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Runware
✓ verifiedFreemium
Pay-as-you-go API aggregating thousands of image, video, audio and LLM models with custom inference hardware for lower per-request cost.
249K visits/mo
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AnythingLLM
✓ verifiedFree
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
682K visits/mo
Pricing
No public pricing
Pay-as-you-go: 42.8% platform fee for corporate, 33.3% for academic/non-profit (no monthly fee)
Participant payment: minimum $8.00/hr, recommended $12.00/hr
vCPU compute: $0.016/hr
RTX PRO 6000: $1.99/hr (as low as $0.99)
H100: $2.76/hr
H200: $3.18/hr
B200: $4.99/hr
Free trial available
No public pricing
Core features
- ✦Markdown to Notion publishing
- ✦Automatic subpage creation from directory structure
- ✦CLI flag support for title and emoji
- ✦Integration with Notion's AI, search, and formatting
- ✦Access to a verified and engaged participant pool
- ✦Self-serve platform for easy task setup and launch
- ✦Tools for AI training and evaluation
- ✦Fair compensation for participants
- ✦Audience checker
- ✦Single API for image, video, audio, 3D and LLM models
- ✦Standardized model addressing across hosted, partner and custom uploads
- ✦Support for LoRAs, ControlNets, VAEs and embeddings on open-source models
- ✦WebSocket and REST access with async webhook delivery
- ✦Pay-per-request billing with no infrastructure to manage
- ✦Raw serverless GPU/CPU compute for custom workloads
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
Use cases
- →Publishing documentation to a Notion workspace
- →Creating a public website from markdown documentation
- →Maintaining documentation alongside code in a repository
- →Academic research
- →AI training and evaluation
- →Market research
- →User research & testing
- →Data annotation
- →Training & alignment
- →Evaluation & safety
- →Adding AI image or video generation to an app without managing infra
- →Batching multi-modal generation tasks in one API call
- →Running custom fine-tuned models via Model Upload
- →Cutting inference costs at high generation volume
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
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