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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.

Coze logo
Coze
✓ verifiedFreemium

ByteDance's Coze (Kouzi): an all-in-one AI office assistant for writing, slides, sheets, design, podcasts and images.

7.2M visits/mo
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
Agenta logo
Agenta
✓ verifiedFreemium

Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.

34K visits/mo
Dify.ai logo
Dify.ai
✓ verifiedFreemium

Open-source platform to build, deploy and monitor agentic AI workflows and RAG apps, with cloud, self-host and enterprise options.

1.1M visits/mo
Pricing

No public pricing

No public pricing

No public pricing

Sandbox: Free (200 message credits)
Professional: $590/workspace/year
Team: $1,590/workspace/year
Core features
  • AI writing
  • AI presentation/PPT generation
  • AI spreadsheets and tables
  • AI design
  • AI podcast generation
  • AI image generation
  • 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
  • Visual workflow studio for agents
  • RAG knowledge pipelines
  • Agent runtime with tools and memory
  • Marketplace of models and plugins
  • Publish as app, API or MCP tool
  • Logging, analytics and monitoring
Use cases
  • Drafting documents
  • Building presentations
  • Generating spreadsheets
  • Creating designs and images
  • Producing podcasts
  • 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
  • Building AI agents and chatbots
  • Creating RAG-based knowledge apps
  • Deploying LLM apps at enterprise scale
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