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Applied-AI studio and R&D group building enterprise products like the FeatureOS feedback platform for product teams.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
A paid gateway to premium OpenAI models with free daily credits, positioned as a ChatGPT Pro alternative.
Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.
Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
Free trial available
Free trial available
- ✦In-house product line including FeatureOS and SupportWire
- ✦Feedback board, roadmap, changelog, and knowledge base modules (FeatureOS)
- ✦AI-powered feedback analysis and duplicate detection (FeatureOS)
- ✦API and webhook access for custom integrations
- ✦Applied AI R&D consulting engagements
- ✦Product engineering services for client teams
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- ✦Access to premium OpenAI models
- ✦Daily free credits
- ✦Subscription and pay-as-you-go options
- ✦Model picker for thinking/pro models
- ✦Invite-based bonus credits
- ✦Serverless GPU compute defined in Python
- ✦Sub-second container cold starts
- ✦Autoscale 0 to 1000+ GPUs
- ✦Inference, training and batch workloads
- ✦Secure sandboxes for untrusted code
- ✦Built-in logging and observability
- ✦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
- →Product teams collecting and prioritizing customer feedback (via FeatureOS)
- →Companies needing custom applied-AI research or engineering
- →Startups outsourcing product engineering to a specialist studio
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →Use OpenAI Pro-tier models affordably
- →Solve complex reasoning problems
- →Occasional AI use on free daily credits
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes
- →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