Dataiku LLM Guard Services
Built-in controls for managing costs, protecting sensitive data, and maintaining quality across every LLM and AI application.
Control GenAI spend
Track LLM usage in real time, set budget limits by project, team, or provider, and automatically block queries when thresholds are reached, so there are no surprise invoices.
Guard sensitive data
Automatically detect and handle personally identifiable information (PII), toxic content, and forbidden terms in LLM inputs and outputs before they cause compliance issues.
Ensure LLM performance
Measure LLM quality with standardized metrics and side-by-side model comparisons, so you always know which model is delivering the most accurate, consistent results for each use case.
Set granular budget controls across your entire organization
Dataiku Cost Guard lets administrators define budget limits at the project, user group, or LLM provider level. When usage hits a threshold, Cost Guard alerts stakeholders or automatically blocks further queries for that project without affecting other teams. Prebuilt dashboards provide detailed cost reports by use case, user, and project, supporting internal cost allocation and re-billing.
Screen every request and response for risk
Dataiku Safe Guard evaluates both LLM inputs and outputs for personally identifiable information (PII), toxic content, forbidden terms, and prompt injection attempts. When a risk is detected, Safe Guard can redact the information, block the request, or alert an administrator. Organizations get automated data privacy and compliance controls across every GenAI application.
Deploy with consistent quality standards
Quality Guard provides GenAI-specific quality metrics and side-by-side comparisons of model results, giving teams a standardized, automated way to evaluate LLM performance from proof of concept through production.
Take the guesswork out of LLM selection
Quality Guard provides GenAI-specific metrics and side-by-side model comparisons, so teams can evaluate which LLM delivers the highest performance for each use case before deploying to production.
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