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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.
Open-source library and desktop app for fast, memory-efficient local fine-tuning and inference of open LLMs.
AI-focused cloud offering NVIDIA GPU compute, storage and MLOps tooling for training and inference at scale, with usage-based pricing.
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
- ✦Optimized LoRA/FFT/PT training kernels for 500+ models
- ✦Local offline model runner for Mac and Windows
- ✦No-code dataset creation from PDFs, CSVs, and JSON
- ✦Unlimited tool-calling and web search inside model runs
- ✦Data Recipes workflow to turn documents into training datasets
- ✦Export to safetensors or GGUF for llama.cpp, vLLM, Ollama
- ✦Multi-GPU support on paid tiers
- ✦NVIDIA GPU instances (H100, H200, B200, GB200)
- ✦On-demand and preemptible GPU pricing
- ✦High-performance and object storage
- ✦Managed Kubernetes and Slurm (Soperator)
- ✦Serverless and managed inference (Token Factory)
- ✦MLOps tooling and 24/7 expert support
- ✦Commitment discounts up to 35%
- →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 fine-tuning open models on a single GPU for free
- →Teams building custom datasets from unstructured documents
- →Developers wanting to run and compare LLMs fully offline
- →Enterprises needing faster, more accurate multi-node training
- →Train large AI/ML models on GPU clusters
- →Run scalable inference workloads
- →Store and manage large training datasets
- →Run Slurm/Kubernetes AI pipelines