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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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DeepSeek
✓ verifiedFreemium
Chinese AI lab DeepSeek offering free chat apps and low-cost API access to its frontier V-series and R-series reasoning models.
430M visits/mo
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Weights & Biases
✓ verifiedFreemium
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
2.5M 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
No public pricing
No public pricing
No public pricing
Core features
- ✦Free DeepSeek chat (web and app)
- ✦Open API platform
- ✦V-series and R-series reasoning models
- ✦DeepSeek-V4 with long context and stronger agent ability
- ✦OpenAI/Anthropic-compatible API
- ✦Extensive published model lineup
- ✦Dialogue with GLM large model
- ✦AI search
- ✦AI drawing
- ✦AI reading
- ✦AI-generated video (沉思清影-AI生视频)
- ✦AI-generated PPT
- ✦Data analysis tools
- ✦Code assistance (代码速写)
- ✦Intelligent agents
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- ✦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
- →Free AI chat and assistance
- →Building apps via API
- →Reasoning and coding tasks
- →Low-cost LLM inference
- →Engaging in conversations with an AI model
- →Generating images and videos using AI
- →Creating presentations with AI assistance
- →Analyzing data with AI tools
- →Assisting with code development
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects
- →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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