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Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.
End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Free trial available
Free trial available
Free trial available
- ✦AI bot detection
- ✦Transaction fraud protection
- ✦Account-takeover (ATO) defense
- ✦Pull-based SMS MFA
- ✦Private Learning ML risk models
- ✦Two-line reCAPTCHA migration
- ✦Hundreds of integrations
- ✦Prompt IDE, versioning, and deployment
- ✦Agent simulation and evaluation
- ✦Production tracing and observability
- ✦Pre-built and custom evaluators
- ✦Human-in-the-loop evaluation
- ✦Bifrost LLM gateway
- ✦Request logging and LLM observability
- ✦AI gateway with routing and automatic fallbacks
- ✦Caching and rate limiting
- ✦Session, user and custom-property analytics
- ✦Prompts, playground and datasets for testing
- ✦Integrations with OpenAI, Anthropic, Azure and more
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- →Blocking bots and spam signups
- →Preventing account takeover
- →Reducing transaction and payment fraud
- →Stopping credential stuffing
- →Testing and comparing prompts and models
- →Evaluating and simulating AI agents
- →Monitoring agents in production
- →Running human evaluation pipelines
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs