Build Pipelines
That Think.
AI-native orchestration for modern data teams. Compose flows, run them in parallel, and let AI optimize every step, from ingestion to embedding.
Traditional orchestrators were built for batch ETL. You're building something else.
Monolithic DAGs break when touched. Infra overhead slows shipping. LLMs are bolted on as afterthoughts. We rebuilt the runtime for the AI era.
Legacy orchestrators Before
DAGY After
Four primitives. One runtime.
Compose flows from graph-native units. Treat LLM calls, vector lookups, and routing decisions as first-class citizens, not glue code wrapped around a cron job.
Composable by design
Break ingestion, validation, enrichment, and retrieval into graph units you can deploy, version, and scale independently. No more 2,000-line DAG files.
from dagy import flow, task @task def extract(pdf): return parse(pdf) @task(provider="openai", model="gpt-4o") def enrich(text): return llm(text) @flow def document_pipeline(pdf): doc = extract(pdf) return enrich(doc) # AI-native task
Stateful or stateless
Mix stateless transforms with long-lived execution paths without changing tools halfway through.
Independently scalable
Scale only the expensive or noisy branch, not the entire workflow around it.
AI-aware nodes
Treat model calls, routing decisions, and vector lookups as graph-native runtime behavior. Cost-route between providers, cache embeddings, and retry on malformed outputs without custom glue.
# Runtime behavior, declared not coded @task(retry="on_malformed_json", cost_route=["haiku", "sonnet"], cache="embedding")
Everything you need to ship AI workflows.
One opinionated runtime. Zero glue code. Every feature built for teams who already know a DAG when they see one.
Flow Composer
Model long-running AI workflows as small graph modules you can version, test, and reuse. Python SDK, YAML DSL, or the visual builder. Pick what fits the team.
AI-assisted builder
Turn prompts and docs into a starting graph, then tune the runtime instead of wiring boilerplate.
Event-driven triggers
Product events, inbound documents, schedules, webhook bursts, without separate glue code.
Durable execution
Recover mid-run state, replay safely, and keep multi-step AI jobs alive through retries and partial failures.
Observability + tracing
Inspect every run with execution traces, cost deltas, latency hotspots, and AI-suggested remediation. Connect OpenTelemetry or ship logs to your own stack.
▸ ingest_pdf 1.02s · $0.000 ▸ extract_text 2.14s · $0.000 ▸ enrich (gpt-4o)3.82s · $0.019 ✓ cached ▸ push_crm 0.41s · $0.000
LLM + vector DB integrations
Work directly with model providers, embedding services, and vector stores as graph-native nodes.
Multi-cloud deployments
Run the same model in DAGY Cloud, inside your VPC, or on your existing cloud footprint.
Enterprise security
RBAC, SSO, BYOC, audit trails, retention controls. Governance lives next to the runtime, not bolted on later.
From weekend prototype to enterprise runtime.
DAGY scales with the team. Same runtime, same model, different guardrails.
Go from prompt to pipeline in one sitting.
Prototype ingestion, agent, and retrieval flows without provisioning clusters or hand-rolling retries.
- Prompt, code, or visual authoring
- Local and hosted execution
- Pay only for what you run
- CLI-first workflow
- Fast iteration loop
Keep AI launches moving without workflow debt.
Share reusable DAG blocks, promote changes safely, and keep runtime behavior visible as traffic grows.
- Shared workspaces + permissions
- Reusable graph modules
- Observability + alerts
- Versioned deployments
- Priority support
Bring orchestration under your guardrails.
Run DAGY behind your network, attach governance, and give application teams a faster runtime without giving up control.
- RBAC + SSO
- Audit and retention controls
- Private / BYOC deployment
- Runtime policy guardrails
- SLA + dedicated support
Simple pricing. Usage-based where it matters.
Start free with no credit card. Scale when the workload does.
Free
Validate DAGY with one production-shaped workflow and no procurement friction.
- 3 active micro-DAGs
- 500 runs / month
- Local + cloud execution
- Core observability
- CLI + API access
- Community support
Pro
For teams running live AI workflows that need observability, reuse, and faster iteration.
- Unlimited micro-DAGs
- 10,000 runs / month
- Prompt-to-graph generation
- Advanced traces + optimization hints
- Shared workspaces
- Versioned deployments
- Webhook + schedule triggers
- Priority support (< 4h SLA)
Enterprise
For regulated or high-throughput environments that need control, guarantees, and private deployment.
- Unlimited workloads
- Private / BYOC deployment
- 99.9% uptime SLA
- SSO + RBAC
- Audit and retention controls
- Custom integrations
- Dedicated support channel
- Architecture review
Questions, answered.
01What exactly is DAGY?+
02How is DAGY different from Airflow or Prefect?+
03What does AI-Native actually mean?+
04Do I need to know how to code to use DAGY?+
05What integrations does DAGY support?+
06Can I self-host DAGY?+
07Is DAGY production-ready?+
08How do I get started?+
Ship the workflow
you've been sketching in notebooks.
Start free, connect your stack, and see how DAGY behaves with a real AI workload before you commit to a migration.