OpenAI Moderation vs Azure vs Llama Guard: A 2026 Comparison
Choosing the right moderation provider is one of the most important decisions for any platform that hosts user-generated content. But comparing providers is difficult — each has different APIs, different category schemas, different pricing, and different language coverage.
In this post, we compare the three most popular options: OpenAI Moderation API, Azure AI Content Safety, and Meta's Llama Guard.
OpenAI Moderation API
OpenAI's Moderation API is the most widely used AI moderation service. It's tightly integrated with the OpenAI ecosystem and requires no additional infrastructure.
Strengths:
- Excellent accuracy for English content — consistently the best in class
- Rich category taxonomy including hate, harassment, sexual, violence, self-harm, illicit, and more
- Simple API — just send text and get scores back
- Low latency — typically 200-300ms
Limitations:
- Vendor lock-in — switching means rewriting your integration
- 32K character input limit
- Limited non-English performance — good for major European languages, weaker for others
- No image or video moderation
- Pricing: $0.0001/1K tokens (roughly $0.10 per 1K short texts)
Azure AI Content Safety
Microsoft's Azure AI Content Safety is the enterprise choice. Built into the Azure ecosystem, it offers strong compliance features and multimodal support.
Strengths:
- Enterprise-grade compliance — SOC 2, ISO 27001, HIPAA
- Supports both text and image moderation
- Custom severity thresholds and blocklists
- Good integration with other Azure services
Limitations:
- Requires Azure subscription — can be a blocker for smaller teams
- Vendor lock-in to the Microsoft ecosystem
- More complex authentication and API structure
- Pricing: $0.50-$1.00 per 1K text records, higher than OpenAI
Llama Guard (HuggingFace)
Meta's Llama Guard is a purpose-built safety classification model available on HuggingFace. It's open source, free to use, and can run on your own hardware.
Strengths:
- 100% free — no API costs, no usage limits
- Runs locally — zero data leaves your infrastructure
- Open source — auditable, customizable
- No vendor lock-in
Limitations:
- Requires GPU hardware for reasonable latency
- 1-2 second latency on GPU, much slower on CPU
- English-only in practice
- No managed API — you need to host it yourself or use HF Inference API
- Less rich category taxonomy compared to OpenAI
Head-to-head comparison
| Criterion | OpenAI | Azure | Llama Guard |
|---|---|---|---|
| English accuracy | Best | Good | Good |
| Non-English support | Good (major langs) | Good | Limited |
| Latency | ~250ms | ~300ms | ~1.5s (GPU) |
| Cost per 1K requests | ~$0.10 | $0.50-$1.00 | $0 (self-hosted) |
| Self-hostable | No | No | Yes |
| Open source | No | No | Yes |
The smart approach: use all three
The best moderation strategy isn't picking one provider — it's using multiple providers together. Use OpenAI for English content with rich categories. Use Azure for enterprise compliance requirements. Use Llama Guard for zero-cost, zero-data-leakage moderation of sensitive content.
This is exactly what OpenModeration enables. One API, all providers, smart routing that picks the best option for each request. Change your provider strategy without changing your code.