Build pipelines that think.
Describe the pipeline you need and watch Dagy's AI draft it, run it serverless, and defend it in production - retrying, rerouting, and explaining itself so your team never babysits a DAG again.
- No credit card required
- First flow live in 5 minutes
- SOC 2 Type II ready
Drop-in nodes for the stack you already run
01The problem
Your pipelines shouldn't page you at 2 a.m.
One 4,000-line DAG, a web of glue scripts, and a single schema change away from a full restart. Traditional orchestrators were built for batch ETL, not for AI workloads that branch, retry, and evolve every week.
- Monolithic DAGs that break when touched
- Hand-rolled retries, backfills, and glue
- No awareness of LLM steps, costs, or drift
02Composable by design
Break the monolith into micro-DAGs
Dagy pipelines are small, versioned graph modules with typed contracts between them. Ship, test, and scale each flow independently, then reuse them across every team and workload.
- Independently deployable, versioned units
- Typed connectors validated at connection time
- Scale the hot branch, not the whole workflow
03Author your way
Code, canvas, or plain English
Write flows in Python with @flow and @task decorators, drag them together in the visual builder with 27 validated node types, or describe the outcome and let AI Studio draft the graph.
- Python SDK with typed decorators and tests
- Visual builder that validates every connection
- AI Studio: prompt → production-ready draft
@flow(retries=3, schedule="@hourly")def enrich_docs(bucket: S3Path): docs = extract(bucket) entities = llm_extract(docs, model="gpt-4o") return embed_and_index(entities)
“Take inbound PDFs, extract entities with GPT-4o, and push approved records to our CRM.”
Drafting graph · 6 nodes, 3 lanes, retries configured
04Execution engine
Serverless scale, zero babysitting
The BackendRouter sends every task to the right runtime: Lambda for milliseconds, Step Functions for huge fan-outs, Fargate for heavy jobs. Durable state, automatic retries, and mid-run resume come standard.
- Automatic backend selection per task
- Exponential backoff and partial replay
- Durable execution survives any failure
05AI-native to the core
Intelligence in every layer
AI isn't a plugin here: it drafts your graphs, picks the cheapest capable model, explains failures in plain language, and takes routing decisions on live data, across authoring, execution, and observability.
- Prompt-to-pipeline generation in AI Studio
- Cost-aware routing across models and lanes
- AI root-cause analysis on every failed run
06Built for real workloads
From documents to RAG to revenue
The same composable flows, pointed at different problems. Teams run Dagy for document intelligence, retrieval pipelines, CDC syncs, and ML operations, without changing tools between them.
- Start from 57 cookbook flows and templates
- Mix AI steps and classic ETL in one graph
- Swap sources and sinks without rewiring
Confidence-gated: low scores queue for one-click human review.
Incremental re-embedding: only changed chunks pay for tokens.
Schema drift quarantines records instead of killing the run.
07Observability
See every run, explain every failure
Execution traces, per-task timings, cost deltas, and lineage on every run, with AI-suggested remediation when something breaks. No more grepping CloudWatch at midnight.
- Live run timelines and task-level traces
- Cost and latency hotspots per node
- AI root-cause with a suggested fix
08Ship together
From laptop to prod, safely
Shared workspaces, versioned deployments, scoped secrets, and environment promotion with approval gates, so your whole team ships pipelines the way it ships product code.
- Environments with promotion gates
- 4-role RBAC across 22 granular permissions
- A full audit trail on every mutation
- 10:41 mira@ deployed enrich_ai v2.4.1 → staging
- 10:44 ci@ ran 42 contract tests · all green
- 10:52 dev-lead@ approved promotion to prod
- 10:52 system rotated scoped secrets · audit #99,412
Security, scale & enterprise
Ready for the strictest room in the building
The story so far fills the pipeline; this keeps the promise: identity, isolation, auditability, and guarantees designed into the runtime, not bolted on after the pilot.
SSO + RBAC
SAML/OIDC single sign-on with four roles across 22 granular permissions: owner, admin, developer, viewer.
Encrypted secrets
Fernet-encrypted secrets scoped per environment, rotated on promotion, never readable back through the API.
Audit + retention
Every mutation logged with actor, before/after, and reason. Retention windows you control, exports on demand.
Private / BYOC deployment
Run Dagy Cloud, or the same control plane inside your own AWS account and VPC. Your data never leaves.
Quotas + 99.9% SLA
Plan-based quotas, usage metering, and an uptime SLA with a dedicated support channel on enterprise plans.
Scale without ceremony
1M+ runs a day across customers, 42ms p99 dispatch, and independent scaling per micro-DAG, not per cluster.
“We deleted 4,000 lines of Airflow glue the month we migrated. Six micro-DAGs, each owned by the team that runs it - and nobody has been paged since March.”
“The AI Studio draft was 90% of our document pipeline. We tuned two nodes, set the confidence gate, and shipped to prod the same week.”
“Cost-aware routing paid for the platform by itself. Same quality, 38% less model spend - and the finance team can finally see cost per run.”
Pricing
Start free, scale when the pipeline does
All plans include a 14-day Pro trial. No credit card required on 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
For teams running live AI workflows that need observability, reuse, and speed.
- 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)
For regulated or high-throughput environments that need control and guarantees.
- Unlimited workloads
- Private / BYOC deployment
- 99.9% uptime SLA
- SSO + RBAC
- Audit and retention controls
- Custom integrations
- Dedicated support channel
- Architecture review
Questions
Everything teams ask before the first run
01What exactly is Dagy?
Dagy is an AI-native orchestration platform that lets you build, run, and monitor micro-DAG pipelines. Unlike traditional workflow tools, Dagy treats AI as a first-class citizen across authoring, execution, and observability.
02How is Dagy different from Airflow or Prefect?
Traditional orchestrators were built for batch ETL. Dagy was built for the AI era: micro-DAG architecture, AI-native nodes, natural-language pipeline generation, and cost-aware execution.
03What does AI-native actually mean?
AI is embedded in authoring, execution, and observability. You can describe pipelines in English, let runtime intelligence optimize them, and get AI-powered root-cause analysis when something breaks.
04Do I need to know how to code to use Dagy?
Not necessarily. You can build visually or with natural language. Power users can drop into the Python SDK and YAML DSL whenever they want full control.
05What integrations does Dagy support?
LLM providers, vector databases, warehouses, queues, object storage, and notification systems - plus custom HTTP and Python nodes for everything else.
06Can I self-host Dagy?
Yes. Enterprise plans support Bring-Your-Own-Cloud deployments in your own AWS, GCP, or Azure account, with the same control plane.
07Is Dagy production-ready?
Dagy is in public beta and already orchestrates over a million pipeline runs per day across customers, with SLAs available on paid plans.
08How do I get started?
Start on the free plan, run your first flow in minutes with the quickstart, and lean on the docs or onboarding support as you go deeper.
Ship the pipeline you've been sketching in notebooks
Start free, connect your stack, and watch Dagy run a real AI workload - composed, observed, and explained - before you commit to a migration.
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