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Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.
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.
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
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No public pricing
- ✦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
- ✦LPU custom inference hardware
- ✦GroqCloud tokens-as-a-service API
- ✦High-speed, low-latency inference
- ✦Pay-as-you-go token pricing
- ✦Free API key to start
- ✦Broad open-model support
- ✦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
- ✦OpenTelemetry-native distributed tracing across 100+ LLMs and frameworks
- ✦Online evaluation via LLM-as-a-judge or code
- ✦Offline experiments and regression detection
- ✦Annotation queues for expert review
- ✦Alerts and drift detection
- ✦Prompt management, CLI and docs MCP server
- ✦Agent trace capture and conversation intelligence
- ✦Semantic and exact-text search across all traces
- ✦Automatic issue discovery with Slack/email/webhook alerts
- ✦OpenTelemetry-compatible SDK with no lock-in
- ✦Automated evals and golden dataset generation
- ✦Failure-mode clustering and MCP server integration
- →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
- →Running LLM inference at high speed
- →Cutting inference costs at scale
- →Powering low-latency AI chat apps
- →Serving models via a hosted API
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →Debugging multi-agent systems
- →Monitoring live agent quality at scale
- →Catching regressions before release
- →Human review of edge cases
- →Aligning automated evaluators with domain experts
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues