Dagster manages your data assets. Avalanche manages the work your agents do.

Quick answer

Updated October 27, 2026. Dagster is an Apache-2.0 data orchestrator: data engineers declare tables, files and models as assets in Python, and Dagster keeps them up to date with lineage, partitions, backfills and asset checks, self-hosted or on the managed Dagster+.[1, 2, 5, 14] Avalanche brings that discipline to agentic business work: you set a few coarse steps, checks and approvals, agents do the work inside each step, and every step commits a new version of the run's document workspace, so you can see which step changed which file.[20, 25] Choose Dagster for data pipelines in a Snowflake, BigQuery or dbt stack; choose Avalanche for long, document-heavy workflows where agents do the work and every run must be inspectable and comparable across runs and versions.[25, 29]

Key takeaway

Choose Avalanche when agents should do the work inside a few business steps you control, over PDFs, Word files and spreadsheets, with every run comparable by version.

Choose Dagster when data engineers need asset lineage, partitions, backfills and a mature, generally available data platform today.

  • Avalanche wins on agents inside steps: an agent step is first-class, with typed outputs, Claude Agent SDK support and Agent Skills (SKILL.md); in Dagster, a model call is code inside an asset.
  • Avalanche wins on checks and approvals: checks every run must pass and owner approval before results go out; Dagster has strong blocking asset checks but documents no built-in human approval step.
  • Dagster wins on partitions, backfills and data integrations: time, static, two-dimensional and dynamic partitions, backfills from the lineage graph, and a large integrations library.
  • Tie on rollback: both keep deploy history and roll back to a previous version from the UI without rebuilding your code.

By , CTO, Trampoline AI. Updated · Facts checked · Published by the Avalanche team. Avalanche competes with Dagster.

01 / The core differenceAvalanche vs Dagster

Data assets for data engineers, or agentic business workflows for your team.

Dagster keeps data assets, such as tables, files and machine learning models, up to date, and records how each one was produced.[1, 2] Avalanche works at a higher level for business work: you set the few steps, checks and approvals that matter, agents do the work in between, and each step checkpoints its typed outputs and its files.[21, 25]

Dagster's unit is the data asset: a table, file or model that a Python function produces and that Dagster keeps fresh, partition by partition, with lineage between assets.[2, 5] Avalanche's unit is a step of business work: an agent reads documents, drafts an answer or reviews a contract inside a few steps you set, with checks and an owner's approval around it.[25] Both record how a result was produced. Dagster tracks lineage between assets across your data platform; Avalanche tracks which step of which run produced or changed each document, and lets you compare that step across runs and versions.[10, 25]

02 / At a glanceChecked 2026-10-27

Avalanche and Dagster, side by side.

Avalanche and Dagster compared, checked 2026-10-27
CriterionAvalancheDagsterEdge
Agents inside stepsCoarse business steps where an agent does the work, with typed outputs; agent steps run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md); the Avalanche skill drafts a workflow from a described outcome.[22, 25, 26, 27]No first-class agent step: a community Anthropic resource calls the API inside an asset. The dagster-expert skill and an MCP server help coding agents write Dagster code and inspect deployments.[12, 13]Avalanche edge
LineageAvalanche Cloud: every run gets a versioned workspace; each step's file changes are committed as a new version, so you can see which step changed a file, compare versions and download any step's files.[25]Asset-level lineage across your data platform, column lineage on paid plans, and a code version and data version for each materialization.[2, 3, 10]Different jobs
Partitions, backfills and data integrationsIceberg and Lance tables and incremental streams in the open-source runtime; no partition or backfill manager.[20]Time, static, two-dimensional and dynamic partitions; backfills per partition or in one run; a large integrations library for dbt, warehouses and ELT tools.[1, 5, 6, 29]Dagster edge
Inspecting and comparing runsAvalanche Cloud: typed inputs and outputs at every step checkpoint, in one run history filtered by status, flow and deployment; a web console with a live DAG of every flow and run, the inputs and outputs of any step in one click, and the full console in your terminal, so you can compare a step across many runs and versions.[21, 25]Run history with re-execution, asset activity per partition and asset change history; Dagster+ Insights compares success rates and step times across up to five asset selections over 30 days.[5, 8, 16, 19, 31]Different jobs
Checks and approvalsChecks every run must pass and owner approval before results go out, on Avalanche Cloud.[25]Asset checks test data quality and can block downstream assets; the docs describe no built-in human approval step.[9]Avalanche edge
Deploys, versions and rollbackAvalanche Cloud: each deploy is a frozen version of the whole workflow, mapped to an exact git commit, with locked, hash-verified dependencies; running workflows finish on the version they started on; instant rollback to any previous version from the CLI or console, no rebuild; an audit trail of who promoted or rolled back.[25]Dagster+ keeps a history of every code location deploy with its commit and changed assets, and rolls back to a previously deployed image from the UI.[8]Similar
Availability, previews and licenseSelf-hostable MIT-licensed runtime with operator, web console, REST API and terminal UI, more limited than Cloud; Avalanche Cloud is on a waitlist, with separate projects but no automatic previews.[20, 24, 28]Apache-2.0, self-hosted or generally available on Dagster+ Serverless or Hybrid, with branch deployments for every pull request.[1, 4, 14]Dagster edge
Schedules, webhooks, CLI and APIAvalanche Cloud: cron schedules and webhooks declared in your workflow code, following rollbacks and promotions; webhook URLs never change on redeploy, redeliveries are deduplicated for 7 days, and idempotency keys return the same run for 7 days; a typed REST API with an OpenAPI spec, and a CLI.[25]Cron schedules, sensors that poll for events such as a new file, declarative automation conditions, a GraphQL API that is still evolving, and the dg CLI.[3, 7, 17, 18]Similar
Identity and accessAvalanche Cloud: SSO; organization roles and per-action permissions, managed in your organization's settings; scoped, encrypted secrets; agents see only what each step passes them (its inputs, its files and the secrets it declares); steps run non-root in a sandbox, cut off from cloud metadata and private networks; an audit trail of promotions and rollbacks; no spend limits yet.[25]Dagster+ Pro: SCIM provisioning (Okta, Entra ID), granular per-project roles, an organization audit log of logins, permission, secret and schedule changes, and SIEM log export; spend limits are not documented.[3, 15]Dagster edge
03 / Where each one winsOne claim per card

Where each one wins

Where Avalanche wins

  • Agents do the work inside each step

    You set the few business steps, checks and approvals that matter; an agent does real work inside each step. Python steps keep exact rules and arithmetic.[20, 25]

  • Checkpointed Claude agent steps

    Agent steps can run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md), each sandboxed and checkpointed like any other step.[26, 27]

  • Document lineage, step by step

    Avalanche Cloud gives every run a versioned workspace. Pass one document or hundreds; each step works on the files directly, and every step's changes are committed as a new version of the workspace, so you can see which step produced or changed each file, compare versions, and download any step's files. Document lineage, the way data teams have table lineage. Agent steps get PDF, Word and spreadsheet skills.[23, 25]

  • Owner approval before results go out

    On Avalanche Cloud, a workflow can require checks every run must pass and an owner's approval before results leave the workflow.[25]

  • Runs your business team can compare

    Each step is a business result with typed outputs, such as an RFP answer or a contract finding. Avalanche Cloud keeps every run by workflow and version, so you can compare one step across many runs.[21, 25]

  • From a skill to a workflow

    Install the Avalanche skill with npx skills add Trampoline-AI/avalanche, describe the outcome in your coding agent, and it drafts the workflow with you.[22]

Where Dagster wins

  • Asset lineage and catalog

    Assets declare their upstream dependencies, so Dagster draws a global asset lineage graph; Dagster+ adds column lineage on paid plans and a searchable asset catalog.[2, 3, 10]

  • Partitions and backfills

    Time, static, two-dimensional and dynamic partitions, with backfills launched from an asset or the lineage graph, one run per partition or a single run.[5, 6]

  • Data platform integrations

    A large library of Dagster and community integrations, from dbt, Airbyte and Fivetran to Snowflake, BigQuery and Databricks, plus Insights cost tracking for BigQuery and Snowflake on Pro.[1, 3, 16, 29]

  • Branch deployments

    Dagster+ creates an ephemeral preview deployment for every pull request, rebuilt on each push, and marks the assets that changed against production.[4]

  • Mature and generally available

    Apache-2.0 open source with 16.3k GitHub stars, self-hosted or on Dagster+ Serverless or Hybrid, with a 30-day free trial.[1, 3, 14]

  • Audit log and provisioning

    Dagster+ Pro adds SCIM provisioning, granular per-project roles, an organization audit log of logins, permission, secret and schedule changes, and SIEM log export.[3, 15]

04 / Detailed comparison4 buyer questions

What changes for your team.

01

Data assets or agentic business work?

Buyer question

Is the output a table your data platform depends on, or a business result an agent produces from documents?

Dagster is built for data assets. An asset is an object in persistent storage, such as a table, file or model; you declare it as a Python function, and Dagster runs it at the right time, records each materialization and draws lineage between assets.[1, 2] Schedules, sensors and declarative automation keep assets fresh, and partitions with backfills handle history at scale.[5, 6, 7] That is the right tool for warehouse, dbt and ELT pipelines.

Avalanche is built for long, multi-step business work that agents do, such as drafting RFP answers from past proposals or reviewing a stack of contracts. You set the few steps, checks and approvals that matter, agents do the work in between, and simple workflows fit the same model. On Avalanche Cloud, workflows run for hours or days, and an owner approves results before they go out.[25] You can call a model from a Dagster asset through the community Anthropic resource, but the agent loop, its documents and its approval are code you write.[12]

02

Asset lineage and document lineage

Buyer question

When a result looks wrong, can you trace it back to the step and the version that produced it?

Dagster traces data across your platform. Each asset knows its upstream assets, the lineage graph shows them, and each materialization carries a code version and a data version; paid plans add column lineage and a searchable catalog.[2, 3, 10] Dagster+ also keeps a change history for each asset definition.[8]

Avalanche traces documents through a run. Avalanche Cloud gives every run a versioned workspace: each step works on the files directly, and every step's changes are committed as a new version, so you can see which step produced or changed each file, compare versions and download any step's files.[25] Each step also keeps typed outputs, so a team can compare that step across hundreds of runs and see which outputs drift or which version changed them, the way data teams moved from one-off queries to managed transformations.[21, 25]

03

Agents in Dagster and in Avalanche

Buyer question

Do you want AI to help operate your pipelines, or agents to do the work inside them?

Dagster's AI features help engineers build and operate pipelines. The open-source dagster-expert skill and the Dagster+ MCP server let coding agents such as Claude Code write Dagster code, launch runs and read logs.[13] Dagster+ AI, in early access, summarizes failed runs, groups failures into Issues and can dispatch Claude Code to open a fix as a pull request.[11]

In Avalanche, the agent is a step of the workflow. Agent steps declare typed inputs and outputs, get PDF, Word and spreadsheet skills, can run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md), and are checkpointed like any other step.[21, 23, 26, 27] The Avalanche skill drafts the workflow from an outcome you describe in your coding agent.[22]

04

Running it in production

Buyer question

How mature does the managed service need to be today?

Dagster has the edge on maturity. Dagster+ is generally available as Serverless or Hybrid, with branch deployments, deploy history and rollback, Insights, alerts, and on Pro, SSO, SCIM, audit logs and SIEM export.[3, 4, 8, 14, 15] Dagster+ bills a monthly plan fee plus credits, where each asset materialization or op execution is one credit, as of October 27, 2026.[3]

You can self-host the MIT-licensed Avalanche runtime, with its operator, web console, REST API and terminal UI, on your own infrastructure; it is more limited than Avalanche Cloud, which runs the production infrastructure for you.[20, 24] Avalanche Cloud adds SSO and organization roles, versioned deploys, instant rollback of the whole workflow with no rebuild, step checkpoints, managed queueing, owner approvals and one run history for every deployed workflow, and is open through a waitlist.[25, 28]

05 / FAQDirect answers

Common questions.

Is Avalanche an alternative to Dagster?
For different work. Dagster orchestrates data assets such as tables and models for data engineering teams.[1, 2] Avalanche runs agentic business workflows where agents work inside a few steps you set, with checks, approvals and document lineage per step.[25] A team can run both: Dagster for the data platform, Avalanche for document-heavy agent work.
Can Dagster run AI agents?
Dagster can call models from inside an asset, for example through the community dagster-anthropic resource, which also logs API usage to Insights.[12] It has no first-class agent step. Its AI features, the dagster-expert skill, the Dagster+ MCP server and Dagster+ AI in early access, help engineers build and debug pipelines.[11, 13]
Does Dagster support rollback?
Yes, in Dagster+. The Deployment tab keeps a history of every code location deploy, and "Rollback to this version" redeploys a previously deployed image; it needs Organization, Admin or Editor permissions.[8]
Does Avalanche Cloud support rollback?
Yes. Instant rollback: Avalanche Cloud points a workflow back at any previous version in one click or one command, with no rebuild, and running workflows keep the version they started on. Avalanche Cloud is on a waitlist.[25, 28]
Can I see which step changed a document?
Yes, on Avalanche Cloud. Every run gets a versioned workspace, and each step's changes are committed as a new version, so you can see which step produced or changed each file, compare versions and download any step's files.[25] Dagster tracks lineage and versions per asset materialization; its default I/O manager stores step outputs as pickle files.[2, 30]
Is Dagster open source, and how is Dagster+ priced?
Dagster is Apache-2.0 and can be self-hosted.[1] Dagster+ Solo is $10 a month plus $0.040 per credit and Starter is $100 a month plus $0.035 per credit, where a credit is one asset materialization or op execution; Pro is priced by sales, as of October 27, 2026.[3] The Avalanche runtime is MIT-licensed.[20]
06 / SourcesChecked 2026-10-27

Public primary sources.

Every source was fetched on the date shown. Prices and plans change; check the vendor before you buy.

  1. [1]dagster-io/dagster repository and README (Apache-2.0)Dagster Labs on GitHub · Checked https://github.com/dagster-io/dagster
  2. [2]AssetsDagster docs · Checked https://docs.dagster.io/guides/build/assets
  3. [3]Dagster pricing and plan comparisonDagster Labs · Checked https://dagster.io/pricing
  4. [4]Branch deploymentsDagster docs · Checked https://docs.dagster.io/deployment/dagster-plus/deploying-code/branch-deployments
  5. [5]Partitioning assetsDagster docs · Checked https://docs.dagster.io/guides/build/partitions-and-backfills/partitioning-assets
  6. [6]Backfilling dataDagster docs · Checked https://dagster.io/docs/guides/build/partitions-and-backfills/backfilling-data
  7. [7]AutomateDagster docs · Checked https://docs.dagster.io/guides/automate
  8. [8]Dagster+ project (code location) history and rollbacksDagster docs · Checked https://dagster.io/docs/guides/build/projects/dagster-plus-project-history
  9. [9]Testing assets with asset checksDagster docs · Checked https://dagster.io/docs/guides/test/asset-checks
  10. [10]Asset catalog (Dagster+)Dagster docs · Checked https://dagster.io/docs/guides/build/assets/asset-catalog
  11. [11]Dagster+ AI (early access)Dagster docs · Checked https://dagster.io/docs/guides/labs/dagster-ai
  12. [12]Dagster & Anthropic (community integration)Dagster docs · Checked https://dagster.io/docs/integrations/libraries/anthropic
  13. [13]dagster-io/skills: dagster-expert skill and Dagster+ MCP serverDagster Labs on GitHub · Checked https://github.com/dagster-io/skills
  14. [14]About Dagster+Dagster docs · Checked https://dagster.io/docs/deployment/dagster-plus
  15. [15]Viewing and accessing audit logsDagster docs · Checked https://dagster.io/docs/deployment/dagster-plus/authentication-and-access-control/rbac/audit-logs
  16. [16]Insights (Dagster+)Dagster docs · Checked https://dagster.io/docs/guides/observe/insights
  17. [17]SensorsDagster docs · Checked https://dagster.io/docs/guides/automate/sensors
  18. [18]Dagster GraphQL APIDagster docs · Checked https://dagster.io/docs/api/graphql
  19. [19]Configuring run retriesDagster docs · Checked https://dagster.io/docs/deployment/execution/run-retries
  20. [20]Avalanche repository and README (MIT License)Trampoline AI · Checked https://github.com/Trampoline-AI/avalanche
  21. [21]Results, cancellation, and reruns (Avalanche docs)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/build-workflows/results-and-reruns
  22. [22]Build a workflow with the Avalanche skill (Avalanche docs)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/build-workflows/build-with-skill
  23. [23]Skills for agent steps: PDF, DOCX and spreadsheet (Avalanche docs)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/agent-steps/skills
  24. [24]Architecture and current limits (Avalanche docs)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/reference/architecture-and-limits
  25. [25]Avalanche and Avalanche Cloud overviewAvalanche · Checked https://avalanche.run/
  26. [26]Pull request #124: Claude Agent SDK agent stepsTrampoline AI on GitHub · Checked https://github.com/Trampoline-AI/avalanche/pull/124
  27. [27]Pull request #136: Agent Skills (SKILL.md) in agent stepsTrampoline AI on GitHub · Checked https://github.com/Trampoline-AI/avalanche/pull/136
  28. [28]Avalanche Cloud waitlistAvalanche · Checked https://cloud.avalanche.run/waitlist
  29. [29]Integrations libraryDagster docs · Checked https://dagster.io/docs/integrations/libraries
  30. [30]I/O managersDagster docs · Checked https://docs.dagster.io/guides/build/io-managers
  31. [31]Dagster webserver and UIDagster docs · Checked https://dagster.io/docs/guides/operate/webserver
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See the runtime for yourselfOpen source · MIT

Our code speaks for itself.

The Avalanche runtime is open source under the MIT License. Read the code, run the starter project, and see how it fits the work your team repeats.