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LangSmith platform to trace, evaluate and deploy AI agents; framework-agnostic tooling for AI engineering teams.
Chinese AI lab DeepSeek offering free chat apps and low-cost API access to its frontier V-series and R-series reasoning models.
Side-by-side arena to compare AI coding models and build multi-file apps, with a public leaderboard and battle mode.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Privacy-focused AI platform offering uncensored chat, image, video, and audio generation across many third-party and open-source models.
No public pricing
No public pricing
No public pricing
- ✦Agent observability and tracing
- ✦Evaluation and scoring
- ✦Agent deployment and scaling
- ✦Framework-agnostic SDKs
- ✦Open-source frameworks (langgraph, langchain)
- ✦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
- ✦Head-to-head model comparison
- ✦Battle mode matchups
- ✦Public model leaderboard
- ✦Multi-file app generation
- ✦File uploads as input
- ✦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
- ✦Multi-provider chat, image, video, and audio generation
- ✦Zero data retention on private/self-hosted models
- ✦OpenAI-compatible API for agent integrations
- ✦Tiered privacy levels including TEE and end-to-end encryption
- ✦Uncensored open-source model access
- ✦Credit-based access to premium third-party models
- →Debug and monitor LLM agents in production
- →Evaluate and improve agent quality
- →Deploy and scale agents
- →Free AI chat and assistance
- →Building apps via API
- →Reasoning and coding tasks
- →Low-cost LLM inference
- →Choosing the best coding model
- →Benchmarking AI code quality
- →Prototyping small apps
- →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
- →Chatting or generating content without prompt logging
- →Building AI agents via an OpenAI-compatible API
- →Generating images or video with fewer content restrictions
- →Accessing many leading AI models under one subscription