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Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Enterprise Work AI platform for company-wide search, an AI assistant and building governed agents across 250+ connectors.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
All-in-one digital-safety subscription protecting families from identity theft, fraud and online threats, with parental controls.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Prompt management as a single source of truth
- ✦Playground for prompt experimentation
- ✦Evaluation to measure changes before production
- ✦Observability and tracing for debugging
- ✦Collaboration across technical and non-technical roles
- ✦Open-source and self-hostable
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- ✦Enterprise search across company apps
- ✦Personal AI assistant grounded in work data
- ✦Agent builder, orchestration and governance
- ✦250+ connectors and actions
- ✦Enterprise knowledge graph and hybrid search
- ✦Security controls for scaling AI
- ✦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
- ✦Identity theft protection with insurance
- ✦3-bureau credit monitoring and lock
- ✦Antivirus, VPN and password manager
- ✦Online data removal from brokers
- ✦Parental controls and safe-gaming alerts
- ✦Dark-web and financial-fraud alerts
- →Version and manage prompts centrally
- →Benchmark and evaluate LLM outputs
- →Debug and trace production LLM issues
- →Collaborate across a team on LLM apps
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Search across all company knowledge
- →Answer employee questions with grounded AI
- →Build and deploy custom AI agents
- →Automate cross-system workflows
- →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
- →Protecting against identity theft
- →Monitoring family credit and finances
- →Keeping kids safe online
- →Removing personal data from broker sites