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Higress logo
Higress
✓ verifiedFreemium

Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.

29K visits/mo
Fiddler AI logo
Fiddler AI
✓ verifiedFreemium

Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.

51K visits/mo
ApX Machine Learning logo
ApX Machine Learning
✓ verifiedFreemium

Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.

355K visits/mo
Gamma AI logo
Gamma AI
✓ verifiedPaid

Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).

168K visits/mo
Pricing

No public pricing

Free: $0 (real-time guardrails)
Developer: $0.002 per trace
Basic: $0/mo (free forever)
Pro: $19/mo
Pro+: $59/mo

No public pricing

Core features
  • Unified proxy and protocol conversion across 100+ LLMs
  • Model-level fallback and routing
  • Semantic and exact-match AI caching
  • Token tracking and quota controls
  • Content-safety and data-protection filtering
  • MCP service hosting and plugin marketplace
  • 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
  • Deep-learning data classification (99.5% claimed accuracy)
  • Cloud DLP across SaaS applications
  • One-click deployment across apps, devices and users
  • End-user self-remediation of violations
  • Broad SaaS integrations
  • Insider-threat and breach monitoring
Use cases
  • Centralizing access to multiple LLM providers
  • Building and governing AI agent/MCP services
  • Controlling token spend across teams
  • Adding caching and safety to LLM calls
  • 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
  • Prevent data leaks across SaaS apps
  • Classify and monitor sensitive data
  • Reduce breaches from human error
  • Give security teams cloud data visibility
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