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✕
Runable
✓ verifiedPaid
General AI agent executing tasks and integrating thousands of apps; very high traffic.
955K visits/mo125K 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
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Higress
✓ verifiedFreemium
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
29K visits/mo
Pricing
No public pricing
No public pricing
No public pricing
No public pricing
Core features
- ✦Create presentations, websites, reports, slides, documents, podcasts images & videos
- ✦Conduct deep research on any topic
- ✦Command a virtual computer to browse the web
- ✦Connect and automate with over 2700+ apps
- ✦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
- ✦Open-Source AI Gateway
- ✦Multi-LLM Management & Cost Optimization
- ✦Efficient and Secure LLMs Invocation
- ✦Unified API Signature for LLMs
- ✦Load Balancer for seamless switching between LLMs
- ✦Fine-Grained Traffic Control for LLMs
- ✦LLM Quota Management
- ✦Real-time LLM Traffic Monitoring
- ✦Caching Strategies for AI in Production
- ✦Flexible Prompt Management
- ✦Unified proxy and protocol conversion across 100+ LLMs
- ✦Model-level fallback and routing
- ✦Semantic and exact-match AI caching
- ✦Token tracking and quota controls
- ✦Content-safety and data-protection filtering
- ✦MCP service hosting and plugin marketplace
Use cases
- →Generate a high-conversion landing page for a startup's ebook.
- →Build a web-based image cropper tool with live preview.
- →Create a beginner-friendly PDF cheatsheet for Python.
- →Develop a one-week travel itinerary for a destination like Vietnam.
- →Analyze social media mentions and sentiment for a specific entity.
- →Organize hiring task submissions from emails into a Google Sheets spreadsheet.
- →Clone a pixel-perfect website for design accuracy and responsive web experience.
- →Make a podcast on the rise of SpaceX, make it conversational and include fun facts.
- →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
- →Building API portals for secure sharing of internal APIs with partners.
- →Tracking API usage and driving API monetization.
- →Managing and securing API access in compliance with enterprise policies.
- →Connecting to multiple AI large models simultaneously.
- →Optimizing LLM costs and improving efficiency.
- →Protecting against LLM attacks and data leaks.
- →Centralizing access to multiple LLM providers
- →Building and governing AI agent/MCP services
- →Controlling token spend across teams
- →Adding caching and safety to LLM calls
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