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Langtail
✓ verifiedFreemium
Spreadsheet-like prompt management platform for product teams to build, test and deploy AI prompts collaboratively.
7.0K saves
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AIprm
✓ verifiedFreemium
Browser extension adding a community prompt library and prompt management to ChatGPT, Claude and other AI tools.
920K visits/mo
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Claude
✓ verifiedFreemium
Anthropic's AI assistant for writing, coding, and analysis across web, mobile, and desktop, plus a developer API.
22M visits/mo231K saves
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Weights & Biases
✓ verifiedFreemium
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
2.5M visits/mo
Pricing
Free: $0/mo
Pro: $99/mo (1 user)
Team: $499/mo (10 users)
Plus: $10
Pro: $33
Free: $0/mo
Pro: $17/mo (annual; $20 monthly)
Max: from $100/mo
No public pricing
Core features
- ✦Spreadsheet-like prompt interface
- ✦Collaborative prompt building
- ✦Prompt testing and validation
- ✦Model and parameter experimentation
- ✦Performance insights
- ✦Deployment to production
- ✦4000+ community prompt templates
- ✦Private prompt storage and lists
- ✦Team prompt sharing
- ✦Tone and writing-style controls
- ✦Power Continue output controls
- ✦Works with ChatGPT, Claude, Gemini and more
- ✦Conversational writing and editing
- ✦Code generation and debugging (Claude Code)
- ✦Data analysis and visualization
- ✦Web search plus memory across chats
- ✦Connectors and remote MCP integrations
- ✦Extended thinking for complex tasks
- ✦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
Use cases
- →Manage AI prompts across a team
- →Test and compare prompts and models
- →Deploy and monitor prompts in production
- →Speeding up marketing and SEO tasks
- →Reusing and organizing prompts
- →Standardizing team prompts
- →Improving ChatGPT output quality
- →Drafting and refining written content
- →Building and debugging software
- →Analyzing datasets for insights
- →Research and learning support
- →Team and enterprise automation
- →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
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