COMPARISON
Self-Hosted LLM Moderation vs Sentiqra
Running your own Llama, Mistral, or fine-tuned classifier in-house - or a multilingual detection API with 2,680 indicators, versioned reproducible outputs, a sub-500ms p95 latency target, and non-generative analysis?
Compare the workflow, not the number of labels
LLMs can return multiple labels too. Top 3 is not an exclusive capability or proof of better detection. Evaluate whether your workflow needs Sentiqra's documented taxonomy, versioned analysis, structured outputs, and separation between detection and customer policy.
Keeping relevant secondary signals can support different routing or review tasks. Whether it improves your operation depends on taxonomy fit, false alerts, and reviewer workload. Compare those outcomes against your existing process on the same labeled cases.
Use both where the workflow calls for it
Sentiqra signals → Your policy → human or LLM review where needed
An optional customer-built workflow. Reviewers or an LLM can interpret complex context; your policy owns the final action. Signals and receipts do not prove that a classification is correct.
Feature-by-feature comparison
Last verified September 21, 2026 · Not a vendor - qualitative build-vs-buy comparison · Source: Internal analysis
| Feature | Sentiqra | Self-Hosted LLM Moderation |
|---|---|---|
| Languages | Varies by domain | Depends on your model (variable quality) |
| Indicators/Categories | Domain-specific | None (free-form labels) |
| Per-indicator signal score | Yes | No |
| Structured taxonomy | Yes (groups + concepts) | No |
| Deterministic output | Yes | No |
| Semantic version identity | Explicit (configuration ID per response) | Usually model name only |
| Drift surface | Analyzer/configuration change only | Prompt + model + infra + your own maintenance changes |
| Reproducible audit trail | Yes (pinned config) | No |
| Custom taxonomy | Yes (Enterprise) | No |
| Custom thresholds & policy logic | Yes - all plans (customer-side) | Customer-side only |
| Product domains | 12 production analyzers | 1 (T&S only) |
Where Self-Hosted LLM Moderation works
- Full control over the model and data path
- No per-call vendor fees at high volume
- Can be fine-tuned on your own labeled data
- Flexible - custom prompts for any policy
- Natural-language explanations
Where Self-Hosted LLM Moderation falls short
- GPU infrastructure cost and capacity planning are yours
- Fine-tuning maintenance - every policy change risks a retrain
- Drift monitoring is your problem - model updates change behavior silently
- No reproducible audit trail for appeals
- No structured taxonomy - labels are free-form
- Hallucination risk on classification
- No per-indicator signal scores
- Requires prompt engineering and ML-ops expertise
When Sentiqra is the better fit
Running your own classifier means owning the GPU bill, the fine-tuning pipeline, the eval harness, and the drift monitoring - before you write a single policy rule. If your moderation stack is one service among many rather than a core competency, a managed deterministic analyzer is usually the better trade.
Languages platform-wide; 8-41 per analyzer
Indicators across the production analyzer catalog
One response fingerprint for identical input under a pinned configuration (reproduced live)
When Self-Hosted LLM Moderation is still the right choice
Self-hosting is the right call when you have an ML-ops team, a genuinely unique taxonomy that no vendor taxonomy fits, and traffic volume large enough that per-call pricing dominates GPU costs. For most teams the honest comparison is build-vs-buy: the eval harness, drift monitoring, and evaluation sets cost more than the model.
Full Comparison
Sentiqra vs all competitors
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Per-language Top-3 Hit Rate
Pricing
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