| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $49/mo | — |
| Capabilities | 15 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Routes requests to 500+ external LLM models (OpenAI, Anthropic, etc.) through a single API endpoint, abstracting provider-specific request/response formats and handling protocol translation. Implements request caching, automatic retries with exponential backoff, and fallback routing to alternative models when primary provider fails, reducing integration complexity from managing N provider SDKs to a single gateway interface.
Unique: Implements protocol-agnostic gateway that normalizes 500+ models into single API contract with built-in caching and retry logic, rather than requiring developers to manage provider-specific SDKs and error handling separately
vs alternatives: Faster integration than managing multiple provider SDKs directly because it abstracts protocol differences and adds automatic retries/caching at the gateway layer rather than application level
Stores, versions, and deploys prompts through a web IDE with git-like version control, enabling teams to track prompt changes, rollback to previous versions, and deploy new prompts to production through the gateway without code changes. Integrates with the unified gateway to serve deployed prompt versions at inference time, supporting A/B testing by routing traffic to different prompt versions.
Unique: Implements git-like prompt versioning with one-click deployment through the gateway, allowing non-technical users to manage prompt lifecycle without touching code or infrastructure
vs alternatives: Faster prompt iteration than hardcoding prompts in application code because changes deploy instantly without recompilation or redeployment of the main application
Enables A/B testing by deploying multiple prompt or model versions and routing traffic to each variant based on configurable split percentages (e.g., 50% to variant A, 50% to variant B). Automatically collects metrics for each variant (latency, cost, quality) and provides statistical comparison dashboards to determine which variant performs better. Supports gradual rollout (canary deployment) by starting with small traffic percentages and increasing based on performance.
Unique: Implements A/B testing with automatic metric collection and comparison dashboards, rather than requiring manual traffic splitting and external statistical analysis tools
vs alternatives: More integrated than manual A/B testing because traffic splitting and metric comparison are built-in, reducing the need for custom infrastructure and statistical analysis
Supports multiple team members with role-based access control (RBAC), enabling organizations to grant different permissions to engineers, product managers, and finance teams. Tracks who made changes to prompts, deployments, and alert configurations with audit logs, and supports team-scoped dashboards and alerts. Integrates with Google SSO for authentication (Pro/Team tiers) with SAML support on Enterprise tier.
Unique: Implements RBAC with audit logging and team-scoped resources, rather than all-or-nothing access, enabling organizations to grant granular permissions without sharing credentials
vs alternatives: More secure than shared credentials because RBAC enables fine-grained access control and audit trails provide accountability for changes to production configurations
Caches identical LLM requests at the gateway level and returns cached responses without calling the LLM provider, reducing latency and cost for repeated queries. Supports cache invalidation strategies (TTL, manual) and provides cache hit/miss metrics on dashboards. Works transparently for requests routed through the Respan gateway without application-level changes.
Unique: Implements transparent request-level caching at the gateway with cache metrics, rather than requiring application-level caching logic or external cache infrastructure
vs alternatives: More efficient than application-level caching because gateway-level caching works across all applications using the same Respan gateway, enabling cache hits across different services
Offers self-hosted deployment option for Enterprise tier customers, allowing Keywords AI infrastructure to run on customer's own servers or cloud account. Enables data residency compliance (e.g., data must stay in EU for GDPR). Self-hosted deployment includes all Keywords AI features (gateway, tracing, evaluation, dashboards). Requires customer to manage infrastructure, updates, and security patches. Specific deployment options (Kubernetes, Docker, VMs) not documented.
Unique: Offers self-hosted deployment option for Enterprise customers, enabling data residency compliance and reducing vendor lock-in. Allows organizations to run full Keywords AI stack on their own infrastructure.
vs alternatives: More compliant than cloud-only deployment for data residency requirements; more flexible than managed-only platforms because customers can choose deployment model.
Supports SAML 2.0 authentication for Enterprise tier customers, enabling integration with corporate identity providers (Okta, Azure AD, etc.). Allows centralized user management and access control through existing identity infrastructure. Supports role-based access control (RBAC) and single sign-on (SSO). SAML is available only on Enterprise tier; Pro/Team tiers use Google OAuth.
Unique: Implements SAML 2.0 authentication for Enterprise tier, enabling integration with corporate identity providers and centralized access control. Reduces friction for enterprise deployments by leveraging existing identity infrastructure.
vs alternatives: More secure than OAuth-only authentication because SAML enables centralized access control; more convenient for enterprises because it integrates with existing identity providers.
Captures complete execution traces from production LLM calls including request/response content, latency, token counts, cost, and custom metadata, storing traces in a searchable index with 7-30 day retention. Enables filtering and searching by content keywords, latency ranges, cost thresholds, quality tags, and custom properties, with trace replay functionality allowing developers to re-run requests through the playground for debugging.
Unique: Implements production trace capture with rich context (cost, latency, custom metadata) and replay-in-playground debugging, rather than simple logging that requires external tools to correlate and analyze
vs alternatives: More actionable than generic logging because traces include cost and latency metrics by default, and replay functionality eliminates the need to manually reconstruct requests for debugging
+7 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 Keywords AI at 57/100. Keywords AI leads on quality, while SafetyBench Eval is stronger on ecosystem.
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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