AI Content Moderation for LLM Applications
Filter what goes into your LLM and what comes out. OpenModeration protects users from harmful model responses and protects your model from malicious prompts.
The Challenge
LLM-powered applications are exposed to prompt injection and jailbreaks, and they can generate harmful content. If you rely only on your model's built-in filters, you depend on a single, frozen safety layer.
The OpenModeration Solution
Our own moderation AI filters both inputs and outputs, and other AI models cross-check delicate cases. The Action Engine lets you define your own safety policies without writing moderation logic yourself.
The Result
You ship your AI application knowing it follows your rules and won't produce outputs that hurt your brand. Your developers keep working on features instead of moderation infrastructure.
The fastest-growing moderation need
Every LLM-powered application needs content moderation on two fronts:
Block harmful, illegal or manipulative user prompts before they reach the LLM.
Catch dangerous or inappropriate model responses before users see them.
OpenModeration cross-checks delicate cases with other AI models for defense in depth, all managed from a single dashboard.
Input → Moderated → LLM
User prompt -> {"input": prompt} -> POST /v1/moderation ->
-> if not flagged -> forward to your LLM
-> if flagged -> return "Content policy violation"LLM Output → Moderated → User
LLM response -> {"input": response} -> POST /v1/moderation ->
-> if not flagged -> display to user
-> if flagged -> show fallback responseDesigned for AI applications
Everything an LLM-powered product needs to moderate both sides of the conversation.
Input filtering
Block prompt injection, jailbreaks, and harmful prompts before they ever reach your model.
Output filtering
Catch dangerous or off-policy model responses before users see them. When we plug in a better model, nothing changes in your code.
Defense in depth
A fast first check by our moderation AI, then arbitration by other AI models on delicate cases, on the same request.
Custom categories
Define arbitrary safety policies through natural-language prompts with any LLM, without retraining a classifier.
LLM moderation FAQs
Can I moderate both prompts and model responses with the same integration?
Yes. Call POST /v1/moderation on the user's prompt before forwarding to your LLM, then again on the model's response before displaying it. Same endpoint, same response schema.
Will moderation add noticeable latency to my AI application?
Most requests return in a few hundred milliseconds, well under typical LLM response times. You can also run output moderation asynchronously and stream a disclaimer first, then revoke if the content is flagged.
Can I define policies specific to my product?
Yes. Beyond the built-in categories, use LLM-based moderation with custom prompts to enforce your own rules, such as limits on medical advice, competitor mentions or age-inappropriate content.
Related Resources
Content Moderation for AI Applications
Best practices for filtering inputs and outputs in LLM-powered apps.
OpenModeration vs Azure Content Safety
Why a full moderation platform does more for AI safety than a standalone API.
Explore All Features
Discover the Action Engine, team management, and automated workflows.
Ship your AI app with moderation built in.
Filter inputs and outputs through one API, with cross-checked AI decisions and full audit trails, hosted in the EU.
No credit card required · DSA-compliant · GDPR-compliant
Questions? contact@openmoderation.com