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Credit-based AI coding agent that builds full applications from plain-language instructions, including backend, billing, and admin features.
ByteDance's Coze (Kouzi): an all-in-one AI office assistant for writing, slides, sheets, design, podcasts and images.
Enterprise Work AI platform for company-wide search, an AI assistant and building governed agents across 250+ connectors.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
Free trial available
No public pricing
No public pricing
No public pricing
- ✦Builds complete apps (auth, storage, payments, admin) from natural-language prompts
- ✦Runs on top of multiple frontier coding models
- ✦Retains full project context across sessions for incremental feature additions
- ✦Remote task submission via Slack/Telegram messaging
- ✦'Eco Mode' for lower-cost usage without consuming credits
- ✦VS Code and JetBrains IDE integrations plus a desktop app
- ✦AI writing
- ✦AI presentation/PPT generation
- ✦AI spreadsheets and tables
- ✦AI design
- ✦AI podcast generation
- ✦AI image generation
- ✦Enterprise search across company apps
- ✦Personal AI assistant grounded in work data
- ✦Agent builder, orchestration and governance
- ✦250+ connectors and actions
- ✦Enterprise knowledge graph and hybrid search
- ✦Security controls for scaling AI
- ✦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
- ✦On-demand GPU pods across 30+ GPU types and 31 regions
- ✦Serverless GPU endpoints with sub-200ms cold starts
- ✦Zero idle cost billing for inference workloads
- ✦Multi-node clusters for distributed training
- ✦Persistent network storage for full pipelines
- ✦Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
- →Solo founders building a launchable product without a dev team
- →Developers offloading multi-step feature builds to an autonomous agent
- →Teams wanting a shared coding agent with pooled usage billing
- →Drafting documents
- →Building presentations
- →Generating spreadsheets
- →Creating designs and images
- →Producing podcasts
- →Search across all company knowledge
- →Answer employee questions with grounded AI
- →Build and deploy custom AI agents
- →Automate cross-system workflows
- →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
- →Renting GPUs for model training and fine-tuning
- →Deploying low-latency real-time inference APIs
- →Running AI agents that need to scale instantly
- →Processing compute-heavy batch or distributed workloads