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Open-source platform to build, deploy and monitor agentic AI workflows and RAG apps, with cloud, self-host and enterprise options.
AI assistant for teams with secure LLM and company-knowledge access.
Always-on cloud AI agent that runs multi-step workflows and monitoring on a dedicated 24/7 VM to automate business tasks.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
No public pricing
No public pricing
- ✦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
- ✦Unified and safe access to GPT-4
- ✦Connection to team's data for up-to-date answers
- ✦Customizable AI agent building without code
- ✦Team collaboration features for sharing prompts and conversations
- ✦Suggestions for documentation updates and improvements
- ✦Always-on agent on a dedicated 24/7 VM
- ✦Multi-step task automation (docs, PPT, video, research)
- ✦Proactive monitoring with alerts and actions
- ✦Shared/self-improving agent knowledge network
- ✦Page deployment and drive storage
- ✦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
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- →Building AI agents and chatbots
- →Creating RAG-based knowledge apps
- →Deploying LLM apps at enterprise scale
- →RevOps & Sales: Create customer profiles, flag at-risk deals, analyze calls, generate SQL.
- →PMM & Marketing: Write on-brand content, create consistent messaging, translate content, extract insights.
- →Customer Support: Connect to knowledge base, identify product improvements, auto-create FAQs, provide real-time guidance.
- →Product & Design: Improve product copy, analyze customer sentiment, extract competitor insights, generate user stories.
- →Engineering: Review code, auto-create docs, compile incident timelines, generate SQL.
- →Data & Analytics: Enable non-technical teams to query data, automate reporting, transform insights, connect data sources.
- →Automating recurring business workflows overnight
- →Generating reports, documents and presentations
- →Monitoring uptime, pricing or metrics with auto-actions
- →Running research and content tasks hands-off
- →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
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products