Understanding Compliance Tags For Generative Ai Stock
Stock photography earnings are determined by metadata quality above all else. The keywords, titles, and descriptions attached to your files dictate whether buyers find your work. With over 400 million files on Adobe Stock alone, the difference between page 1 and page 87 is almost entirely metadata.
This guide covers everything stock contributors need to know about compliance tags for generative ai stock, with specific examples and platform rules.
Platform-by-Platform Breakdown
| Platform | Max Keywords | Title Limit | Key Rule |
|---|---|---|---|
| Adobe Stock | 45 | 70 chars | Order by relevance; first 10 matter most |
| Shutterstock | 50 | 200 chars | Anti-spam filter; no stuffing |
| Getty Images | 50 | 250 chars | Controlled vocabulary required |
| Pond5 | 50 | 100 chars | Include format/resolution for video |
Getty Images uses a controlled vocabulary system. Keywords must match their approved taxonomy. Freeform tags that work on Adobe Stock may get rejected on Getty. Built-in Getty compliance saves hours of manual vocabulary matching.
The Data-Driven Approach
Buyer search data reveals that 73% of stock photo purchases come from multi-word queries (3+ words). Single-word tags like 'sunset' or 'office' generate impressions but not conversions. Compound phrases matching project briefs drive actual sales.
The fundamental limitation of image-recognition-based keywording is that it answers the wrong question. It asks 'what is in this image?' when buyers ask 'what project am I building with this image?' CyberStock bridges that gap with real purchase query data.
Practical Steps
- Start with buyer intent: What problem does this image solve for a buyer?
- Use exact-match compound phrases: 'Female entrepreneur laptop' and 'woman with laptop' are different queries.
- Optimize per platform: Adobe, Shutterstock, Getty have different rules.
- Prioritize first 10 keywords: On Adobe Stock, early keywords carry more ranking weight.
- Re-keyword existing portfolio: Improving metadata on existing files is faster than uploading new ones.
One contributor documented their results after switching to CyberStock: monthly earnings went from $40 to $380 within 90 days — same portfolio, same platforms, only the metadata changed.
Common Mistakes to Avoid
- Keyword stuffing: Adding 50 generic single-word tags hurts more than it helps. Stock agencies penalize files with irrelevant or repetitive keywords.
- Ignoring title optimization: The title field carries significant ranking weight on both Adobe Stock and Shutterstock. A descriptive, buyer-intent title outperforms generic ones.
- Same metadata across platforms: Adobe Stock, Shutterstock, and Getty have different keyword limits, ordering rules, and compliance requirements. Copy-pasting the same metadata everywhere underperforms.
- Not updating old files: Your existing portfolio has the most leverage. Re-keywording 1,000 existing files produces faster results than uploading 1,000 new files with generic metadata.
- Descriptive instead of commercial keywords: Tagging what you see in the image instead of what buyers search for is the most common earnings killer.
Buyer search data reveals that 73% of stock photo purchases come from multi-word queries (3+ words). Single-word tags like 'sunset' or 'office' generate impressions but not conversions. Compound phrases matching project briefs drive actual sales.
How CyberStock Automates This
The fundamental limitation of image-recognition-based keywording is that it answers the wrong question. It asks 'what is in this image?' when buyers ask 'what project am I building with this image?' CyberStock bridges that gap with real purchase query data.
The combination of buyer-data keywords, per-platform compliance, and CyberPusher FTP distribution creates a complete workflow: keyword your files, export platform-specific CSVs, and distribute to all agencies in under 30 minutes for a 1,000-file batch.
Buyer-Intent Keywords
50M+ real purchase queries as training data
1.33s Per File
10,000 photos in a single session
Selling Score
Predict earnings before upload
CyberPusher FTP
0% commission distribution
Frequently Asked Questions
How does CyberStock generate keywords differently?
Most tools analyze images visually. CyberStock cross-references visual analysis against 50 million real buyer purchase queries from Adobe Stock, Shutterstock, and Getty. The result: keywords with verified commercial demand.
Which stock marketplaces does CyberStock support?
Adobe Stock, Shutterstock, Getty Images, iStock, Pond5, 123RF, Depositphotos, and custom FTP endpoints. Compliance rules for each platform are built in.
How fast is processing?
Approximately 1.33 seconds per file. A 1,000-photo batch completes in about 22 minutes. Up to 10,000 files per session.
Does it work for video?
Yes. Photos, 4K video, vectors, and illustrations. Each file type gets optimized metadata for its format.
What is the Selling Score?
A pre-upload earnings prediction based on current market demand, competition, and buyer trends. Prioritize your strongest content before uploading.
Related Guides
Try CyberStock Free — 20 Credits, No Card
AI keywords trained on 50M+ real buyer searches. Adobe Stock, Shutterstock, Getty. See the difference in your first batch.
Generate Keywords Free →