Google Stax vs Unprompt: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Google Stax and Unprompt — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Google Stax

Google

Paid

A complete toolkit from Google for evaluating, measuring, and comparing AI model performance with hard data and flexible tools.

Comprehensive Evaluation Toolkit: Centralizes tools to run structured evaluations and collect quantitative 'hard' data about model performance across tasks and datasets.
Flexible Analysis Workflows: Supports customizable evaluation pipelines so teams can define, repeat, and compare different test suites, metrics, and slices of data.
Model Comparison and Baselines: Enables side-by-side comparisons of model versions and baselines to surface regressions, improvements, and trade-offs for release decisions.
Data Slicing and Diagnostics: Provides the ability to analyze model behavior on specific data subsets or slices to identify failure modes and targeted improvement areas.
Reporting and Insights: Produces reproducible evaluation reports and visualizations that help teams communicate results and justify product or model changes.
Integration-Friendly Tooling: Designed to fit into ML development workflows so evaluation outputs can inform CI/CD, model registries, or release gating (integration specifics per implementation).
Structured evaluation workflows for assessing model behavior and performance
Comparative analysis tools to compare models and model versions
Metrics and reporting for quantitative measurement of model quality
Visualization and dashboards for inspecting evaluation results
Flexible tooling designed to integrate into development and release processes

Pre-release Validation: Run standardized evaluation suites to ensure a new model version outperforms the production baseline before deployment.
Regression Detection: Automatically compare model versions to detect performance regressions on key metrics or critical data slices.
Targeted Debugging: Drill into specific data slices where performance drops to identify root causes and prioritize fixes.
Cross-model Benchmarking: Benchmark multiple candidate models against shared metrics and baselines to select the best performer for a product.
Monitoring Model Drift: Periodically re-evaluate models on fresh data to identify drift and trigger retraining or rollback decisions.
Stakeholder Reporting: Generate reproducible evaluation reports and visualizations to inform product, legal, or leadership teams about model readiness and risk.
Benchmarking model variants to choose best-performing architectures or checkpoints
Regression detection during model updates and CI/CD model validation
Evaluating model behavior across slices, datasets, or demographic groups
Instrumenting evaluation dashboards for product and research teams to monitor model performance

Polymorph

Freemium

iPhone and Mac app that measures your AI usage in tokens, API-rate cost and water, without judging it.

Automatic Measurement: Mac reads local AI tool logs and iPhone uses Screen Time, so there is nothing to count manually.
Evening Check-In: A one-tap daily confirmation of personal versus work AI use, with adjustments allowed.
Pixel Calendar: Each day becomes a pixel colored against your own average, with streaks and a full-year band.
Usage and Cost (Premium): Tokens, the API-rate cost and estimated water use over today, 30 days and full history.
Codex Quotas and Widgets (Premium): Quota countdowns plus USAGE and LIMITS widgets.
Milestones and Friends: 62 milestones and friend trends via QR code or link with no account needed.
Private by Design: Data stays on device and in your iCloud, with no conversation read and no ads or tracking.

Self-Awareness: See how much you rely on AI day to day without being blocked or lectured.
Cost Tracking: Estimate what your token use would cost at API rates.
Environmental Awareness: View an estimate of the water behind your AI usage.
Work-Life Separation: Separate personal AI use from work-hours use.
Quota Planning: Watch Codex limits and reset countdowns to avoid wasting capacity.
Group Accountability: Compare trends with friends without sharing exact numbers.