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Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
A Japanese AI firm that grew from shogi-AI research into industry ML solutions and a generative-AI platform, HEROZ ASK.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
Free trial available
No public pricing
No public pricing
No public pricing
- ✦Unified access to 100+ LLMs in OpenAI format
- ✦Cost/spend tracking per key, user and team
- ✦Budgets and rate limiting
- ✦Automatic provider fallbacks and retries
- ✦Virtual keys and team management
- ✦Logging and observability integrations
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- ✦Deep-learning and machine-learning core technology
- ✦HEROZ ASK generative-AI platform
- ✦BtoB and BtoC AI solutions
- ✦BLOOMWORKS product
- ✦Industry AI deployment case studies
- ✦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
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Deploying generative AI in enterprises
- →Applying ML to industry-specific problems
- →AI-driven business transformation (DX)
- →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
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems