Union.ai

What Union.ai does

Union.ai provides an AI runtime that unifies data, models, and compute, offering the fastest path to production-ready AI systems. It orchestrates extremely scalable AI, ML, and agentic systems, built on Flyte, the open-source standard for pipeline orchestration. The platform accelerates AI/ML workflows and agents from experiment to production, enabling on-demand resource provisioning and scaling. It supports effortless scaling of model training across clusters with automatic caching and reproducibility, and performs real-time inference with ultra-low latency. Union.ai offers comprehensive observability across the development cycle, including cost and usage allocation dashboards, and easily discoverable logs and failures. The platform is designed for AI builders, providing tools to accelerate engineers, such as reusable containers for fast task startup and live remote debugging. It emphasizes security, privacy, and compliance with enterprise-grade security and fine-grained access control, featuring a Zero Trust architecture where customer data never transits Union.ai infrastructure.

Products

• Union.ai PlatformA managed, production-grade platform for advanced AI workloads, unifying data, models, and compute. It provides orchestration, training & fine-tuning, inference, and observability capabilities for AI/ML workflows and agents.
• FlyteThe open-source standard for pipeline orchestration, forming the foundation of Union.ai. It enables building AI workflows in pure Python with built-in durability, reproducibility, and recovery.

Key capabilities

• MLOps
• ML orchestration
• AI infrastructure
• Data pipelines
• AI pipelines
• ML infrastructure
• Compound AI systems
• AI systems
• Inference
• Unify data, models, and compute
• Orchestrate extremely scalable AI, ML, and agentic systems
• Accelerate AI/ML workflows and agents from experiment to production
• Provision and scale resources on-demand
• Scale model training effortlessly across clusters
• Automatic caching and reproducibility
• Real-time inference with ultra low latency
• Visibility across the development cycle
• Cost and usage allocation dashboards
• Easily discoverable logs and failures
• Deep integration with data, models, and compute
• True agency for AI/ML workflows at runtime
• Author in pure Python with custom branching, looping, automatic retries
• Durable workflows that recover from failure automatically
• Monitor and debug with logs streamed to the UI
• Integrate with any cloud, any model, unstructured data
• Reusable containers for <100ms task startup time
• Live remote debugging
• Secure, private, and compliant by design
• Enterprise-grade security and fine-grained access control
• Zero Trust security architecture
• Data never transits Union.ai infrastructure
• Infra-aware orchestration
• Dynamic, Python-based workflows
• Integrated cost tracking
• Debugging & error handling
• Advanced debugging, automatic retries
• Output caching
• Task-level overrides
• Scalability (actions/run, concurrent actions)
• Multi-cloud and multi-region support
• SSO (OIDC, SAML/p)
• Role-based access control (standard, fine-grained, custom)
• Managed secrets
• VPC (Union-managed, self-managed)
• Self-hosted control plane deployment option
• Per-task container images
• Mixed CPU, GPU, and high-memory nodes in a single workflow
• Typed data handoffs
• Input-hash caching
• Intra-task checkpointing
• Per-task resource requests
• Artifact versioning with automatic lineage
• Immutable execution records
• Flyte-Native Agent Construct
• Multi-Pod Log Streaming
• Build and Deploy MCP Servers
• Smarter Failure Classification
• Faster CLI Startup and Unified Local Caches
• Friendlier Build and Deploy Errors
• More Resilient Uploads
• SDK Reliability Improvements
• Exclude Files from Code Bundles with .flyteignore
• Settings Applied at Run Creation
• Console Run Exploration Improvements

Use cases

• Scaling geospatial AI
• Visualizing flight prices with Python orchestration
• Scaling agentic research
• Scaling autonomous driving
• Accelerating drug discovery
• Scaling personalized cancer therapy
• Accelerating data and ML operations
• Accelerating infectious disease diagnostics
• Cutting quarterly forecast time
• Accelerating ML development for protein design
• Accelerating ML workflow delivery
• Reducing orchestration costs
• Orchestrating global methane reduction
• Cutting pipeline compute costs
• Scaling Earth’s Digital Twin
• Scaling ML operations
• Building AI from experiment to production
• Orchestrating complex AI, ML, and agentic systems
• Model training and fine-tuning
• Real-time inference
• Data processing
• Building durable workflows
• Debugging remote tasks
• Managing petabytes of sensor data
• Handling hundreds of thousands of node hours
• Managing large-scale distributed workloads
• Data annotation
• Perception labeling
• Dynamic scene processing
• GPU-intensive training
• Backfilling and recomputation of models
• Metagenomic diagnostics
• Parameter sweeps and in silico experiments
• Alignment-stringency studies
• Forecasting workflows
• Building dashboards, REST APIs, and model endpoints
• Implementing ReAct, Plan-and-Execute, and other agent patterns
• Safely executing LLM-generated code
• Serving Model Context Protocol servers for AI assistants

AI approach

Union.ai provides an AI runtime and orchestration platform for building, deploying, and scaling AI, ML, and agentic systems. It focuses on unifying the AI development cycle (orchestration, training & fine-tuning, inference, observability) and offers features like dynamic Python-based workflows, resource provisioning, automatic caching, reproducibility, real-time inference, and cost tracking. It integrates with various ML tools and cloud providers, allowing users to run their AI workloads in their own cloud environments.

Tech named: Flyte, MLOps, ML orchestration, AI infrastructure, data pipelines, AI pipelines, ML infrastructure, Compound AI systems, AI systems, Inference, AlphaFold, RDKit, GATK, Legacy C++, Python, asyncio, torch, ESMFold, Spark, Ray, Dask, PyTorch, Kubernetes, Airflow, Polars, LangGraph, PydanticAI, OpenAI Agents SDK, LLM, Model Context Protocol (MCP), FastAPI, Streamlit, vLLM, SGLang, Ollama

Industries served

• Geospatial
• Logistics
• Autonomous Systems
• Biotech & Healthcare
• Financial Services & Fintech
• Media & Entertainment
• Software Development
• Insurance and Home Services

What it says sets it apart

• The only AI runtime that runs in your cloud, not ours
• 9.8x ROI according to analysts
• Simple pricing for complex AI systems with monthly credits and predictable costs
• Usage billed as a combination of actions and action-allocated resources, never for idle capacity
• Built on Flyte, the OSS standard for pipeline orchestration
• Zero Trust security architecture: data never transits Union control plane, runs entirely inside customer's secure cloud
• Contractual data isolation guarantee
• Support for clinical-grade reproducibility and auditability (e.g., for biotech)
• Strong typing on all task inputs and outputs to prevent dataflow mismatches
• Input-hash caching to avoid re-running computations for identical inputs
• Intra-task checkpointing for long-running jobs to resume from failure
• Per-task resource requests for optimized compute allocation (e.g., GPU only when needed)
• Automatic versioning of workflows, containers, and artifacts for traceability
• Pure Python workflow authoring, eliminating YAML or DSLs
• Managed infrastructure that abstracts Kubernetes complexity
• Seamless migration from Airflow without rewriting legacy code using Flyte Agents
• Accelerates experimentation by 200% through workflow versioning
• Clear error propagation and UI insights for self-sufficient troubleshooting
• GitOps-native integration for full traceability of workflow lineage
• Containerized task execution for clean dependency isolation
• Support for multi-cloud and multi-region deployments
• Comprehensive support tiers including white-glove support and dedicated AI Solutions Engineers
• Fast iteration from notebook exploration to production workflows in hours/days

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.