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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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Fiddler AI
✓ verifiedFreemium
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
51K visits/mo
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Higress
✓ verifiedFreemium
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
29K visits/mo
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Agenta
✓ verifiedFreemium
Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.
34K visits/mo
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portkey.ai
✓ verified
AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.
266K visits/mo
Pricing
Free: $0 (real-time guardrails)
Developer: $0.002 per trace
No public pricing
No public pricing
No public pricing
Core features
- ✦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
- ✦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
- ✦Prompt management as a single source of truth
- ✦Playground for prompt experimentation
- ✦Evaluation to measure changes before production
- ✦Observability and tracing for debugging
- ✦Collaboration across technical and non-technical roles
- ✦Open-source and self-hostable
- ✦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
Use cases
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →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
- →Version and manage prompts centrally
- →Benchmark and evaluate LLM outputs
- →Debug and trace production LLM issues
- →Collaborate across a team on LLM apps
- →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.
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