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Pay-per-use API hub aggregating 1000+ image, video, and audio generation models for developers building AI media pipelines.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Unified pay-per-generation API for 500+ image, video and audio models like FLUX, Kling and Seedance at low cost.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
Free trial available
No public pricing
No public pricing
- ✦Unified API access to 1000+ image/video/audio generation models
- ✦Pay-per-use pricing billed per image or per second of video
- ✦Includes chat/LLM model access (Claude, GPT, Gemini, etc.) priced per token
- ✦Account tiers unlock higher GPU limits and concurrency
- ✦CLI and desktop app for building workflows
- ✦Enterprise options with dedicated support and custom deployment
- ✦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
- ✦Single API for 500+ image, video and audio models
- ✦Pay-per-generation billing with no subscription
- ✦No charge on failed tasks
- ✦Workflows, agents and studio tools
- ✦MCP and CLI integrations, white-label option
- ✦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
- ✦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
- →Integrating AI image/video generation into an app via API
- →Building automated content pipelines needing multiple AI models
- →Testing and comparing many generative models from one account
- →Scaling AI media production with volume-based account tiers
- →Accessing both media-generation and LLM APIs from one platform
- →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 on top of many generative models via one API
- →Generating images, video and audio at scale
- →Cutting model API costs versus direct providers
- →Deploying white-label AI generation studios
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →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