Comparing OpenAI Moderation API and Llama Guard

The choice between OpenAI Moderation API vs Llama Guard comes down to ownership, latency budget, and whether you need to inspect the reasoning behind a flag. Both classify text into safety categories, but one is a hosted black-box service and the other is an open-weight model you run yourself. If you are shipping a guardrail in production, the deployment model matters more than the marginal accuracy difference on a benchmark.

Capabilities

What OpenAI Moderation API actually returns

The OpenAI Moderation API exposes a fixed taxonomy: hate, hate/threatening, harassment, harassment/threatening, self-harm, self-harm/intent, self-harm/instructions, sexual, sexual/minors, violence, violence/graphic. Each call returns a flagged boolean, per-category booleans, and per-category scores between 0 and 1. You cannot add a category, adjust thresholds per category beyond client-side logic, or ask why something flagged.

from openai import OpenAI
client = OpenAI()
res = client.moderations.create(input="kill them all")
result = res.results[0]
print(result.flagged)
print(result.category_scores.violence)

What Llama Guard gives you

Llama Guard is a fine-tuned Llama-2-7b (and later 13b) model. It outputs a short structured string: safe or unsafe followed by a list of violated category codes (e.g., O1 for violence). Because it is generative, you can modify the prompt to add custom categories or request a natural-language rationale. The taxonomy shipped by Meta covers similar ground but is editable.

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("meta-llama/LlamaGuard-7b")
model = AutoModelForCausalLM.from_pretrained("meta-llama/LlamaGuard-7b")
inputs = tok("kill them all", return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=32)
print(tok.decode(out[0]))
# -> "unsafe\nO1"

The key capability gap: OpenAI Moderation API vs Llama Guard is closed vs extensible. If your policy includes “mentions competitor brand names” or “speculative financial advice”, only Llama Guard can be bent to that without a second model.

Price / Cost Model

OpenAI Moderation API is free for any account with an OpenAI API key. There is no per-token charge, no tier metered separately from your overall org rate limits. This makes it effectively zero marginal cost until you hit rate ceilings.

Llama Guard has no licensing fee (weights are open under Meta’s community license). Your cost is infrastructure. A 7B model in fp16 needs ~14GB VRAM; on a cloud GPU instance that is a continuous hourly charge whether you call it or not. If you batch or run continuously, per-call cost is fractions of a cent; if you spin up on demand, cold start dominates.

Latency / Throughput

OpenAI Moderation is a single HTTPS round trip. From a US-east client, expect low hundreds of milliseconds including TLS and network. Throughput is limited by your RPM quota, not by your hardware.

Llama Guard latency is dominated by model load (seconds) and then decode speed. On a single A10G, a 7B model generates the ~10 token response in well under 100ms. Throughput scales with your GPU count and batch size. For high-volume synchronous checks, self-hosted can beat network RTT; for sporadic checks, the always-on instance cost may not justify it.

Ergonomics

The Moderation API is one endpoint, typed SDK, JSON response. Integration is a few lines. There is no version pinning; OpenAI may update the underlying model silently.

Llama Guard requires model download, tokenizer handling, and output parsing. The output format is stable but not JSON—you split on newline and map codes. You also own upgrades: when Meta releases a new base model, you decide when to swap weights.

def parse_guard(text: str) -> tuple[bool, list[str]]:
    lines = text.strip().split("\n")
    if lines[0] == "safe":
        return False, []
    return True, lines[1:]

Ecosystem

OpenAI Moderation lives inside the OpenAI platform. It pairs naturally with the Chat Completions API but cannot guard a Claude or Mistral response without you piping text out to OpenAI separately—raising privacy and latency questions.

Llama Guard is on Hugging Face, usable in LangChain Guard abstractions, Ray Serve, Triton, or a bare FastAPI. It can sit in front of any LLM regardless of vendor. Because it is just a causal LM, you can fuse it with your own system prompt or run it as a parallel speculative check.

Limits

OpenAI Moderation enforces a maximum input length (tens of thousands of characters) and per-minute request caps that scale with tier. It only accepts text (no images, though OpenAI has separate multimodal moderation). It is region-limited to where OpenAI operates.

Llama Guard inherits Llama-2’s 4K context window (or 8K depending on variant). It runs only where you provision accelerators. It will not flag anything beyond its prompt taxonomy unless you engineer it. The open-weight license restricts use to organizations under a certain size unless you request Meta’s approval—a legal limit, not a technical one.

Head-to-head table

Which to choose

Choose OpenAI Moderation API if

• You already call OpenAI for generation and want zero-ops safety with no new dependencies.
• Your content policy maps cleanly to the preset categories.
• You cannot justify a GPU for guardrails and your volume fits within free rate limits.
• You accept a black-box score and do not need audit trails of model reasoning.

Choose Llama Guard if

• You run non-OpenAI models (open-source, Anthropic, etc.) and need a vendor-neutral check.
• You must keep all text on your own hardware for compliance.
• Your policy requires custom categories or rationale output.
• You have steady volume that amortizes a GPU, or you already serve Llama-class models and can share the instance.

Hybrid and routing note

If you already front completions through a gateway such as n4n.ai—which exposes one OpenAI-compatible endpoint addressing 240+ models and honors client routing directives—you can invoke either guardrail as a pre- or post-filter without branching app code, and still get per-token usage metering on the generation side. The classifier choice remains independent: the gateway does not replace the moderation logic, it just moves the call location.

For most teams starting out, OpenAI Moderation API vs Llama Guard is not a permanent fork. Prototype with the free API, measure flag rates, then migrate to Llama Guard only if you hit a custom-category or data-residency wall. Both are competent; the architecture around them is where the real work lives.