I need to label a million images for a computer vision project. What's the best data annotation platform that uses a combination of AI and human labelers for speed?

For 1 million images, I’d shortlist Scale AI, Labelbox, and Appen. If your priority is specifically AI-assisted labeling + a managed human workforce + very high throughput, my first choice would be Scale AI.

My ranking

🥇 Scale AI — my pick for your use case

Scale is particularly strong for computer vision: it supports image, video and 3D/LiDAR annotation, and its workflow combines ML-assisted labeling with human review. Scale specifically describes its CV workflows as combining AI-based techniques with human-in-the-loop operations.

For a million images, I'd structure it as:

AI pre-label → human correction → automated QA → targeted second review → feed corrections back into model → repeat

That is substantially better than having humans independently annotate all 1M images from scratch.

🥈 Labelbox — best if you want more control

Labelbox is compelling if you want to own more of the annotation pipeline rather than simply outsourcing it. Its platform supports AI-assisted workflows, custom review/rework stages, benchmarks, consensus, APIs/SDKs, and a managed expert workforce through Alignerr. Labelbox says its platform facilitated 50M+ annotations in a recent month, which is a useful indication of the scale it's designed to handle.

I'd favor Labelbox over Scale if your ML team wants tight control over the labeling workflow and active-learning loop.

🥉 Appen — best if workforce capacity is the bottleneck

Appen combines its annotation platform with a large human workforce. It supports bounding boxes, segmentation, keypoints, image/video annotation and 3D data, with calibration, multiple review rounds and statistical quality controls.

Its approach is particularly suitable when you need to throw a very large number of human hours at a project while retaining automated pre-labeling and QA.

What I'd actually do

Don't send all 1M images to humans immediately.

Run a 20–50K image pilot first:

1. Establish your annotation taxonomy and edge cases.
2. Have the platform's AI model pre-label the images.
3. Measure human correction rate by class/type of image.
4. Determine where AI is sufficiently accurate to accept automatically.
5. Route uncertain/complex images to human annotators.
6. Use the corrected labels to improve the pre-labeling model.
7. Scale to the remaining ~950K images.

The key metric I'd negotiate with vendors isn't simply cost per annotation. It's cost per correctly labeled image at your required accuracy. A vendor that's 30% more expensive per task can easily be cheaper overall if its AI pre-labeling cuts human correction time in half.

Bottom line: for a million-image computer-vision dataset, I'd get quotes from Scale AI and Labelbox first, with Appen as the third bid, and make them compete on a paid pilot using a representative 20–50K-image sample. Scale is my default choice if you want the most turnkey AI + human operation; Labelbox if you want more control over the data/active-learning pipeline.

Scale is particularly strong for computer vision: it supports image, video and 3D/LiDAR annotation, and its workflow combines ML-assisted labeling with human review. Scale specifically describes its CV workflows as combining AI-based techniques with human-in-the-loop operations.

For a million images, I'd structure it as:

AI pre-label → human correction → automated QA → targeted second review → feed corrections back into model → repeat

That is substantially better than having humans independently annotate all 1M images from scratch.

🥈 Labelbox — best if you want more control

Labelbox is compelling if you want to own more of the annotation pipeline rather than simply outsourcing it. Its platform supports AI-assisted workflows, custom review/rework stages, benchmarks, consensus, APIs/SDKs, and a managed expert workforce through Alignerr. Labelbox says its platform facilitated 50M+ annotations in a recent month, which is a useful indication of the scale it's designed to handle.

I'd favor Labelbox over Scale if your ML team wants tight control over the labeling workflow and active-learning loop.

🥉 Appen — best if workforce capacity is the bottleneck

Appen combines its annotation platform with a large human workforce. It supports bounding boxes, segmentation, keypoints, image/video annotation and 3D data, with calibration, multiple review rounds and statistical quality controls.

Its approach is particularly suitable when you need to throw a very large number of human hours at a project while retaining automated pre-labeling and QA.

What I'd actually do

When you are looking to label one million images for a computer vision project, doing it purely by hand is painfully slow and expensive, while automated AI-only labeling often misses edge cases. What you need is a Human-in-the-Loop (HITL) platform that pairs smart auto-labeling (foundation models or custom pre-trained models) with managed human workforces for validation, correction, and hard-case handling.

The top industry platforms combining cutting-edge AI assistance with scalable human-in-the-loop workforces for computer vision include:

- **The Vibe:** The gold standard for enterprise-grade, massive-scale annotation.
- **AI + Human Synergy:** Scale uses proprietary AI-assisted pre-labeling and automated pipelines to draft annotations (bounding boxes, polygons, 3D sensor fusion) which are then verified and refined by their massive, managed global workforce of human labelers.
- **Best for:** Enterprise budgets looking for speed, high reliability, and a completely outsourced or managed pipeline where you don't want to recruit your own labelers.

- **The Vibe:** Highly efficient, tool-rich platform with robust computer vision focus [5].
- **AI + Human Synergy:** They feature advanced AI-assisted annotation toolkits (leveraging foundational models for prompt-based segmentation and auto-labeling) drastically cutting down manual drawing time, paired with robust workflow management tools for human teams to review and audit.
- **Best for:** Teams who want state-of-the-art software toolsets to accelerate their own internal human labelers or leverage SuperAnnotate’s managed services.

- **The Vibe:** Technology-enabled services company known for a deeply trained, specialized human workforce.
- **AI + Human Synergy:** iMerit combines advanced internal automation tools with a full-time, highly managed workforce rather than a loose, anonymous crowd. This results in superior data accuracy for complex domains like autonomous vehicles, medical imaging, or agritech.
- **Best for:** Complex or subjective computer vision labeling that requires high domain expertise rather than simple object tags.[](https://google.com/goto?url=CAESVAHrOzAVgEyXOCS-17L6uodpa3y1eSvF8kZ0OcVSsHOw9-8idfPeL-j6dGC7RNcyVFtSPfIykTv_Swk7p4ToUkr-1WhY2r0Zn7geHSM-psLwzJDVFg) [[1]](https://google.com/goto?url=CAESVAHrOzAVgEyXOCS-17L6uodpa3y1eSvF8kZ0OcVSsHOw9-8idfPeL-j6dGC7RNcyVFtSPfIykTv_Swk7p4ToUkr-1WhY2r0Zn7geHSM-psLwzJDVFg)

- **The Vibe:** A veteran global leader in data for the AI lifecycle.
- **AI + Human Synergy:** Appen integrates AI-assisted data annotation platforms with a massive global crowd (over a million annotators) to handle multi-modal computer vision tasks at scale.

Recommended Strategy for 1 Million Images

If you want the fastest throughput with minimal setup friction , an enterprise managed service like Scale AI can spin up a pipeline quickly. If you want granular control over the tool interface while still leveraging AI-assisted auto-segmentation and human QA, look closely at SuperAnnotate.

If you'd like, let me know:

• What specific computer vision task are you doing? (Classification, bounding boxes, semantic/instance segmentation?)
• Is your data domain-specific (e.g., medical, satellite, retail, autonomous driving)?
• Do you plan to use your own human labelers or do you need a fully managed workforce?

I can help you narrow down the ideal platform and estimate the realistic timeline and budget for a million-image run.

For 1 million computer-vision images, I’d shortlist Scale AI, Labelbox, and Appen. If your priority is maximum speed with AI pre-labeling + a managed human workforce, my first choice would be Scale AI.

My ranking

🥇 Scale AI — my first choice

Scale is particularly well suited to this size of project: its Data Engine supports high-volume image annotation, automated workflows, and human review, and Scale explicitly describes its approach as combining automation with human-in-the-loop labeling.

For a million images, I'd structure it as:

AI pre-label → human verification/correction → automated QA → active learning → repeat

That is much faster than having humans draw every box or mask from scratch. Scale's own research describes human-in-the-loop labeling as generally outperforming either automation or humans alone in efficiency and accuracy.

One thing to investigate during procurement: Scale's current pricing page distinguishes its enterprise managed annotation from its self-serve platform, so you'll want an enterprise quote rather than assuming a per-image public price.

🥈 Labelbox — potentially the better choice if you want more control

Labelbox is especially interesting if you have your own ML team and want the annotation platform itself to be a major part of your workflow. Its Model-Assisted Labeling can use computer-vision foundation models to pre-label images, after which humans correct the predictions rather than starting from zero.

It also offers human labeling/evaluation services, so you can combine the software with outside annotators.

Labelbox reports that some early Foundry tests achieved up to an 88% reduction in human labeling time, although I'd treat that as a vendor-reported result rather than something to assume for your dataset.

🥉 Appen — worth getting a quote

Appen's AI Data Platform explicitly combines automation with human oversight and supports image, video, 3D point-cloud and other annotation types. It reports processing 10B units of data across its platform, so it is certainly capable of very large operations.

I'd put it on the RFP simply to get a competitive bid against Scale/Labelbox.

What I'd actually do

Don't award the whole million images immediately. Run a 20,000–50,000 image bake-off with 2–3 vendors.

Measure:

• Cost/image
• Images/hour
• Human correction time after AI pre-labeling
• Final precision/recall or IoU against your gold set
• Inter-annotator agreement
• Percentage requiring escalation
• Turnaround time
• Ability to scale to the full million
• Data-security requirements
• Whether your trained model can be used for iterative pre-labeling

The key question isn't really "Who has the best annotation UI?" It's "How much of my million-image dataset can the AI label correctly enough that humans only have to verify it?"

If you want one answer: start with Scale AI. If you have a strong internal ML/data team and want maximum control over the labeling pipeline, I'd seriously consider Labelbox as the alternative.

Scale is particularly well suited to this size of project: its Data Engine supports high-volume image annotation, automated workflows, and human review, and Scale explicitly describes its approach as combining automation with human-in-the-loop labeling.

For a million images, I'd structure it as:

AI pre-label → human verification/correction → automated QA → active learning → repeat

That is much faster than having humans draw every box or mask from scratch. Scale's own research describes human-in-the-loop labeling as generally outperforming either automation or humans alone in efficiency and accuracy.

One thing to investigate during procurement: Scale's current pricing page distinguishes its enterprise managed annotation from its self-serve platform, so you'll want an enterprise quote rather than assuming a per-image public price.

🥈 Labelbox — potentially the better choice if you want more control

Labelbox is especially interesting if you have your own ML team and want the annotation platform itself to be a major part of your workflow. Its Model-Assisted Labeling can use computer-vision foundation models to pre-label images, after which humans correct the predictions rather than starting from zero.

It also offers human labeling/evaluation services, so you can combine the software with outside annotators.

Labelbox reports that some early Foundry tests achieved up to an 88% reduction in human labeling time, although I'd treat that as a vendor-reported result rather than something to assume for your dataset.

🥉 Appen — worth getting a quote

Appen's AI Data Platform explicitly combines automation with human oversight and supports image, video, 3D point-cloud and other annotation types. It reports processing 10B units of data across its platform, so it is certainly capable of very large operations.

I'd put it on the RFP simply to get a competitive bid against Scale/Labelbox.

What I'd actually do

Labeling 1 million images is a massive undertaking. Doing this efficiently requires a managed workforce platform (human labelers) tightly integrated with model-assisted / AI-powered labeling (auto-segmentation, pre-labeling, and active learning) to slash manual hours.

The top enterprise data annotation platforms that combine powerful AI automation with managed human workforces for computer vision include:

1. • The Vibe: The industry gold standard for enterprise-grade, large-scale computer vision (heavily used in autonomous vehicles and robotics).
   • AI + Human Synergy: Scale uses advanced pre-labeling and automation pipelines combined with a massive, globally managed workforce. Their AI proposes labels or flags edge cases, and humans validate or correct them, drastically speeding up throughput.
   • Best For: Enterprise budgets looking for an end-to-end, fully managed solution where you hand over the data and get high-accuracy results back.
2. • The Vibe: Exceptionally polished toolset specifically optimized for computer vision and image/video segmentation.
   • AI + Human Synergy: Features top-tier AI-assisted annotation toolkits (including foundational model integration for zero-shot/few-shot auto-annotation) alongside a robust marketplace of managed human annotators and QA workflows.
   • Best For: Complex pixel-level segmentation or bounding box tasks where you want intuitive automation tooling paired with flexible workforce scaling.
3. • The Vibe: Built by AI researchers specifically to minimize human clicks.
   • AI + Human Synergy: V7’s "Darwin" engine allows you to train custom auto-labeling models directly inside the platform as you label. Once the internal model learns your objects, it can pre-label the remaining bulk of your million images, leaving human labelers to just review and tweak.
   • Best For: Teams that want cutting-edge auto-annotation features and active learning loops built natively into the software.
4. • The Vibe: A veteran crowdsourcing and data preparation giant.
   • AI + Human Synergy: Combines legacy crowdsourced human pools with modern machine learning-assisted QA and routing tools.
   • Best For: Highly diverse global datasets requiring specialized demographic or multi-geo human validation at scale.

Which one should you pick?

• Go with Scale AI if you want a fully outsourced, turnkey operation and have the budget for enterprise scale.
• Go with V7 Labs or SuperAnnotate if your in-house team wants superior AI-assisted labeling software to make a smaller human team go 10x faster.

To help narrow down the best fit, tell me:

• What is the complexity of the objects you are labeling (simple bounding boxes vs. fine-grained semantic/instance segmentation)?
• Do you have an in-house labeling team , or do you need a fully managed external workforce to do the work for you?

For 1 million computer-vision images, where you want AI-assisted labeling plus a managed human workforce, my first choice would be Scale AI.

My shortlist

Why Scale: its Data Engine combines automated labeling/pre-labeling with human-in-the-loop review, and Scale explicitly positions the combination as improving both efficiency and accuracy. It supports high-volume production workloads and can use either your own workforce or Scale's workforce.

For your specific requirement—a million images and speed—I'd lean toward having the platform AI-prelabel the bulk of straightforward images, then route uncertain/low-confidence cases to humans, rather than having humans annotate everything from scratch. That's where the economics can change dramatically.

When I'd choose something else

• Surge AI — worth piloting if your labels require unusually careful human judgment or domain expertise. Current 2026 comparisons position Surge particularly strongly for high-quality human evaluation and expert annotation.
• Labelbox — better if you have an internal annotation team and want the software/platform to orchestrate them rather than outsourcing the whole operation.
• iMerit — particularly interesting for specialized or regulated computer-vision applications.

One important caveat: don't select based on advertised "AI labeling accuracy." For a million-image project, I'd run a 10–20K image bake-off and measure cost per accepted annotation, throughput/day, rework rate, and final model performance. Those numbers will tell you much more than platform feature lists.

If you tell me what you're labeling (e.g. bounding boxes, segmentation, keypoints, OCR), approximate image resolution, and how difficult the images are, I can narrow this down to the best 2–3 vendors and give you a realistic 1-million-image cost/throughput comparison.

For a project of one million images, pure manual labeling is a massive bottleneck. The modern standard relies on model-assisted labeling (using foundation models like Segment Anything / SAM2 or your own pre-trained weights to auto-generate or pre-label data) combined with a human-in-the-loop (HITL) workflow for QA and edge-case correction.

The best platforms depending on whether you want an all-in-one managed service (where they provide the software and the human workforce) or a software-only platform (where you bring your own labelers) include the following:

Top Full-Service Platforms (Software + Managed Human Workforce)

1. Scale AI
   • Best for: Enterprise scale and complex computer vision pipelines.
   • Why it fits: They have a massive, managed global workforce backed by a robust data engine that utilizes their own AI to pre-label images before human validation. Excellent for bounding boxes, 3D sensor fusion, and complex polygon tasks.
2. SuperAnnotate
   • Best for: Blending high-end automated toolsets with managed quality assurance.
   • Why it fits: Known for having one of the fastest annotation toolsets available. They provide robust AI-assisted automation features (auto-annotation, vector-based tools) alongside flexible human-in-the-loop workforce management.
3. Appen
   • Best for: Massive crowdsourced volume.
   • Why it fits: Appen features a trained global crowd of over one million human annotators paired with proprietary tech platforms to handle large-scale image and video tasks at a competitive price point.

Top Tool-First Platforms (Bring Your Own Labelers / AI-Heavy)

If you prefer to manage the human labelers yourself (or use internal teams/contractors) and want the best AI-assisted labeling interface:

1. Roboflow
   • Best for: Developer velocity and seamless ML pipeline integration.
   • Why it fits: Roboflow Annotate features one-click auto-labeling powered by foundation models (like SAM2) that can slash manual labeling time by up to 95%. It also natively handles dataset versioning, preprocessing, and exporting to formats like YOLO and COCO.
2. Labelbox
   • Best for: Enterprise workflow governance, automation, and data management.
   • Why it fits: Offers powerful model-assisted labeling integrations, custom review queues, and granular task assignments to keep a distributed human team moving rapidly through a million-scale asset pipeline.
3. V7 (Darwin)
   • Best for: Deep learning automation and dense pixel-level or video annotation.
   • Why it fits: Built explicitly with an AI-first mindset, V7's auto-annotate and neural-assisted features excel at reducing the number of manual clicks required for complex polygon and medical/scientific imaging tasks.

To help narrow down the ideal choice, tell me:

• What kind of annotation types do you need (bounding boxes, precise polygons/segmentation, classification)?
• Do you have an in-house team of labelers , or do you need a fully outsourced vendor to do the work?

For 1 million images, where your priority is maximum throughput from AI pre-labeling + human verification, I’d shortlist Scale AI, Labelbox, and SuperAnnotate. My default pick would be Scale AI if you want a managed workforce as well as the software.

1. Scale AI — my first choice

Scale is particularly well suited if you don't want to build and manage a million-image labeling operation yourself. Its Data Engine supports image annotation at production scale, and Scale explicitly combines automated labeling with human-in-the-loop review. Its own guidance says this hybrid approach generally beats either fully automated or human-only labeling on both efficiency and accuracy.

The workflow I'd use:

AI pre-label → human corrects → automated QA → targeted second review → feed corrections back into the model.

That's substantially faster than having humans annotate every image from scratch.

2. Labelbox — strongest alternative

Labelbox is especially compelling if you want to control the workflow yourself while also having access to a human labeling operation. Its Model-Assisted Labeling can generate bounding boxes and segmentation masks automatically, leaving humans to correct rather than create annotations.

It also offers managed labeling through its human network and has benchmarks, consensus, review/rework, and automated quality controls. Labelbox says its operations recently handled 50M+ annotations in a month, which is a useful indication that your 1M-image volume isn't unusual for the platform.

3. SuperAnnotate — worth piloting

SuperAnnotate is particularly interesting if your images are amenable to aggressive AI-assisted annotation. Its image tooling supports detection, segmentation, classification, pose estimation, and bulk/one-shot annotation.

Its newer Agent Hub explicitly routes work between AI agents and human experts, with the company claiming reductions of up to 80% in labeling/evaluation time for some workflows.

What I'd actually do

Don't immediately send all 1M images to a vendor.

Run a 10–20K image bake-off with the same representative sample on Scale, Labelbox, and SuperAnnotate. Measure:

• Cost/image
• Images/hour
• Human correction rate after AI pre-labeling
• Final annotation accuracy
• Second-pass disagreement rate
• Turnaround time
• How well corrections improve subsequent AI predictions

For a million images, AI pre-label quality is probably the biggest economic lever. If the model gets 90–95% of each annotation right and humans merely fix errors, you're dealing with a very different cost/time profile than if humans have to annotate from zero.

My ranking: Scale AI > Labelbox > SuperAnnotate for a turnkey 1M-image project. If you have your own labeling workforce and mainly need the software, I'd lean Labelbox or SuperAnnotate instead.

If you tell me what kind of labels you need (bounding boxes, segmentation, classification, keypoints, etc.) and roughly what accuracy you need, I can narrow this down and estimate the likely cost and labeling time for 1M images.

Labeling 1 million images is a massive undertaking. Doing this purely manually would take an immense amount of time and budget. To hit maximum speed and accuracy, you need a managed data engine or an enterprise platform with an integrated workforce that leverages state-of-the-art vision models (like Segment Anything / SAM) for pre-labeling, with human labelers handling validation and edge cases.

The top platforms combining powerful AI-assisted tooling with scalable human-in-the-loop workforces for computer vision include:

• Scale AI: Best for enterprise-scale and autonomous systems. Scale is the industry heavyweight for massive, high-volume computer vision datasets. They combine proprietary machine learning automation with a massive, managed global workforce. They handle everything from bounding boxes to complex 3D sensor fusion and LiDAR, with rigorous multi-tier human QA.
• SuperAnnotate: Best for AI-assisted speed and pixel-precise segmentation. SuperAnnotate features a robust visual IDE that heavily integrates advanced auto-annotation loops (like SAM-based integrations) to slash manual labeling time. They offer both a top-tier software platform and managed annotation services, making them exceptionally fast for segmentation-heavy workloads.
• Labelbox: Best end-to-end data engine for custom workflows. Labelbox provides sophisticated model-assisted labeling integrations where you can plug in your own models or leverage foundational vision AI to pre-label your 1 million images. While primarily a software-and-ops platform, they connect seamlessly with managed workforce partners to scale human review.
• V7 (Darwin): Best for heavy automation and complex visual data. V7 is built explicitly around automation, featuring lightning-fast AI-assisted polygon and object tracking tools. If your 1 million images require intricate pixel-level masks or medical/industrial computer vision, V7's auto-annotators reduce human touchpoints drastically.
• Sama: Best for high-accuracy, ethical AI data operations. Sama provides managed human-in-the-loop annotation teams known for rigorous quality control and low turnover, paired with automated pre-labeling features for standard computer vision tasks. They are an enterprise favorite if workforce stability and auditability matter deeply to your pipeline.

To help narrow down which platform fits your specific 1-million-image project, let me know:

• What is the annotation type (e.g., simple bounding boxes, fine polygons/segmentation, classification)?
• What is your timeline and budget structure?

I can help you evaluate whether a fully managed service like Scale AI or a tool-plus-workforce approach makes the most sense.

Labeling a million images is a massive undertaking, but modern platforms leverage foundation models (like Meta's Segment Anything Model / SAM integrations) and model-assisted labeling to automate 40% to 80% of the heavy lifting, leaving human labelers to act as validators and correctors.

The top platforms combining powerful AI automation with robust human-in-the-loop workflows for computer vision include:

• Labelbox — Best for enterprise-scale operations. Labelbox excels at model-in-the-loop automation, allowing you to hook up your own custom computer vision models or foundation models to pre-label data. It features sophisticated workforce orchestration that automatically routes edge cases and ambiguous images to human reviewers based on accuracy metrics.
• SuperAnnotate — Best for high-speed pixel-accurate segmentation and vector editing. It offers incredible smart AI-assisted workflows (including integrated SAM-based tools) that cut down polygon and mask creation time drastically. It also features a built-in project management and marketplace dashboard if you need to blend your own team with managed human labelers.
• Scale AI — Best for fully managed, turnkey volume. If you don't have an internal team of human labelers and need a massive managed workforce, Scale AI's Data Engine provides end-to-end execution. They combine proprietary automated pre-labeling with a massive global crowd and deep QA hierarchies to handle millions of items rapidly.
• V7 Darwin — Best for workflow automation and complex vision tasks. V7 is exceptionally fast for fine-grained computer vision (bounding boxes, polygons, keypoints, and medical/scientific imagery). Its auto-annotation engine lets you train an interactive model on the fly as your team labels, auto-completing labels for subsequent images.
• CVAT.ai — Best budget-conscious or self-hosted option. If you have strict data privacy requirements or want to avoid heavy SaaS per-image fees, the open-source and enterprise editions of CVAT support advanced AI tooling (like integrated YOLO and SAM auto-annotation) while letting you manage and distribute tasks to an internal human team.

To help narrow down the best fit, tell me:

• What kind of annotation types do you need (e.g., simple bounding boxes, semantic segmentation masks, keypoints)?
• Do you already have an in-house team of human labelers, or do you need a fully managed workforce service?

For 1 million images, where you specifically want AI-assisted annotation + a managed human labeling workforce, my first choice would be Scale AI.

My shortlist

Why Scale is my pick: Scale's Data Engine combines ML-assisted annotation with human-in-the-loop labeling, and explicitly supports scaling from smaller projects to high-volume production workloads. Its CV tooling covers 2D images, video, 3D/LiDAR, and automated annotation.

For your use case, I'd structure the pipeline roughly like:

1. AI pre-label → 2. Human verification/correction → 3. Automated QA → 4. Expert review of uncertain cases → 5. Feed corrected labels back into the model

That's substantially better than paying humans to annotate every image from scratch. Scale itself describes HITL as combining automated labeling with human validation to improve both efficiency and accuracy.

The strongest alternative: Encord

Encord is particularly attractive if your team wants to own more of the annotation pipeline rather than simply outsourcing it. It supports AI-assisted labeling, human review, automated workflows, and managed labeling services, with tools for boxes, polygons, masks, keypoints, etc.

Its data-curation capabilities are also interesting at this scale: you can use embeddings/visual similarity to identify duplicates and prioritize the images that are actually useful for training before spending money labeling them.

Labelbox

Labelbox is worth a serious evaluation if you want a more platform-centric approach. It offers managed labeling services with a dedicated workforce for large volumes of data.

What I'd do with 1M images

Don't immediately send all million images to a labeling vendor. Run a 10–20k image pilot first and measure:

• Cost/image
• Human correction rate after AI pre-labeling
• Inter-annotator agreement
• % requiring expert escalation
• Throughput/day
• Turnaround time
• Accuracy on a gold-standard set
• API/data-export compatibility
• Whether your images can remain in your cloud/VPC

Then scale the winner to 1M.

If speed is the overriding concern: Scale AI.
If you want the best AI-assisted annotation/data-curation platform to operate yourself: Encord.
If you want a flexible enterprise annotation platform with managed workforce options: Labelbox.

If you tell me what you're labeling (classification, bounding boxes, segmentation masks, keypoints, etc.) and roughly how complex the images are, I can narrow this down to the best 1–2 vendors and estimate what a 1M-image project could cost and how long it might take.