AI Content Moderation for LLM Applications
Filter LLM inputs and outputs. Protect users from harmful model responses and your model from malicious prompts with a complete moderation harness.
The Challenge
LLM-powered applications are vulnerable to prompt injection, jailbreaks, and generating harmful content. Relying on a single provider's built-in filters creates a single point of failure.
The OpenModeration Solution
Defense in depth. Layer multiple AI providers for both input and output filtering. Use the Action Engine to define custom safety policies without writing complex moderation logic.
The Result
Ship your AI application with confidence. Maintain strict policy compliance, prevent brand-damaging outputs, and keep your development team focused on building features, not moderation infrastructure.
The fastest-growing moderation need
Every LLM-powered application needs content moderation on two fronts:
Input filtering: Block harmful, illegal, or manipulative user prompts before they reach the LLM.
Output filtering: Catch dangerous or inappropriate model responses before users see them.
OpenModeration lets you layer multiple providers 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 GPT-4, Claude...
-> 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. Swap providers without code changes.
Defense in depth
Layer a fast classifier with an LLM check on the same request. If one provider fails or lags, routing falls back automatically.
Custom categories
Define arbitrary safety policies through natural-language prompts with any LLM — no classifier retraining.
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?
Classifier-based providers 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 — medical advice boundaries, competitor mentions, age-inappropriate content, and more.
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 complete moderation harness beats a standalone API for AI safety.
Explore All Features
Discover the Action Engine, team management, and automated workflows.
Ship your AI app with a safety harness.
Filter inputs and outputs through one API, with multi-provider fallback and full audit trails. Self-host or use the managed cloud.
No credit card required
Questions? contact@openmoderation.com