Comparison

AgnosLogic vs the alternatives.

There are three main approaches to hallucination detection. We explain how each works, what it costs, and where AgnosLogic fits.

Three approaches

The hallucination detection landscape

Approach 1

LLM-as-a-judge

A second LLM evaluates the first LLM's output. Usually GPT-4 or Claude scores each response for faithfulness.

✓ Works with any upstream LLM

✕ Requires 2+ API calls per check

✕ Judge itself can hallucinate

✕ 2–5× the cost per query

Galileo · Datadog · LangSmith · Patronus

Approach 2

RAG grounding

Compare LLM output against retrieved documents. Flag content not supported by the context.

✓ Very accurate when context is available

✕ Requires a retrieval corpus

✕ Can't detect out-of-context fabrications

Vectara · Exa · Ragas · TruLens

Approach 3 · Ours

Hidden-state geometry

Read the model's own internal representations. Truthful and fabricated outputs separate cleanly in hidden-state space.

✓ Single forward pass

✓ No second LLM, no retrieval corpus

✓ Sub-100ms latency (warm)

✕ Requires model-specific training

AgnosLogic · (no direct competitors)

Head to head

Feature comparison

	AgnosLogic	Galileo / Datadog	RAG-based tools
Single forward pass	✓	—	—
Sub-100ms latency	✓	—	—
No second LLM required	✓	—	✓
Works without retrieval corpus	✓	✓	—
Calibrated risk score	✓	Partial	Partial
Detects logical contradictions	✓	Via judge	—
Works on any LLM	Open-weight only	✓	✓
Cost per 1,000 checks	~$4.90	$15–50	$5–20

Latency

Speed matters in production.

Typical latency for a single hallucination check (warm inference, post cold-start).

AgnosLogic

87ms

RAG grounding

~400ms

LLM-as-judge (small)

~600ms

LLM-as-judge (GPT-4)

~1200ms

Measurements based on single-query latency from p50 production traffic.

When to use what

Our honest recommendation.

Use AgnosLogic when:

You run open-weight models (Qwen, LLaMA, Gemma) in production and need low-latency scoring at scale. You value single-call simplicity over multi-LLM pipelines.

Use LLM-as-a-judge when:

You need to score outputs from closed models (GPT-4, Claude) where hidden states aren't accessible.

Use RAG grounding when:

You have a trusted document corpus and need to verify that answers are supported by your sources.

Try it on your own data.

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