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Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Google's agentic development platform and IDE for building software with autonomous, Gemini-powered coding agents.
A Japanese AI firm that grew from shogi-AI research into industry ML solutions and a generative-AI platform, HEROZ ASK.
Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
Credit-based AI coding agent that builds full applications from plain-language instructions, including backend, billing, and admin features.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦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
- ✦Agent-first IDE experience
- ✦Autonomous planning and code execution
- ✦Integrated editor, terminal and browser control
- ✦Powered by Google's Gemini models
- ✦High-level developer supervision
- ✦Deep-learning and machine-learning core technology
- ✦HEROZ ASK generative-AI platform
- ✦BtoB and BtoC AI solutions
- ✦BLOOMWORKS product
- ✦Industry AI deployment case studies
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
- ✦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
- →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
- →Building apps with AI agents
- →Automating multi-step coding tasks
- →Prototyping and iterating on software
- →Assisting developers on complex work
- →Deploying generative AI in enterprises
- →Applying ML to industry-specific problems
- →AI-driven business transformation (DX)
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →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