9 Weights & Biases alternatives, ranked

The API that started the LLM revolution. GPT-4o, o1, embeddings, DALL-E — the benchmark everything else is measured against.

• Strong on: Best-in-class models
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (10.0 vs 8.2)

The GitHub of AI. 900k+ models, datasets, and Spaces. The hub of the open-source ML ecosystem.

• Strong on: Massive model hub
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (10.0 vs 8.2)

The safety-first frontier model. Claude 3.5 Sonnet and Claude 3 Opus lead on reasoning, coding, and long context.

• Strong on: Best for long context (200k tokens)
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (10.0 vs 8.2)

The framework for LLM applications. Chains, agents, RAG — the glue between your code and language models.

• Strong on: Huge ecosystem
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (10.0 vs 8.2)

Run LLMs locally. Pull and run Llama, Mistral, Gemma, and 100+ models with a single command.

• Strong on: Run models completely locally
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (10.0 vs 8.2)

Run ML models in the cloud via API. Image generation, video, audio — deploy any model with one line.

• Strong on: Any model via API
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (10.0 vs 8.2)

High-throughput LLM inference engine. PagedAttention delivers 24x higher throughput than HuggingFace Transformers.

• Strong on: Incredible throughput
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (10.0 vs 8.2)

Fastest inference cloud for open-source models. Run Llama, Mistral, Flux and 200+ models at scale.

• Strong on: Fast inference speeds
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Higher overall score (8.5 vs 8.2)

Open source platform for the ML lifecycle. Track experiments, package models, deploy anywhere.

• Strong on: Open source and free
• Addresses Weights & Biases's tradeoff: Expensive for large teams
• Also: Works everywhere