL and Scale Evaluation) to satisfy regulators, boards, or public-sector stakeholders. Source: Scale AI – Wikipedia.
4. You’re prepared for custom contracts and long sales cycles. You have budget and legal bandwidth for bespoke pricing, SLAs, and security reviews rather than a simple SaaS signup. Source: Label Your Data – Scale AI pricing, Autolabelling.com – Scale profile.
5. You can accept workforce-related controversy in exchange for capability. You’ve weighed the reputational and ethical questions around contractor treatment and Meta’s ownership stake and decided the technical upside is worth it. Source: Scale AI – Wikipedia, Reddit – Scale AI post legit?, Axios – Meta deal.

Choose Labelbox If:

1. You want a general-purpose training data and RLHF platform with a free tier. You’d like to prototype quickly on the free plan and then scale into LBU-based billing without switching tools. Source: Labelbox pricing, G2 – Labelbox.
2. You need both software and expert services. Your internal team can’t handle all labeling and RLHF work, and you want Alignerr managed services plus the option to hire trainers directly via Alignerr Connect. Source: Labelbox labeling services, Labeling services docs.
3. Your roadmap spans CV, NLP, and GenAI. You’re labeling images/video and also running multimodal LLM evaluations, preference ranking, and post-training tasks from a single interface. Source: Labelbox docs – Get started, Labelbox homepage.
4. You value an established ecosystem but don’t need ultra-specialized medical imaging tools. You want something more mature than Coactive but don’t need V7’s depth for radiology-style workflows. Source: G2 – Labelbox, G2 – V7 Darwin.
5. You’re okay investing in onboarding and governance. You have time to train users and design workflows to avoid the complexity and performance pitfalls some reviewers mention. Source: G2 – Labelbox.

Choose V7 If:

1. Your core problem is high-accuracy computer vision. You’re training models on images, video, documents, or medical imaging where pixel-level accuracy and complex workflows are essential. Source: G2 – V7 Darwin, Label Your Data – V7 Darwin.
2. You operate in regulated or safety-critical domains. Healthcare, industrial inspection, or security teams that need SOC 2, HIPAA, and ISO 27001 will benefit from V7’s compliance posture. Source: V7 Darwin pricing/security FAQ.
3. You want strong automation without giving up control. You’re comfortable combining AI-assisted annotation (Auto-Annotate, SAM2) with multi-stage review and consensus to cut labeling time while maintaining quality. Source: G2 – V7 Darwin features, Autolabelling.com – V7 Labs profile.
4. You prefer clear, dataset-based entry pricing. A published $9k/year Starter plan for 50k files gives you a relatively concrete starting TCO, even though larger deployments require quotes. Source: AWS Marketplace – V7 Darwin Starter.
5. You don’t need a full RLHF/GenAI “factory.” You already have LLM infrastructure elsewhere and mainly need a best-of-breed visual data platform to feed it. Source: V7 seller page – G2, Label Your Data – V7 Darwin.

Sources & links

Company Websites

• Coactive – Homepage
• Scale AI – Resources & overview
• Labelbox – Homepage
• V7 – Homepage / Go platform

Pricing Pages

• Coactive AI Platform – AWS Marketplace pricing
• Labelbox – Pricing
• Labelbox – Pricing calculator
• V7 Darwin – Pricing
• V7 Darwin – AWS Marketplace Starter plan

Documentation

• Coactive – Multimodal AI Platform
• Coactive – Product overview
• Scale – Docs overview
• Scale – GenAI Platform docs
• Labelbox docs – Get started
• Labelbox docs – Annotate overview
• Labelbox docs – Labeling services
• G2 – V7 Darwin features

G2 Review Pages

• G2 – Coactive
• G2 – Coactive product reviews
• G2 – Scale AI
• G2 – Labelbox
• G2 – V7 Darwin
• G2 – V7 seller page

Reddit Discussions

• Reddit – Scale AI post legit?
• Reddit – Scale AI lawsuit thread
• Reddit – Alignerr and Labelbox contributor experience

Additional Resources

• Coactive – Autumn ’25 Multimodal AI Platform release
• Coactive funding coverage – Forbes
• Scale AI – Wikipedia
• Scale AI – Y Combinator company profile
• Label Your Data – Scale AI vs competitors
• Label Your Data – Label Your Data vs Scale AI
• Label Your Data – V7 Darwin overview
• TechCrunch – V7 funding story
• Autolabelling.com – V7 Labs profile