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Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Security platform that guards GenAI apps and AI agents against prompt injection, data leaks and misuse for enterprise teams.
Developer-first security platform unifying code, cloud, runtime and AI pentesting with noise reduction and autofix.
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
No public pricing
No public pricing
Free trial available
Free trial available
Free trial available
- ✦Open-Source AI Gateway
- ✦Multi-LLM Management & Cost Optimization
- ✦Efficient and Secure LLMs Invocation
- ✦Unified API Signature for LLMs
- ✦Load Balancer for seamless switching between LLMs
- ✦Fine-Grained Traffic Control for LLMs
- ✦LLM Quota Management
- ✦Real-time LLM Traffic Monitoring
- ✦Caching Strategies for AI in Production
- ✦Flexible Prompt Management
- ✦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
- ✦Runtime protection for AI agents and apps
- ✦Prompt-injection and jailbreak prevention
- ✦Data-leakage detection in prompts
- ✦Shadow-AI discovery across apps and browsers
- ✦Policy controls by user, app and action
- ✦AI red-teaming and adversarial testing
- ✦SAST, SCA and secrets scanning
- ✦Cloud misconfiguration (CSPM) and container scanning
- ✦AI-powered autonomous pentesting
- ✦AutoFix pull requests and auto-triage
- ✦Runtime and bot protection (Zen)
- ✦SOC 2 and ISO compliance support
- ✦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
- →Building API portals for secure sharing of internal APIs with partners.
- →Tracking API usage and driving API monetization.
- →Managing and securing API access in compliance with enterprise policies.
- →Connecting to multiple AI large models simultaneously.
- →Optimizing LLM costs and improving efficiency.
- →Protecting against LLM attacks and data leaks.
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Securing conversational and RAG agents
- →Governing employee use of AI tools
- →Adversarial testing before deploying GenAI
- →Meeting AI compliance requirements
- →Finding and fixing code vulnerabilities
- →Securing cloud and containers
- →Running continuous pentests
- →Automating compliance evidence
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets