| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Captures and stores experiment metadata (hyperparameters, metrics, artifacts, environment configs) through SDK instrumentation that logs to a centralized metadata store with immutable versioning. Uses a hierarchical schema supporting nested parameter spaces, metric time-series, and artifact lineage tracking across thousands of concurrent experiments without requiring code refactoring.
Unique: Implements immutable append-only metadata store with hierarchical versioning that preserves full experiment history without requiring snapshots, enabling retroactive comparison and audit trails across thousands of runs without storage explosion
vs alternatives: Scales to 10,000+ concurrent experiments with sub-second query latency whereas MLflow and Weights & Biases show degradation above 1,000 runs due to file-based or flat-schema storage models
Provides a query engine that filters and compares experiments across arbitrary dimensions (hyperparameters, metrics, tags, date ranges) and renders interactive dashboards with scatter plots, parallel coordinates, and heatmaps. Uses columnar indexing on metadata to enable sub-second filtering across millions of metric points and supports custom dashboard templates with drag-and-drop widget composition.
Unique: Implements columnar indexing with bitmap filtering to enable sub-second multi-dimensional queries across millions of metric points, combined with template-based dashboard composition that allows non-technical users to create custom views without SQL
vs alternatives: Faster than TensorBoard for comparing >100 experiments (sub-second filtering vs. linear scan) and more flexible than Weights & Biases reports because it supports arbitrary dimension combinations without pre-defined report types
Organizes experiments into team workspaces with role-based access control (RBAC) supporting Owner, Editor, and Viewer roles. Enables fine-grained permissions (e.g., 'can promote models to production' vs. 'can only view experiments'). Supports SSO integration (SAML, OAuth) for enterprise deployments and audit logging of all access and modifications.
Unique: Integrates RBAC with experiment-level operations (e.g., 'can promote models to production') rather than just workspace-level access, enabling fine-grained governance of model deployment decisions
vs alternatives: Provides more granular permission control than Weights & Biases' team-level access and includes built-in audit logging unlike MLflow's minimal access control
Allows users to create custom dashboards by composing widgets (charts, tables, metrics cards) that pull data from experiments. Widgets support dynamic filtering and drill-down to experiment details. Dashboards are shareable via links and can be embedded in external tools via iframes. Supports scheduled dashboard refreshes and email delivery of dashboard snapshots.
Unique: Supports dynamic dashboard composition with drill-down to experiment details and scheduled email delivery, enabling stakeholder reporting without manual data export
vs alternatives: Provides richer dashboard customization than Weights & Biases' fixed dashboard layouts and includes email delivery that TensorBoard doesn't offer
Centralized model storage with semantic versioning, stage transitions (staging/production/archived), and full lineage tracking linking models to source experiments, training data versions, and deployment metadata. Implements a state machine for model lifecycle management with audit logging of all stage transitions and supports model comparison by metrics, parameters, and artifact checksums.
Unique: Implements bidirectional lineage tracking that links models back to source experiments and forward to deployments, with immutable audit logs of all stage transitions and support for comparing models by both metrics and artifact checksums to detect silent data drift
vs alternatives: More comprehensive lineage tracking than MLflow Model Registry (which only links to experiments) and simpler governance than Seldon/KServe because it provides built-in stage machine without requiring external approval systems
Enables team members to view, comment on, and compare experiments with granular permission controls (viewer, editor, admin) at project and experiment level. Implements real-time collaboration features including experiment comments with threading, @mentions, and activity feeds showing who modified what and when, with audit logging of all access and modifications.
Unique: Implements immutable activity logs with role-based filtering that allow fine-grained audit trails without performance overhead, combined with real-time comment threading that doesn't require external communication tools
vs alternatives: Lighter-weight collaboration than Weights & Biases (no Slack integration required) but more structured than MLflow (which has no built-in commenting or audit logging)
Monitors deployed models in production by ingesting live prediction metrics and comparing against baseline experiment metrics to detect performance degradation. Uses statistical anomaly detection (z-score, IQR, moving average) to identify metric drift and triggers configurable alerts via email, webhooks, or Slack when thresholds are breached, with root cause analysis linking degradation to data drift or model staleness.
Unique: Implements statistical anomaly detection with configurable baselines linked to source experiments, enabling drift detection without requiring separate monitoring infrastructure, combined with webhook-based alert routing for integration into existing MLOps pipelines
vs alternatives: More integrated with experiment tracking than standalone monitoring tools (Datadog, New Relic) because it compares production metrics directly against baseline experiments, and simpler than custom drift detection because it requires no model training
Provides language-specific SDKs (Python, JavaScript/TypeScript) that integrate with popular ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, Keras) via callbacks and decorators to automatically log metrics, hyperparameters, and artifacts without modifying training code. Implements lazy evaluation and batching to minimize logging overhead and supports both synchronous and asynchronous logging modes.
Unique: Implements framework-specific callbacks and decorators that hook into native training loops (PyTorch hooks, TensorFlow callbacks, scikit-learn estimators) to enable zero-code logging, combined with batching and async modes to minimize training overhead
vs alternatives: Less intrusive than Weights & Biases (which requires explicit wandb.log() calls) and more comprehensive than MLflow (which lacks native PyTorch callback support)
+4 more capabilities
Evaluates LLM safety across 7 distinct categories (offensiveness, unfairness, physical health, mental health, illegal activities, ethics, privacy) using 11,435 curated multiple-choice questions available in both Chinese and English. The benchmark constructs category-specific prompts, sends them to target models, extracts predicted answers from model responses, and compares against ground-truth labels (0->A, 1->B, 2->C, 3->D) to compute accuracy metrics per category and overall safety score.
Unique: Combines 11,435 questions across 7 safety categories with explicit Chinese-English parallel coverage and a filtered subset (test_zh_subset.json) for sensitive keyword handling, enabling systematic cross-lingual safety assessment. Uses category-stratified few-shot examples (5 per category) to support both zero-shot and five-shot evaluation paradigms within a single framework.
vs alternatives: Larger and more category-diverse than single-domain safety benchmarks (e.g., ToxiGen for toxicity only), and explicitly supports Chinese alongside English, addressing a gap in multilingual safety evaluation infrastructure.
Supports two distinct evaluation paradigms: zero-shot (questions presented directly without examples) and five-shot (5 category-specific examples provided before each test question). The framework conditionally constructs prompts using dev_en.json/dev_zh.json few-shot examples or omits them entirely, allowing researchers to measure how in-context learning affects safety performance. Prompt templates are language-aware and can be customized per model to improve answer extraction accuracy.
Unique: Provides curated few-shot examples stratified by safety category (5 per category) rather than random sampling, ensuring balanced representation of each harm type. Prompt templates are explicitly customizable per model (e.g., evaluate_baichuan.py shows Baichuan-specific extraction logic), acknowledging that different architectures require different prompting strategies.
More systematic than ad-hoc few-shot selection; category-stratified examples ensure consistent coverage of all safety dimensions rather than potentially biased random sampling.
SafetyBench Eval scores higher at 63/100 vs Neptune AI at 60/100. Neptune AI leads on quality, while SafetyBench Eval is stronger on ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Manages parallel Chinese and English datasets (test_en.json, test_zh.json, dev_en.json, dev_zh.json) with a filtered Chinese subset (test_zh_subset.json, 300 questions per category) for sensitive keyword handling. Data acquisition uses Hugging Face hosting with dual download methods (shell script download_data.sh or Python download_data.py with datasets library). Each question maintains consistent structure (id, category, question, options, answer) across languages, enabling direct cross-lingual comparison of model safety performance.
Unique: Provides both full Chinese dataset (test_zh.json) and a filtered subset (test_zh_subset.json with 300 questions per category) explicitly designed to avoid sensitive keywords, addressing practical concerns about evaluating on content that may trigger platform policies. Dual download methods (shell script and Python) reduce friction for different user workflows.
vs alternatives: More comprehensive multilingual coverage than English-only benchmarks; filtered subset is a pragmatic addition for teams needing to evaluate without policy violations.
Computes accuracy metrics per safety category (offensiveness, unfairness, physical health, mental health, illegal activities, ethics, privacy) and aggregates to an overall safety score. Supports standardized leaderboard submission via JSON format (question_id -> predicted_answer). Metrics are computed by comparing predicted answers (extracted from model responses) against ground-truth labels, enabling fine-grained analysis of which safety dimensions a model excels or fails on. Results can be submitted to llmbench.ai/safety leaderboard for public comparison.
Unique: Stratifies metrics across 7 explicit safety categories rather than computing a single aggregate score, enabling fine-grained diagnosis of safety weaknesses. Leaderboard integration (llmbench.ai/safety) provides public benchmarking infrastructure, creating accountability and enabling direct model comparison.
vs alternatives: Category-level metrics provide more actionable insights than single-number safety scores; leaderboard integration drives standardization and reproducibility across the research community.
Implements a standardized evaluation pipeline (exemplified in evaluate_baichuan.py) that constructs prompts, sends them to a target model via API or local inference, extracts predicted answers from model responses using model-specific parsing logic, and validates extracted answers against expected format (0->A, 1->B, 2->C, 3->D). The pipeline handles model-specific response formats and can be customized per model architecture. Supports batch evaluation of all 11,435 questions with error handling and logging.
Unique: Provides a concrete, model-specific evaluation implementation (evaluate_baichuan.py) that can be forked and adapted, rather than just a dataset. Acknowledges that different models require different answer extraction logic and provides a template for customization. Supports both zero-shot and few-shot evaluation within the same pipeline.
vs alternatives: More practical than dataset-only benchmarks because it includes reference evaluation code; reduces barrier to entry for teams without evaluation infrastructure.
Defines a structured taxonomy of 7 safety categories (offensiveness, unfairness, physical health, mental health, illegal activities, ethics, privacy) and curates 11,435 diverse multiple-choice questions mapped to these categories. Each question is designed to test whether a model correctly handles or refuses harmful content within that category. The taxonomy is explicit and mutually exclusive, enabling fine-grained safety analysis. Questions are curated to be challenging and representative of real-world safety concerns.
Unique: Explicitly defines 7 non-overlapping safety categories and curates 11,435 questions to cover them systematically, providing a structured taxonomy rather than ad-hoc safety testing. The taxonomy is comprehensive enough to cover major harm types (physical, mental, legal, ethical, privacy) while remaining tractable for evaluation.
vs alternatives: More comprehensive and structured than single-category benchmarks (e.g., toxicity-only); provides a holistic safety assessment framework that aligns with regulatory and safety research perspectives.
Provides two download methods for SafetyBench datasets: shell script (download_data.sh) and Python script (download_data.py using Hugging Face datasets library). The architecture leverages Hugging Face Hub for dataset hosting and distribution, enabling one-command dataset acquisition with automatic decompression and directory structure creation. The Python method uses the datasets library for programmatic access, supporting integration into automated evaluation pipelines without manual file management.
Unique: Provides dual download methods (shell script and Python) leveraging Hugging Face Hub for distribution, enabling both manual and programmatic dataset acquisition with automatic decompression and directory structure creation.
vs alternatives: More convenient than manual downloads by providing automated acquisition scripts, and more reproducible than email-based dataset distribution by using Hugging Face Hub as a stable, versioned repository
Computes accuracy metrics stratified by safety category, enabling per-dimension performance analysis. The evaluation pipeline aggregates predictions across all questions in each category (offensiveness, unfairness, physical health, mental health, illegal activities, ethics, privacy) and computes category-specific accuracy scores. This architecture enables identification of category-specific vulnerabilities (e.g., a model may be robust on ethics but weak on physical health) without requiring separate evaluation runs.
Unique: Automatically stratifies accuracy metrics by safety category, enabling fine-grained vulnerability analysis without requiring separate evaluation runs. Provides per-category scores that reveal category-specific weaknesses.
vs alternatives: More diagnostic than aggregate safety scores by breaking down performance by harm category, enabling targeted safety improvements rather than black-box optimization