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Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
Turns Git commits and PRs into AI-summarized daily or weekly reports delivered to Slack or email, no source access.
Privacy-focused AI platform offering uncensored chat, image, video, and audio generation across many third-party and open-source models.
Free trial available
No public pricing
No public pricing
Free trial available
- ✦One-line API calls to run community and proprietary AI models
- ✦Support for image, video, speech, and LLM generation models
- ✦Fine-tuning and custom model deployment via Cog
- ✦Per-second usage billing on shared or dedicated hardware
- ✦Automatic scaling for high-traffic private models
- ✦Thousands of community-published models with production APIs
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
- ✦AI-summarized commit and PR reports
- ✦Daily and weekly scheduled digests
- ✦Slack and email delivery
- ✦One-click OAuth or webhook setup
- ✦GitHub, GitLab and Bitbucket support
- ✦Templates for standups and reports
- ✦Multi-provider chat, image, video, and audio generation
- ✦Zero data retention on private/self-hosted models
- ✦OpenAI-compatible API for agent integrations
- ✦Tiered privacy levels including TEE and end-to-end encryption
- ✦Uncensored open-source model access
- ✦Credit-based access to premium third-party models
- →Developers embedding image/video/speech generation into an app via API
- →Teams deploying and scaling their own fine-tuned models
- →Builders comparing outputs from multiple AI models in one playground
- →Companies avoiding GPU infrastructure management for ML inference
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Keep stakeholders updated on what shipped
- →Replace manual status updates and standups
- →Give teams visibility into Git activity
- →Chatting or generating content without prompt logging
- →Building AI agents via an OpenAI-compatible API
- →Generating images or video with fewer content restrictions
- →Accessing many leading AI models under one subscription