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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

Groq logo
Groq
✓ verifiedFreemium

Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.

3.6M visits/mo
Manus logo
Manus
✓ verifiedFreemium

General-purpose autonomous AI agent that plans and runs multi-step tasks such as building sites, slides and research in the cloud.

28M visits/mo89K saves
Weights & Biases logo
Weights & Biases
✓ verifiedFreemium

Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.

2.5M visits/mo
Coze logo
Coze
✓ verifiedFreemium

ByteDance's Coze (Kouzi): an all-in-one AI office assistant for writing, slides, sheets, design, podcasts and images.

7.2M visits/mo
Pricing
GPT-OSS 20B: $0.075 per 1M input tokens ($0.30 per 1M output)
GPT-OSS 120B: $0.15 per 1M input tokens

No public pricing

No public pricing

No public pricing

Core features
  • LPU custom inference hardware
  • GroqCloud tokens-as-a-service API
  • High-speed, low-latency inference
  • Pay-as-you-go token pricing
  • Free API key to start
  • Broad open-model support
  • Autonomous multi-step task execution
  • Website and app building
  • AI slides, design and image generation
  • Manus browser operator
  • Wide Research mode
  • Cross-platform web, desktop and mobile 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
  • AI writing
  • AI presentation/PPT generation
  • AI spreadsheets and tables
  • AI design
  • AI podcast generation
  • AI image generation
Use cases
  • Running LLM inference at high speed
  • Cutting inference costs at scale
  • Powering low-latency AI chat apps
  • Serving models via a hosted API
  • Automate end-to-end digital tasks
  • Produce websites and presentations
  • Conduct broad research
  • Hand off browser tasks to an agent
  • 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
  • Drafting documents
  • Building presentations
  • Generating spreadsheets
  • Creating designs and images
  • Producing podcasts
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