Cloud Run runs your containers. Avalanche runs the work your agents do.

Quick answer

Updated October 27, 2026. Google Cloud Run is general serverless compute: it runs containers as services that scale with requests and as jobs that run tasks to completion, with GPUs, IAM, audit logs and a 99.95% uptime objective for services.[3, 5, 18, 31, 35] Multi-step work on Cloud Run is usually assembled from Google Cloud Workflows, Cloud Scheduler, Cloud Tasks and Eventarc around your own containers.[9, 12, 13, 14] Choose Cloud Run if developers need scalable, generally available compute inside Google Cloud. Choose Avalanche if the job is a long, multi-step, document-heavy business workflow: you set the few steps, checks and approvals that matter, agents do the work inside each step, and every run keeps typed step outputs and versioned files your team can compare across runs and versions.[21, 23, 25]

Key takeaway

Choose Avalanche when agents should do the work inside a few business steps you control, often over PDFs, Word files and spreadsheets.

Choose Google Cloud Run when developers need scalable, generally available containers, jobs and GPUs inside Google Cloud, under its IAM and billing controls.

  • Avalanche wins on level of abstraction: you set the steps, checks and approvals and an agent does the work inside each step; on Cloud Run you build each container and wire the steps together with other Google Cloud services.
  • Avalanche wins on rolling back a whole workflow at once: Cloud Run rolls back one service at a time, and has no documented rollback for jobs or Workflows definitions; Avalanche Cloud puts every step of a workflow back in one action, with no rebuild, while running workflows keep their version.
  • Google Cloud Run wins on scale, GPUs and availability: Cloud Run is generally available with fast autoscaling, up to 10,000 parallel tasks per job, NVIDIA GPUs and an uptime objective; Avalanche Cloud is on a waitlist.
  • Google Cloud Run wins on identity and access: Google Cloud IAM, Cloud Audit Logs, VPC Service Controls and Cloud Billing budgets go further than Avalanche Cloud today, which has no spend limits yet.

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

01 / The core differenceAvalanche vs Google Cloud Run

Compute you assemble workflows on, or the workflow layer for agentic work.

Google Cloud Run runs the containers you build, as request-serving services or as jobs that run and exit, and teams add Workflows, Cloud Scheduler, Cloud Tasks or Eventarc to chain them.[3, 9] Avalanche works a level higher: you describe the outcome and the few steps, checks and approvals that matter, and agents do the work in between, with each step checkpointed.[21, 25]

Cloud Run is infrastructure for developers: it runs, scales and bills the containers you build, and Google documents pairing it with Workflows, Cloud Scheduler, Cloud Tasks and Eventarc for multi-step and triggered work.[3, 9, 38] Avalanche is built for the work agents do for a business: you define the few steps, checks and approvals that matter, agents do the work in between, and every run keeps its version, its typed step outputs and the files each step changed.[21, 23, 25] Both let you move back to an earlier version without a rebuild; the difference is the unit: a Cloud Run service revision, or an Avalanche workflow version that running workflows stay on.[1, 25]

02 / At a glanceChecked 2026-10-27

Avalanche and Google Cloud Run, side by side.

Avalanche and Google Cloud Run compared, checked 2026-10-27
CriterionAvalancheGoogle Cloud RunEdge
Level of abstractionCoarse business steps: you set the steps, checks and approvals, and an agent does the work inside each step.[20, 25]You build each step as a container (a service or a job) and chain them with Workflows, Cloud Tasks, Cloud Scheduler or Eventarc.[3, 9, 38]Avalanche edge
Agents and skills inside stepsAgent steps can run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md); the Avalanche skill drafts a full workflow from a described outcome.[22, 26, 27]Agents are code you package in a container; Cloud Run instances (Preview) target long-lived agents. No agent step contract or skill loader is documented.[3]Avalanche edge
Step checkpointsCompleted steps are recorded durably and never re-run after a crash; failed steps retry, and uncertain outcomes are held instead of blindly retried.[21, 25]Cloud Run keeps no multi-step state: a failed job task restarts, up to 10 retries. Workflows holds state and retries calls with backoff.[6, 9]Avalanche edge
Document 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; agent steps get PDF, Word and spreadsheet skills.[23, 25]Mount Cloud Storage, NFS or in-memory volumes; Workflows variables and arguments share 512 KB. Per-step file versions across a run are not documented.[11, 16, 17]Avalanche 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]Job executions with history, logs and metrics tabs; Cloud Logging and built-in Cloud Monitoring metrics; Workflows keeps execution history for 90 days.[8, 11, 29, 30]Different jobs
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; schedules follow; an audit trail of who promoted or rolled back.[25]One service at a time: each service deploy creates an immutable revision, and you send its traffic back to a previous revision, no rebuild. A workflow spread over several services, jobs and a Workflows definition is rolled back piece by piece; jobs and Workflows have no documented rollback action.[1, 2]Avalanche edge
Scale, GPUs and availabilitySelf-hostable MIT-licensed runtime with operator, web console, REST API and terminal UI, more limited than Cloud; Avalanche Cloud is on a waitlist, with FIFO queueing and organization capacity limits.[20, 24, 25, 28]Generally available; services scale out to 1,000 instances or more by quota; jobs run up to 10,000 parallel tasks; NVIDIA GPUs; 99.95% uptime objective for services.[3, 5, 18, 35]Google Cloud Run 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]Cloud Scheduler runs jobs on a schedule; services host webhook targets on HTTPS endpoints; Eventarc and Cloud Tasks deliver events and tasks; gcloud CLI and REST API.[8, 12, 13, 14, 15]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]IAM roles per service and job; Cloud Audit Logs for admin activity and data access; VPC Service Controls perimeters; Cloud Billing budgets with alerts and, where supported, spend caps.[31, 32, 33, 34]Google Cloud Run edge
03 / Where each one winsOne claim per card

Where each one wins

Where Avalanche wins

  • Higher-level workflows

    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]

  • 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]

  • Every step versions your documents

    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]

  • Step checkpoints built in

    Completed steps are recorded durably and never re-run after a crash; failed steps retry, and uncertain outcomes are held instead of blindly retried. You do not assemble a separate orchestrator for it.[21, 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]

  • 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]

Where Google Cloud Run wins

  • Scale on demand

    A Cloud Run service can scale out to 1,000 instances, or more with a quota increase, and a job can split work into up to 10,000 independent tasks that run in parallel.[3, 5]

  • GPUs without drivers

    Services and jobs can attach NVIDIA L4 GPUs with 24 GB or RTX PRO 6000 Blackwell GPUs with 96 GB, fully managed, with no extra drivers or libraries.[18, 19]

  • Generally available with an uptime objective

    Cloud Run services carry a 99.95% monthly uptime objective in most regions, and billing is per use, rounded up to the nearest 100 milliseconds.[35, 36]

  • Google Cloud identity and access

    IAM roles per service and job, Cloud Audit Logs for admin and data access, VPC Service Controls perimeters against data exfiltration, and Cloud Billing budgets with alerts.[31, 32, 33, 34]

  • Built-in metrics and logs

    Cloud Monitoring captures metrics with no setup, including running and completed job executions and task attempts, and every job execution has history, logs and metrics tabs.[8, 29, 30]

  • Any language, any container

    You deploy a container image or source code, so any language, library or binary that runs in a container runs on Cloud Run.[3]

04 / Detailed comparison4 buyer questions

What changes for your team.

01

Compute you assemble, or a workflow layer?

Buyer question

Do you need somewhere to run containers, or a way to run a business process your team repeats every week?

Cloud Run runs your code as services that answer requests, jobs that run tasks and exit, and worker pools for pull-based work.[3] A service request can run up to 60 minutes; a job task up to 168 hours, or 1 hour with a GPU, and each task retries from the start on failure.[4, 6, 7] For work with several steps, Google documents pairing it with Workflows, which can hold state, retry, poll or wait for up to a year, plus Cloud Scheduler, Cloud Tasks and Eventarc for triggers.[9, 12, 13, 14, 38]

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]

02

Rollback: one service at a time, or the whole workflow at once

Buyer question

When a release goes wrong in production, how fast can one operator put the previous version back?

On Cloud Run, every deploy or configuration change to a service creates an immutable revision, and up to 1,000 are kept per service.[2] To roll back, you send all traffic to a previous revision from the Revision history tab in the console, with one gcloud run services update-traffic command, or in YAML; nothing is rebuilt. The same controls split traffic for gradual rollouts.[1] That is a fast rollback for one service. A multi-step workflow on Google Cloud is usually several services and jobs tied together by a Workflows definition, and each is rolled back on its own. For jobs and for Workflows definitions, a set-live action for an earlier version is not documented; updating a Workflows definition does not affect executions already in progress.[10]

Avalanche Cloud rolls back the whole workflow, not one container: every step goes back together. A deploy is a frozen, checksum-verified version mapped to a git commit; running workflows keep the code and bundled dependencies they started with, and rolling back from the CLI or console, with no rebuild, makes a previous version live for new runs, with schedules following. An audit trail records who rolled back what and when.[25] Avalanche Cloud is on a waitlist.[28]

03

Documents, agents and run history

Buyer question

How much do you want to build around the agent yourself?

On Cloud Run, files come from volumes you mount: Cloud Storage buckets, NFS shares such as Filestore, or in-memory volumes.[16, 17] Workflows passes data between steps as variables and arguments, capped at 512 KB together, so larger documents live in storage you manage.[11] Agents, parsing and checks are code you package in your containers. Job executions keep history, logs and metrics, and Cloud Monitoring records executions and task attempts without setup.[8, 29, 30]

In Avalanche, files move between steps, and on Avalanche Cloud each step's file changes are committed as a new version of the run's workspace. Agent steps declare typed inputs and outputs and get PDF, Word and spreadsheet skills.[21, 23] Each step is a business result, so Avalanche Cloud lets a team compare that step across hundreds of runs and see which outputs drift or which version changed them.[25] The Avalanche skill drafts the workflow from an outcome you describe in your coding agent, and agent steps can run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md).[22, 26, 27]

04

Running it in production

Buyer question

How mature does the platform need to be today, and who controls access and spend?

Cloud Run has the edge on scale, availability and access controls. It is generally available with a 99.95% monthly uptime objective for services in most regions, fast autoscaling and NVIDIA GPUs.[3, 18, 35] Access runs through Google Cloud IAM roles, Cloud Audit Logs record admin activity and data access, VPC Service Controls guard against data exfiltration, and Cloud Billing budgets send alerts; Google notes that alerts-only budgets do not cap spending.[31, 32, 33, 34]

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, organization roles and per-action permissions, versioned deploys, instant rollback of the whole workflow with no rebuild, step checkpoints, managed queueing and one run history for every deployed workflow. Agents see only what each step passes them: its inputs, its files and the secrets it declares. Spend limits are not available yet. Avalanche Cloud is open through a waitlist.[25, 28]

05 / FAQDirect answers

Common questions.

Is Avalanche an alternative to Google Cloud Run?
For agentic business workflows, yes; for general compute, no. Cloud Run runs any container as a service or job and scales it for you.[3] Avalanche runs workflows where agents work inside a few steps you set, with step checkpoints, comparable runs and document lineage on Avalanche Cloud.[21, 25]
Does Cloud Run support rollback?
Yes, one service at a time. Each deploy creates an immutable revision, and you can send all traffic back to a previous revision from the console, with gcloud run services update-traffic SERVICE --to-revisions REVISION=100, or in YAML, without rebuilding.[1, 2] A rollback action for Cloud Run jobs or Workflows definitions is not documented, so a workflow built from several of them is rolled back piece by piece. Avalanche Cloud rolls back every step of a workflow at once, in one click or one command, with no rebuild, while running workflows keep the version they started on.[25]
How do I run a multi-step workflow on Cloud Run?
Google's documented pattern is to run each step as a Cloud Run job or service and orchestrate them with Workflows, triggered by Cloud Scheduler, Eventarc or Cloud Tasks.[9, 12, 13, 14, 38] Workflows can hold state, retry, poll or wait for up to a year, and keeps execution history for 90 days.[9, 11]
How long can a Cloud Run job run?
Each task runs for 10 minutes by default and up to 168 hours (7 days), or 1 hour with a GPU; the timeout applies to each retry attempt, and there is no timeout on the job execution as a whole.[4] Tasks retry up to 10 times, 3 by default.[6]
How is Cloud Run billed?
By resources used: vCPU-seconds, GiB-seconds of memory and, for request-based services, requests, rounded up to the nearest 100 milliseconds, after a monthly free tier. Request-based services include 2 million requests a month free, then $0.40 per million in us-central1. Workflows bills per step, with 5,000 internal steps free each month, then $0.01 per 1,000. Prices as of October 27, 2026.[36, 37]
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] On Cloud Run, files live in volumes you mount, such as Cloud Storage or NFS; per-step versions of those files across a run are not documented.[16, 17]
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]Rollbacks, gradual rollouts, and traffic migration (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/rollouts-rollbacks-traffic-migration
  2. [2]Manage revisions (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/managing/revisions
  3. [3]What is Cloud Run (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/overview/what-is-cloud-run
  4. [4]Set task timeout for jobs (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/configuring/task-timeout
  5. [5]Create jobs (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/create-jobs
  6. [6]Set maximum retries for jobs (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/configuring/max-retries
  7. [7]Configure request timeout for services (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/configuring/request-timeout
  8. [8]Manage jobs (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/managing/jobs
  9. [9]Workflows overview (Workflows docs)Google Cloud · Checked https://cloud.google.com/workflows/docs/overview
  10. [10]Create, update, and delete a workflow (Workflows docs)Google Cloud · Checked https://cloud.google.com/workflows/docs/creating-updating-workflow
  11. [11]Workflows quotas and limitsGoogle Cloud · Checked https://cloud.google.com/workflows/quotas
  12. [12]Execute jobs on a schedule (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/execute/jobs-on-schedule
  13. [13]Executing asynchronous tasks with Cloud Tasks (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/triggering/using-tasks
  14. [14]Trigger services with events (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/triggering/trigger-with-events
  15. [15]Hosting webhook targets (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/triggering/webhooks
  16. [16]Cloud Storage volume mounts for services (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/configuring/services/cloud-storage-volume-mounts
  17. [17]NFS volume mounts for services (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/configuring/services/nfs-volume-mounts
  18. [18]GPU support for services (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/configuring/services/gpu
  19. [19]Configure GPUs for jobs (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/configuring/jobs/gpu
  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]Monitor health and performance (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/monitoring
  30. [30]Logging and viewing logs (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/logging
  31. [31]Access control with IAM (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/securing/managing-access
  32. [32]Cloud Run audit loggingGoogle Cloud · Checked https://cloud.google.com/run/docs/audit-logging
  33. [33]Using VPC Service Controls (Cloud Run docs)Google Cloud · Checked https://cloud.google.com/run/docs/securing/using-vpc-service-controls
  34. [34]Create, edit, or delete budgets and budget alerts (Cloud Billing docs)Google Cloud · Checked https://cloud.google.com/billing/docs/how-to/budgets
  35. [35]Cloud Run Service Level AgreementGoogle Cloud · Checked https://cloud.google.com/run/sla
  36. [36]Cloud Run pricingGoogle Cloud · Checked https://cloud.google.com/run/pricing
  37. [37]Workflows pricingGoogle Cloud · Checked https://cloud.google.com/workflows/pricing
  38. [38]Execute a Cloud Run job using Workflows (tutorial)Google Cloud · Checked https://cloud.google.com/workflows/docs/tutorials/execute-cloud-run-jobs
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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.