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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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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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metaflow.org
✓ verifiedFree
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
20K 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
Pricing
No public pricing
Free: $0 (real-time guardrails)
Developer: $0.002 per trace
No public pricing
No public pricing
No public pricing
Core features
- ✦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
- ✦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
- ✦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
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
- ✦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
Use cases
- →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.
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Version and manage prompts centrally
- →Benchmark and evaluate LLM outputs
- →Debug and trace production LLM issues
- →Collaborate across a team on LLM apps
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems
- →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
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