Temporal vs Argo Workflows: which fits your team's workflows
Choose Temporal when the work is code-defined, long-running business or application logic that needs retries, signals and replay after failures. Choose Argo Workflows when each step is a container job on Kubernetes and the work is batch, ML pipelines or infrastructure automation. Both can run on your own clusters, but they solve different problems, and a platform team and an application team may reasonably end up with one of each. Three factors usually decide it: who writes and owns the logic, whether the unit of work is a container job or application code, and how much Kubernetes operations your team already runs.
Start with ownership. In Temporal, workflows are ordinary functions in Go, Java, TypeScript or Python, so the developers who build the service also own the orchestration logic, its tests and its versioning. In Argo Workflows, a workflow is a Kubernetes custom resource in which each step is a container, so the logic is usually written by platform or data engineers in YAML and the container images they build. Ask who will debug a stuck workflow at 2 a.m. and who will review changes to it. The answer tells you which model your team can sustain.
Next, check the unit of work. If a step is a container image that reads data, trains a model or runs a Terraform plan, Argo's container-per-step model maps directly to that work. If the steps are calls into your application's domain logic, with decisions that depend on earlier results and waits that may last days, Temporal's event history and replay are built for that. Finally, measure your Kubernetes footprint honestly. Temporal self-hosting requires its own Frontend, History, Matching and Worker services plus persistence and visibility stores, and Argo requires a working Kubernetes cluster with the Argo controller. Confirm the deployment options and the operating model for each with the vendors before you size the team.
Which scenario favors Temporal and which favors Argo Workflows
This table maps common workload scenarios to the platform that fits better, based on whether each step is a container run by Argo or a function in a durable Temporal workflow. Read each row for the reason, then the weak-fit note tells you what to confirm before committing.
| Scenario | Better fit | Why, in terms of step model, durability, signals and retries |
|---|---|---|
| ML training pipelines | Argo Workflows | Each step is a container with its own image, GPU request and artifacts in S3 or GCS, which matches how training jobs are packaged. Argo's DAG model and artifact support fit this well. Confirm how checkpoints are written so a failed step resumes from saved state rather than restarting. |
| Nightly batch jobs | Argo Workflows | Cron scheduling, step timeouts and workflow archiving cover scheduled batch runs, and each container can be sized independently. Temporal can run batches too, but it adds a server cluster to operate for work that Kubernetes already schedules. |
| Infrastructure automation | Argo Workflows | Steps that run Terraform, Ansible or kubectl inside containers map directly to Argo's container-per-step model, and conditionals and loops handle drift checks. Confirm that the tools in each step are idempotent, since Argo retries containers rather than replaying code. |
| Order or onboarding business processes | Temporal | Code-defined workflows keep ordered event history, so a process that waits days for a customer or a third-party callback resumes after worker or server restarts. Choose Temporal when business logic, state and compensation steps live in application code owned by developers. |
| Human approval steps | Temporal | Signals let an external event, such as a manager's approval, advance a running workflow without polling. Argo can pause on a suspend step, but routing approvals through a signal with a timer and escalation is a natural fit only for Temporal. |
| Payment-style retries | Temporal | Per-activity retry policies with backoff, plus durable state across failures, suit operations that must not run twice or be lost. Confirm that each external call is idempotent with a stable key, because Temporal guarantees replay of the workflow, not exactly-once effects in outside systems. |
| Long-running multi-day processes with many external waits | Temporal | Workflows hold state in event history rather than in a pod that must stay alive, so waits do not tie up containers. Argo pods consume cluster resources while a step is running, which makes long idle waits a poor fit. |
| Teams without application developers in the loop | Argo Workflows | Workflows are YAML manifests built from container images, so platform and data engineers can own them without writing workflow SDK code. Confirm that your team can maintain container images for every step. |
| Weak fit: high-volume short tasks with heavy per-task Kubernetes overhead | Neither by default | Argo creates a pod per step, so many very short steps can strain the cluster, and Temporal adds Frontend, History, Matching and Worker services plus a database. Benchmark both in your own environment before committing, and check whether a queue-based worker design fits better. |
How Temporal and Argo Workflows are built and what that means for operations
Temporal and Argo Workflows solve different problems, and their architectures reflect that. Temporal is a service cluster you run alongside your application code, while Argo is a set of Kubernetes controllers that inherit the health and policies of the cluster they run in. The cards below cover the components, storage choices, deployment paths and access exposure that shape the operating effort of each.
Temporal Service components
A self-hosted Temporal Service runs Frontend, History, Matching and Worker services, backed by a persistence store and a separate visibility store. Each service scales and fails on its own, so your team owns the health of four service roles plus two data stores. Before committing, confirm how your platform team will monitor each role, size History shards, and handle upgrades across all of them. Each component is a separate failure point that needs an on-call runbook.
Tested persistence backends
Temporal tests persistence on Cassandra, PostgreSQL and MySQL, and SQLite is meant only for development and testing. The choice drives backup, failover and skills requirements for the database team, not just the Temporal team. Check which database engine and version your organization already operates well, and confirm that the version you pick is on Temporal's tested list before you build around it. Do not carry SQLite from a laptop test into a shared environment.
Argo as a Kubernetes CRD
Argo Workflows is implemented as a Kubernetes Custom Resource Definition, and each step runs as a container. Workflow state lives in the cluster as Kubernetes objects, so your platform team manages it with the same tooling as Deployments and Jobs. Check how your cluster handles pod scheduling, resource quotas and image pull policies, because those settings now govern workflow reliability. Argo does not add a separate server cluster to run.
Temporal deployment paths
Self-hosted Temporal can run on Docker Compose, from Go binaries, on Helm charts that place each server service in its own Kubernetes pods, or from a custom Go build that imports the server package. Helm chart versions below 0.73.1 do not work with server images 1.30 and later, so pin chart and server versions together. A development compose file and a production Helm deployment differ in operating effort. Ask your team which path matches the cluster skills you already have.
Network exposure of Temporal
Temporal's deployment guidance says its services should run on hosts that are not reachable from the public internet, with access limited to trusted internal networks. Self-hosted Temporal also ships with a default noopAuthorizer that allows every API request when no Authorizer is configured. Your security team should confirm network segmentation, mTLS, and an Authorizer or ClaimMapper before any workflow handles production data. Treat this as a deployment gate, not a later hardening task.
Argo depends on the cluster
Argo Workflows has no separate control plane to run, but it is only as available as the Kubernetes cluster it runs in. Node pressure, API server health, and cluster upgrades all affect running workflows, and the team that operates the cluster becomes part of the workflow operating model. Ask who owns cluster upgrades and whether workflow pods can be drained safely. Plan for the cluster to be the shared dependency for batch, ML and infrastructure jobs alike.
Capabilities side by side: retries, signals, timeouts, UI and identity
This table compares how Temporal and Argo Workflows handle the runtime and operating questions that decide fit, from retries and long waits to the UI and identity controls. Read each row as a question to answer for your own workload, and treat every item marked to confirm as a check to run before you commit. The main takeaway is that Temporal needs identity controls configured explicitly before production use, while Argo's options depend on your Kubernetes and Argo versions.
| Capability | Temporal | Argo Workflows |
|---|---|---|
| Programming model and step unit | Workflows written as code in Go, Java, TypeScript or Python; code runs on workers | Workflows defined as a Kubernetes CRD; each step is a container in a sequence or DAG |
| State and replay | Complete ordered event history used to replay and resume workflows after failures | Workflow state tracked through Kubernetes resources; confirm recovery behavior for your version |
| Retries and timeouts | Retry and timeout settings live in workflow and activity code; confirm options in the SDK docs | Step and workflow timeouts supported; confirm retry settings for your Argo version |
| Signals and long waits | Signals are part of the programming model; confirm history size and wait limits for your server version | Not covered in the supplied project summary; confirm how suspend, resume and external input work |
| Scheduling and artifacts | Confirm scheduling options for your server version; pass large files through external storage | Cron scheduling supported; artifacts for S3, Artifactory, Azure Blob Storage, GCS, Git and HTTP |
| UI | Web UI with OIDC single sign-on for access, configured on the self-hosted server | Built-in UI; confirm which access controls apply in your installation |
| Client SDKs and API | Go, Java, TypeScript and Python SDKs for building and running workflows | Java, Go, Python (Hera) and TypeScript (Juno) clients, plus REST and gRPC API |
| Identity | mTLS, ClaimMapper, Authorizer plugin and OIDC for the Web UI; default noopAuthorizer allows every request until configured | Confirm the authentication and SSO options for your Kubernetes and Argo version |
Licensing and editions: what is free and what is paid
The Temporal Server is MIT licensed, so you can run, modify and redistribute it without a license fee. Temporal Technologies also sells Temporal Cloud as a managed service, which is a separate paid product rather than a different edition of the server. Its published pay-as-you-go pricing, per the Temporal pricing page, starts at 50 US dollars per million actions, with active storage at 0.042 dollars per GB-hour and retained storage at 0.00105 dollars per GB-hour. The same page lists a 99.9% SLA, or 99.99% with high availability, and SAML SSO.
- Server license: The MIT license covers the Temporal Server itself. It does not cover your infrastructure, persistence stores, or the staff who operate the cluster.
- Usage-based billing: Cloud charges are driven by actions and stored workflow state, so cost moves with how many workflows run and how long their history is kept.
- Self-hosted costs: Cassandra, PostgreSQL or MySQL, plus Elasticsearch or OpenSearch for visibility at scale, are your own infrastructure costs.
- Support tier: The pricing facts we have cover the managed service's SLA and SSO. Ask Temporal directly about any paid support options for self-hosted deployments.
Before comparing totals, confirm current Cloud rates, the storage and action volumes your workload will produce, and which SSO and audit features are included in your plan.
Argo Workflows is licensed under Apache-2.0, a permissive license that allows commercial use, modification and redistribution without a license fee. Argo is a CNCF graduated project, which places it under the Cloud Native Computing Foundation's governance. The project runs as a Kubernetes Custom Resource Definition, so its license cost is zero, but its operating cost sits in the Kubernetes clusters, storage and people that run it.
- License: Apache-2.0 applies to the Argo Workflows source in the argoproj/argo-workflows repository.
- Governance: CNCF graduated status describes the project's maturity and governance model, not a support contract.
- Paid editions: We did not receive facts about any paid edition or commercial support tier for Argo Workflows, so do not assume one exists or does not exist.
- Ecosystem vendors: Some organizations offer commercial support or hosted Argo products. Identify those vendors yourself and check their terms, since they are not covered by the project license.
- Infrastructure: Container registries, artifact storage such as S3 or GCS, and cluster capacity are costs that the license does not include.
Before you budget, ask any commercial provider for written support scope, response commitments and the versions covered, and confirm the current Argo Workflows release line you plan to deploy.
Neither license decides the total cost of ownership. Both platforms are free to download under their licenses, so the cost difference comes from the people, infrastructure and support you need to run them safely in your environment. Temporal adds a stateful cluster and a persistence store, while Argo adds Kubernetes capacity and artifact storage. Both need upgrades, monitoring and backup plans.
- Staff time: Platform engineers, SREs and security staff are the largest recurring cost in self-hosted deployments.
- Persistence and storage: Temporal needs a supported database and a visibility store, while Argo needs artifact storage and archive retention.
- Upgrades: Temporal's upgrade guidance asks you to move one minor version at a time, which is planned work with a test cycle.
- Hosting footprint: Temporal's services run as separate components, and Argo's workflows run as pods on your cluster.
- Support: Decide whether you need vendor support, a retainer or an in-house team, and price that choice separately from the license.
When you request quotes, ask each provider to list what is included, which components are recurring, and what changes if your workload volume grows.
What drives the running cost of Temporal and Argo Workflows
The largest recurring cost of a self-hosted Temporal Service is the cluster itself: Frontend, History, Matching and Worker services, a persistence store (Cassandra, PostgreSQL or MySQL, per the tested versions in the Temporal persistence documentation) and, for anything beyond a few workflow executions, an Elasticsearch or OpenSearch visibility store. Cassandra deployments must add a separate visibility store, so the database choice changes the bill. Argo Workflows has no control plane of its own to host. Its recurring cost is the Kubernetes capacity each step consumes as a container, plus the artifact storage you attach for S3, GCS, Azure Blob or similar. Ask the consultant to size both: Temporal's history and visibility growth against Argo's peak parallel pod count and artifact retention.
Staff skills drive the cost more than most quotes show. Temporal workers are written in Go, Java, TypeScript or Python, so the people who maintain them must understand determinism, replay and versioning of workflow code, not only the infrastructure. Argo Workflows asks for Kubernetes operations skills: RBAC, resource quotas, pod scheduling and the controller itself. Many teams already have that skill set, which lowers the Argo cost; teams without Kubernetes operators will pay more to reach a working platform. The one-time build cost covers workflow or pipeline design, integrations, identity wiring and the first production runbooks. The recurring cost covers on-call, upgrades, backups and developer support, and it scales with the number of teams that depend on the platform.
Upgrades are a recurring line item for both. Temporal Server should move one minor version at a time, to the latest patch of the current minor first, and schema upgrades must run before server upgrades when the release notes call for them. Plan a test environment and a rehearsed rollback for each step. For Argo, plan around Kubernetes cluster upgrades and the Argo release line you run, with testing of existing workflow templates against each new version. Ask a consultant for: a written upgrade runbook, the number of environments covered, backup and restore testing, and whether support or upgrade retainers are included or priced separately. Also ask whether a managed option is in scope. Temporal Cloud publishes pay-as-you-go pricing on its pricing page, and that route trades infrastructure and upgrade work for a usage-based fee; confirm current terms directly with Temporal.
Moving workflows from Argo Workflows to Temporal or the reverse
This sequence moves each workflow onto the platform that fits its shape, keeps the old and new systems running side by side until results match, and ends with a documented cutover and retirement of the old platform.
Inventory every workflow and its dependencies
List each Argo Workflow template, CronWorkflow and any Temporal workflow type in scope, along with its schedule, triggers, inputs, outputs, secrets, and the systems it calls. The owning team for each item should confirm the list. Classify each one as either a container job, where each step is a container that runs and exits, or application logic, where the code makes decisions over time, waits for human or external input, and must survive restarts. Mixed workflows should be flagged for splitting. The output is a signed-off inventory with one classification per item.
Decide what state and artifacts carry over
Argo keeps artifacts in object stores such as S3, GCS, Azure Blob Storage, Artifactory or Git, so those objects can usually stay where they are and be referenced by the new workflow. Temporal keeps a complete ordered event history for each execution, and that history is not portable to or from Argo, so in-flight executions cannot be transferred. Decide whether running work will drain on the old platform or be restarted, and document which workflow outputs the downstream systems still need. Confirm with the platform owner how long Argo archived workflows and Temporal histories must be retained, and where those records will live after retirement.
Rebuild each workflow on its target platform
Rewrite container-oriented pipelines as Argo steps or as Temporal activities that call containers, depending on which platform the inventory selected. Rewrite application logic as Temporal workflow code, keeping workflow code deterministic and moving all I/O, randomness and clock reads into activities. Where a Temporal workflow will replace an Argo DAG, replicate retry policies and timeouts explicitly, since the two platforms express them differently. The output is a tested build for each workflow with its configuration recorded in version control.
Run both platforms in parallel for a defined window
Schedule the new implementation alongside the existing one, and compare outputs, artifacts, alerts and failure handling for the same inputs. Use a window agreed in writing by the workflow owners and the operations team, since no default duration is established for this step. Record each mismatch, classify it as a logic defect or an environment difference, and fix it before the next comparison. The window closes when the agreed comparison criteria are met and the open defects list is empty or accepted in writing.
Confirm observability, access and audit controls
Before any cutover, verify that the new platform emits the logs, metrics and alerts that the on-call team depends on. For Temporal, confirm the authorization configuration, since a self-hosted service with no configured Authorizer allows every API request, and confirm how namespace roles, mTLS and OIDC single sign-on will be set. For Argo, confirm the equivalent access controls on the Kubernetes API and the UI. Ask the compliance lead which audit records the rules require, and verify that both platforms can produce them for the migrated workflows.
Cut over scheduling and triggers
Move schedules, webhooks and event triggers one group at a time, starting with the lowest-impact workflows. For each group, disable the old schedule, enable the new one, and confirm the first runs complete with the expected outputs. Keep a rollback path that re-enables the old schedule until the owners sign off. The output is a change record per group showing the trigger moved, the first successful run, and the owner's approval.
Retire the old platform and keep its records
After every workflow has run successfully on the new platform through its agreed checks, stop new submissions to the old platform and export the records that must be kept, such as archived workflow results, artifact manifests and execution histories where the platform allows export. Remove the old controllers, service accounts and secrets, and update the runbooks and architecture documents. Confirm that the retained records meet the retention and audit requirements agreed in step two, and file the decommissioning record with the owners.
Where Temporal and Argo Workflows implementation projects usually run long
Implementation effort on both platforms sits in different places. For Temporal, projects usually slip on workflow determinism and versioning. Workflow code is replayed from its recorded event history, so a change to workflow logic can break executions already in flight unless it is versioned deliberately. Teams should decide early how workflow versions are rolled out, how old histories are drained, and who reviews changes for determinism before they ship. Persistence and visibility design is the second common delay. The persistence store must be chosen from the tested options (Cassandra, PostgreSQL or MySQL), and advanced visibility needs its own store, so the database decision affects the search and operations model later. Confirm the persistence and visibility versions against Temporal's current documentation before sizing anything.
Security configuration is the third area. The default noopAuthorizer allows every API request with no authentication or access control, so the work of enabling mTLS, a ClaimMapper for namespace roles, an Authorizer, OIDC single sign-on for the Web UI and payload encryption through a codec server is mandatory, not optional. Worker deployment is the fourth: workers run the application code, so their scaling, secret handling and upgrade order need agreement with the application teams. For Argo Workflows, the effort is less about the engine and more about the Kubernetes estate around it. Cluster capacity planning decides whether parallel container steps can run without starving other workloads. Artifact repository setup for S3, GCS, Azure Blob, Git or HTTP needs credentials, retention and access rules settled before the first pipeline runs. RBAC and template governance decide who may create workflows and who may publish shared WorkflowTemplates. Projects slip most often when these owners are not named early.
DeployOpen scopes this work in writing before it starts, so the security configuration, persistence choices, cluster and artifact decisions and template ownership are agreed as deliverables rather than discovered during build. Ask any implementation partner to list these items explicitly, state which are dependencies on your own platform or identity teams, and confirm which upgrade and version checks they will run against the release notes of the platform you choose. Illustrative example: a team that has not assigned a determinism reviewer or an artifact store owner before build will often find those gaps during the first production change.
Questions buyers ask before choosing or hiring help
How much effort does it take to run Temporal compared with Argo Workflows?
Temporal needs a dedicated server cluster, while Argo Workflows runs inside a Kubernetes cluster you already operate. A self-hosted Temporal Service consists of Frontend, History, Matching and Worker services plus a persistence store and a visibility store, and the deployment docs say these services should run on hosts not reachable from the public internet. Effort rises with the persistence choice (Cassandra, PostgreSQL or MySQL), the visibility store (Elasticsearch or OpenSearch is recommended beyond a few workflow executions), and the upgrade discipline the Temporal upgrade docs describe, which requires moving one minor version at a time. Argo's effort centers on controller configuration, artifact storage and cluster capacity for containerized steps. Ask any vendor or partner to size both stacks against your own workload volume in writing.
Can our platform team operate Temporal without an outside consultant?
Yes, if your team already runs stateful databases, Kubernetes or Docker Compose, and can own upgrades and security settings. Temporal's Helm charts run the server services in separate pods, and the self-hosted security docs show that the default noopAuthorizer allows every API request until you configure an Authorizer, so authentication and namespace roles must be set deliberately before production use. Teams often also need to build workers in Go, Java, TypeScript or Python and maintain their own determinism rules. A consultant is most useful for the initial architecture, hardening and runbooks, not for daily operation. Check whether your team has at least one engineer who can own the workflow code, not just the cluster.
What does a consultant need from us before scoping an implementation?
A consultant needs a written picture of the workflows, the systems they touch, the data they carry and the controls your organization must evidence. For a Temporal project, expect questions about the number of namespaces, expected workflow volume, persistence and visibility choices, identity provider for OIDC single sign-on, mTLS requirements, and payload encryption through custom codecs. For an Argo project, expect questions about cluster topology, artifact stores such as S3 or GCS, cron schedules, and how workflow archives are retained. Fixed-scope engagements at DeployOpen are agreed in writing before work starts, so prepare an inventory of workloads, existing tooling and acceptance criteria. Deliverables and timelines appear in that written scope, not in this page.
How long does an implementation take for each platform?
No supplied fact gives a duration for either platform, so treat any number you hear as unverified until it appears in a written scope. Duration depends on how many workflow types exist, whether they need signals, retries and long timers, how many persistence and visibility stores you must stand up, how many identity integrations are needed, and whether your compliance team needs evidence collected during the build. Argo implementations tend to be shorter when the work is containerized batch or CI steps that already run in Kubernetes, but that depends on your pipelines. Ask for a phased plan with milestones, named dependencies and the testing you expect to see before go-live.
Is Temporal Cloud worth considering instead of self-hosting?
It can be, if managing the Temporal Service and its stores is the main cost you want to avoid, and your data rules allow a managed service. Temporal Cloud pay-as-you-go pricing starts at 50 US dollars per million actions, with active storage at 0.042 dollars per GB-hour and retained storage at 0.00105 dollars per GB-hour, according to the Temporal pricing page. It also lists a 99.9% SLA, or 99.99% with high availability, and SAML SSO. Your total cost depends on action volume and storage retention, so model both against your expected workload. Self-hosting shifts that spend to your engineers and infrastructure, and it keeps data in your environment. Confirm the regions and data residency terms directly with Temporal.
Can we run Argo Workflows and Temporal together in one platform?
Yes, many teams split them by job type, with Argo running containerized batch, ML and infrastructure pipelines and Temporal running long-lived business processes that need signals, retries and an event history. Argo's steps are containers defined as a Kubernetes Custom Resource Definition, while Temporal workflows are code in general-purpose languages with a replayable history. The main integration risk is ownership: two engines means two upgrade cycles, two sets of logs and two places where a failed job can hide. Define which system is the source of truth for each process, how results pass between them, and who is paged when either fails. Confirm the boundary in the written scope before work begins.
What does Argo Workflows cost to license?
Argo Workflows is licensed under Apache-2.0 and is a CNCF graduated project, so the software itself carries no license fee. Your costs are the Kubernetes clusters, compute, artifact storage such as S3 or GCS, and the people who build and run pipelines. Any paid support or implementation work is a separate line item, quoted from a written scope. The same logic applies to Temporal's server, which is MIT licensed; the recurring cost decision there is between self-hosted infrastructure and Temporal Cloud usage fees. Ask any provider to separate software, infrastructure, support and implementation costs in the quote, and to state which of them recur each year.
How DeployOpen helps choose, implement or migrate
DeployOpen helps platform and application teams decide whether a durable application workflow engine or a Kubernetes container pipeline engine fits the work, then plans, builds and hands over the chosen platform inside your own infrastructure. Engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts.
How we scope
- Map which processes need long-running code with retries and signals, and which are container steps in a pipeline
- Document the target cluster, cloud accounts or air-gapped network where the platform will run
- Agree in writing which persistence, identity and secrets integrations are in scope before work begins
- Confirm licensing, support windows and upgrade ownership with each vendor before the scope is fixed
How we build
- Deploy the chosen engine with the Kubernetes, Helm or Docker method your team standardizes on
- Configure authentication, authorization and network restrictions so services are not exposed publicly
- Build sample workflows that exercise retries, timeouts and artifact handling for your real use cases
- Test failure recovery, upgrade paths and backup and restore procedures against the agreed scope
How we hand over
- Deliver written architecture, configuration and runbook documentation for the platform as built
- Provide upgrade procedures that follow the vendor's version order and schema migration steps
- Walk your engineers through writing, deploying and troubleshooting workflows on the platform
- Hand over evidence of controls and configuration that your compliance team can review
How we support
- Offer ongoing support, upgrades and continuous testing as a retainer after handover
- Plan vendor version upgrades, patch releases and schema changes as recurring work
- Repeat security testing of the workflow platform and its integrations on a retainer schedule
- Agree response expectations and scope boundaries in the retainer before it starts
