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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
portkey.ai logo
portkey.ai
✓ verified

AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.

266K visits/mo
Maxim AI logo
Maxim AI
✓ verifiedFreemium

End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.

102K visits/mo
honeyhive.ai logo
honeyhive.ai
✓ verifiedFreemium

Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.

24K visits/mo
Pricing

No public pricing

No public pricing

Developer: $0 (3 seats, 10k logs/mo)
Professional: $29/seat/mo (100k logs/mo)
Business: $49/seat/mo (500k logs/mo)

Free trial available

Developer: $0 (10K events/month, up to 5 users, 30-day retention)
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
  • AI Gateway for reliable LLM routing
  • Prompt Engineering for collaborative prompt management
  • Guardrails for enforcing reliable LLM behavior
  • Observability Suite for monitoring costs, quality, and latency
  • MCP Client for building AI agents with real-world tool access
  • 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
  • OpenTelemetry-native distributed tracing across 100+ LLMs and frameworks
  • Online evaluation via LLM-as-a-judge or code
  • Offline experiments and regression detection
  • Annotation queues for expert review
  • Alerts and drift detection
  • Prompt management, CLI and docs MCP server
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
  • Monitor costs, quality, and latency of AI applications.
  • Route to 250+ LLMs reliably with a single endpoint.
  • Streamline and scale prompt engineering.
  • Enforce reliable LLM behavior with guardrails.
  • Build agents with access to real-world tools.
  • Testing and comparing prompts and models
  • Evaluating and simulating AI agents
  • Monitoring agents in production
  • Running human evaluation pipelines
  • Debugging multi-agent systems
  • Monitoring live agent quality at scale
  • Catching regressions before release
  • Human review of edge cases
  • Aligning automated evaluators with domain experts
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