Temporal vs Kestra: when each one fits your workflows
Choose Temporal when the workflow is application logic that must survive crashes, deploys and dependency outages while running for hours, days or longer, and when developers own that logic in Go, Java, TypeScript or Python. Temporal records a complete ordered event history and replays it to resume a workflow, so the code itself carries the business rules. Choose Kestra when the work is scheduled or event-driven data, AI or infrastructure pipelines assembled from its plugins, and when a data or platform team wants to define them as declarative YAML flows that can also be edited in a no-code editor. The choice is less about which engine is more capable and more about who will write, review and change the workflows after go-live.
Three questions settle most of the decision. First, who writes and maintains the workflows: software engineers who want durable functions under version control, or operators who need to add pipeline steps without redeploying application code. Second, how much of the security model you must pay for. Kestra's open-source edition has basic authentication, manual secret and access management and no audit logging, so SSO, RBAC, external secrets managers and audit logs belong in your costing if you need them. Temporal's self-hosted server ships with a default authorizer that allows every API request, so mTLS, a ClaimMapper and an Authorizer must be configured by your team before production. Third, whether a multi-service cluster is acceptable. A self-hosted Temporal Service runs Frontend, History, Matching and Worker services on a database, and Kestra's default deployment is a single server, with high availability requiring its Enterprise Edition.
Before you shortlist either platform, confirm the licensing and edition details in your own environment. Check the current Kestra edition comparison against the features you must have, and review the Temporal self-hosted security documentation with your security team. Then ask each vendor which controls they expect you to operate yourself and which are included.
Which Temporal vs Kestra scenario points to which platform
This table maps common scenarios to the platform that usually fits better, based on execution model, who authors the work and the operational footprint. Read each row as a starting hypothesis, since the right answer depends on your team and environment, and confirm fit with a proof of concept before committing.
| Scenario | Better fit and reason | Where the other platform fits or the choice is wrong |
|---|---|---|
| Order processing with compensation (illustrative) | Temporal. Business logic lives in application code with explicit compensation steps, and the event history lets a workflow resume exactly where it stopped after a failure. | Kestra can orchestrate the calls, but compensation logic is usually easier to test and version in a general-purpose language owned by application developers. |
| Nightly ETL with dependencies across databases and cloud storage | Kestra. Declarative YAML flows, scheduled triggers and a large plugin catalog let data teams model dependencies without writing a service. | Temporal is a poor choice if the pipeline team wants prebuilt connectors and schedule-first operations rather than writing and deploying worker code. |
| AI agent tool calls inside an application | Temporal for agent loops that need durable state, retries and signals inside application code. Kestra fits when the agent step is one task in a data or infrastructure flow. | Confirm whether your agent needs human-in-the-loop signals or long waits in code. Either platform may need custom work for your agent framework, so test it first. |
| Infrastructure runbooks run on a schedule or on an alert | Kestra. Event-driven triggers and script tasks in any language fit operations teams that author runbooks as flows and want them visible to non-developers. | Temporal is the wrong choice for one-off ops scripts with little branching, since it adds a server cluster and worker code to maintain. |
| Human approval step in a multi-day business process | Temporal. Workflows can wait durably for a signal, and the replayable history keeps state across restarts and deployments. | Kestra can pause flows for approval, but confirm its waiting, timeout and audit behavior against your control requirements, since audit logs need Enterprise Edition. |
| Team mix: developers own the code, data engineers own the schedules | Split by workload. Temporal for application logic maintained by developers, Kestra for data and infrastructure flows maintained by analysts and operators. | Running both platforms adds two operational footprints. Only do this if each workload clearly belongs to a different owner. |
| Self-hosting with a small platform team and no HA requirement | Kestra open-source is fully functional for production with a single-server default, which keeps the initial footprint smaller than a Temporal cluster. | Confirm whether your availability target needs the high availability architecture, which is part of Kestra Enterprise Edition. |
| Multi-tenant access control, SSO and audit logs required on day one | Kestra Enterprise Edition covers SSO, RBAC, audit logs and multi-tenancy per its edition comparison. Temporal needs its own authorizer, ClaimMapper and OIDC setup, which you must design and test. | Both need configuration work. Kestra's open-source edition has basic authentication only and no audit logging, so plan for the paid edition if these are mandatory. |
How Temporal and Kestra are built and what each means for operations
Temporal splits its server into several services backed by separate stores, while Kestra's open-source edition starts as a single server and reaches high availability only in its Enterprise Edition. The cards below set out what each design asks of the team that runs it.
Temporal's four server services
A self-hosted Temporal Service runs Frontend, History, Matching and Worker services, each of which can scale and fail on its own. Your platform team must monitor all four, plus the persistence and visibility stores they depend on. Ask who on your team owns that cluster, and whether they have run a stateful multi-service system before.
Persistence and visibility stores
Temporal persists workflow history to Cassandra, PostgreSQL or MySQL, and tested versions are listed in the persistence documentation. Visibility queries need a separate store, and Elasticsearch or OpenSearch is recommended once executions grow beyond a few. Plan for two data stores to back up, upgrade and size, not one.
Four deployment paths for Temporal
Temporal can be deployed with Docker Compose, Go binaries, Helm charts that run each server service in its own Kubernetes pod, or a custom Go build that imports the server package. Docker Compose suits evaluation, while Helm suits production clusters your team already operates. Confirm the chart version before you install, because Helm chart versions below 0.73.1 do not work with server images 1.30 and later.
Network exposure for Temporal
Temporal's deployment guidance says its services should run on hosts that the public internet cannot reach, with access limited to trusted internal networks. If no Authorizer is configured, the default noopAuthorizer allows every API request without authentication. Treat network placement and authorization as day-one work, and confirm both in your security review.
Kestra's single-server open-source default
Kestra's open-source edition is fully functional for production, but its default deployment is a single server with basic authentication, manual secret handling and no audit logging. A small team can run it quickly on one host. Before you go live, decide how you will back up the server, rotate secrets and retain logs, since the edition does not do these for you.
Kestra Enterprise high availability
Kestra Enterprise adds a high availability architecture with Kafka plus Elasticsearch, Redis, AMQP or GCP Pub/Sub queue backends, along with worker groups and task runners for Kubernetes and cloud batch services. Running that stack is a separate platform project with its own components to operate. If uptime for scheduled jobs matters, price the Enterprise license and the queue infrastructure together.
Temporal vs Kestra on features, persistence and access control
This table compares how each platform handles authoring, recovery, triggers, persistence, visibility, transport encryption, access control and high availability. Read the open-source column for each platform first, then check the paid or self-hosted conditions, because Kestra's identity and high availability features sit in its Enterprise Edition. Confirm any cell marked for vendor confirmation before you commit.
| Capability | Temporal (self-hosted, MIT) | Kestra (open source, Apache 2.0) |
|---|---|---|
| Workflow authoring model | Workflows written as code in Go, Java, TypeScript or Python, with a complete ordered event history | Declarative YAML flows, editable in a YAML editor, a no-code editor and an AI Copilot that stay in sync |
| Failure recovery and replay | Replays the event history to resume workflows after failures; code must stay deterministic | Task-level retries and flow execution state; confirm recovery semantics for your flow types with the vendor |
| Scheduled and event-driven triggers | Triggers come from your application code, schedules and signals rather than a built-in flow trigger catalog | Scheduled and event-driven triggers are built into flow definitions |
| Git integration | Not a built-in feature; workflow code is managed through your own repository and release process | Git version control integration is listed in the README; confirm the sync workflow with the vendor |
| Plugin library | No plugin catalog; integrations are written in the SDK language you choose | Hundreds of built-in plugins for databases, cloud storage and APIs, plus scripts in any language |
| AI and MCP support | Not a stated built-in feature; AI workloads run as ordinary workflow code | AI agents and RAG workflows are task types, and a built-in MCP server exposes flows to AI agents |
| Persistence options | Cassandra, PostgreSQL and MySQL are tested; SQLite is for development only | Confirm the supported metadata and queue backends for your version with the vendor |
| Visibility options | Elasticsearch or OpenSearch recommended beyond a few workflow executions; MySQL, PostgreSQL and SQLite also supported | Confirm execution search and retention options with the vendor |
| mTLS and internode encryption | Supported, with separate settings for internode and frontend traffic | Confirm transport encryption options for each component with the vendor |
| Authentication and authorization (open source) | Default noopAuthorizer allows every API request unless an Authorizer is configured; ClaimMapper maps JWT or certificate claims to namespace roles | Basic authentication only, with manual secret and access management |
| SSO and SCIM | OIDC single sign-on for the Web UI; SCIM not a stated feature | SSO and SCIM are Enterprise Edition features |
| Audit logs | Confirm audit logging options for your deployment with the vendor | Audit logs are an Enterprise Edition feature |
| Secrets managers | Payload encryption through custom codecs and a codec server; external secrets managers are not a stated built-in feature | External secrets managers such as HashiCorp Vault and Azure Key Vault are Enterprise Edition features |
| High availability | Services run as separate Frontend, History, Matching and Worker components, deployable on Kubernetes with Helm | High availability architecture with Kafka plus Elasticsearch, Redis, AMQP or GCP Pub/Sub backends is an Enterprise Edition feature |
What Temporal and Kestra license terms and paid editions cover
The Temporal server is licensed under the MIT license, according to its GitHub repository. Your team can run, modify and embed the server in its own infrastructure without a license fee for the server code. The license covers the server source only. Your effort to operate the cluster, the persistence stores it depends on and any support contract you buy are separate decisions.
- Permissive terms: MIT allows internal use and modification. Legal teams should still review the license with counsel before they ship a modified build.
- Current lines: v1.32.1 was published on October 8, 2026, and patch releases v1.31.4 and v1.30.8 followed on October 12, 2026 for earlier minor lines. Pin the minor line your team has tested.
- Upgrade discipline: Backward compatibility is guaranteed only between successive minor versions, so plan upgrades one minor version at a time.
- Paid features: The supplied facts describe no paid edition of the self-hosted server. Ask Temporal which security or support features, if any, sit outside the MIT server.
Check the license file and the release notes for your target version, and get in writing what support Temporal offers self-hosted customers.
Temporal Cloud is the managed service that Temporal Technologies sells alongside the open-source server. Its pay-as-you-go pricing is metered, so cost follows usage. Model actions, storage and availability tier together before you compare the total with the labor and infrastructure of running your own cluster. The rates below are as published on Temporal's pricing page.
- Action rate: Pay-as-you-go pricing starts at 50 US dollars per million actions.
- Active storage: 0.042 US dollars per GB-hour.
- Retained storage: 0.00105 US dollars per GB-hour, billed separately from active storage.
- Availability: A 99.9% SLA, or 99.99% with high availability. Confirm which figure applies to the plan you select.
- Identity: SAML SSO is listed on the pricing page. Confirm whether it is included in your plan.
Ask for a written estimate based on your expected action volume, retention period and region, and confirm which terms the quote covers.
Kestra's open-source edition is licensed under the Apache License 2.0, according to the LICENSE file in the kestra-io/kestra repository. Kestra's edition comparison states that the open-source edition is fully functional for production. The license therefore covers the core orchestrator without a license charge, but the edition has documented limits on identity, secrets and audit that matter for regulated environments.
- Apache 2.0 terms: The license permits use, modification and redistribution, with notice and attribution obligations. Have counsel confirm what applies to your distribution model.
- Included features: Scheduled and event-driven triggers, Git version control integration, built-in plugins for databases, cloud storage and APIs, and scripts in any language, according to the README.
- Documented limits: Basic authentication only, a single-server default deployment, manual secret and access management, and no audit logging.
- Version line: Kestra v2.0.6 was published on October 13, 2026, and the 1.3 line still receives patches such as v1.3.43. Version 2.0 contains breaking changes, so follow its migration guide.
Compare the documented limits against your identity, audit and secrets requirements before you commit to the open-source edition.
Kestra Enterprise Edition is the paid layer on top of the open-source core. Kestra's edition comparison lists the features below. The supplied facts do not include Enterprise pricing, so request a quote from Kestra with a written scope before you set a budget. Pricing, tier boundaries and support terms all come from that quote.
- Identity and access: SSO, SCIM, RBAC, service accounts and API tokens.
- Secrets and audit: External secrets managers such as HashiCorp Vault and Azure Key Vault, plus audit logs.
- Isolation: Multi-tenancy and worker isolation, along with worker groups and task runners for Kubernetes and cloud batch services.
- High availability: An HA architecture using Kafka plus Elasticsearch, Redis, AMQP or GCP Pub/Sub queue backends. Each backend is another system your team must operate.
Ask Kestra which features are included in each tier, which queue backends it supports in your environment, and what support and upgrade terms the quote covers.
Who writes and maintains workflows on each platform
The main difference is who changes a workflow after it goes live. In Temporal, a workflow is application code written in Go, Java, TypeScript or Python, so developers version, test and deploy it alongside the service it supports. Changing a workflow that is already running requires respecting determinism rules, because Temporal replays the recorded event history to resume it. Engineers who change workflow logic therefore need to understand replay behavior, and a careless edit can break in-flight executions. The maintenance load sits with the application team, and that team is usually the one paged when a workflow stalls.
Kestra flows are declarative YAML, and the same flow can be edited in a YAML editor, a no-code editor or the AI Copilot, which stay in sync. Git version control integration lets a team manage flows through its normal review process without cutting an application release. That makes Kestra practical for data engineers and platform staff who own scheduled jobs, data movement and infrastructure runs. The trade-off is that more people can change production flows, so the control burden moves to review rules, branch protection and access roles. On the open-source edition, Kestra's own comparison lists basic authentication only and no audit logging, so role separation for flow authors needs to be designed around the Enterprise Edition features if it matters to you.
Before you choose, confirm how your team will review and deploy changes on each platform. Ask who approves a change to a Temporal workflow that has executions in flight, and whether that change needs a versioning plan. For Kestra, confirm which people can edit flows directly, whether Git is the only path to production, and who holds the on-call duty for failed flows. Write down these owners and approval steps for both platforms, then test them with one realistic illustrative change before you commit.
How each platform recovers when a worker, step or deployment fails
Each card starts from a failure that happens in production and asks what the platform keeps, what it re-runs and what the operator must do. Temporal's answers rest on its complete ordered event history. Kestra's answers depend on its edition and version, so treat its recovery behavior as a set of questions to confirm before you commit.
Worker or process crash mid-run
Temporal stores a complete, ordered event history for each workflow, so a healthy worker can replay that history and resume from the last recorded point. Kestra's open-source edition defaults to a single server, and the high availability architecture with queue backends and worker groups is listed under Enterprise Edition, so a crashed host raises a different question there. Confirm how a Kestra run resumes after a node loss for your version, and test a forced crash on a staging cluster before go-live.
Failed task with retries
In Temporal, a failed activity is retried under the retry policy set in your code, and each completed step is already in history, so it is not executed again when the workflow resumes. Kestra's documentation describes retries and task-level re-execution, but the supplied material does not spell out the retry settings, backoff options or whether a rerun starts at the failed task or the whole flow. Ask Kestra for the exact retry and re-execution behavior of the version you plan to run, and check it against a task that fails on purpose.
Workflow logic changed while running
Temporal replays stored history against the current workflow code, so an execution that now takes a different path than its recorded history can fail during replay. Plan how running executions will be handled before you deploy, and read the Temporal workflows documentation for the versioning patterns it describes. Kestra flows are declarative YAML that can be kept in Git, and its latest major release includes breaking changes with a migration guide, including replacing ForEach and ForEachItem with a single Loop task. Check which in-flight executions a flow change affects.
Bad input or external API outage
A malformed payload or an unavailable API should stop the affected step without corrupting the run. In Temporal, earlier results stay in history, so recovery targets the failing call, and custom codecs can encrypt payloads so the service stores ciphertext. Kestra's sources describe plugins for databases, cloud storage and APIs, but they do not describe how a failed external call is surfaced, retried or alerted on. Confirm error handling and alerting for each external dependency in Kestra's documentation before you rely on it for production data or AI workflows.
What drives the running cost and implementation effort of each
Running cost starts with infrastructure, and the two platforms carry it differently. A self-hosted Temporal Service runs Frontend, History, Matching and Worker services, plus a persistence store and, for advanced visibility, a separate store. Persistence is tested on Cassandra, PostgreSQL 13 to 16 or MySQL, with SQLite for development only. Elasticsearch 7 or 8, OpenSearch 2 or later, or a SQL visibility store can be added, and the Temporal visibility docs recommend Elasticsearch or OpenSearch beyond a few workflow executions. Kestra's open-source edition is a single-server default, so its footprint is smaller at the start. Kestra Enterprise high availability adds Kafka with Elasticsearch, or Redis, AMQP or GCP Pub/Sub queue backends, plus worker groups. Ask any vendor or DeployOpen for a written sizing of each backing service in your own cloud account or data center, because those lines recur every month.
Skills drive the recurring people cost more than software does. Temporal workflows are written in Go, Java, TypeScript or Python, so the team that builds them also needs to understand determinism, versioning and replay. Kestra flows are declarative YAML assembled from plugins, which lowers the bar for operators and analysts, but someone still owns plugin choice, secrets and Git-based change control. Upgrades are the largest recurring line for Temporal. The upgrade guide asks you to move one minor version at a time, to the latest patch of the current minor first, and to run schema upgrades before server upgrades when release notes require them. Budget for a test environment that mirrors production for each step. For Kestra, the 2.0 major release contains breaking changes, and the dedicated migration guide replaces ForEach and ForEachItem with a single Loop task. Any flow that uses those tasks must be rewritten and retested before the upgrade.
One-time implementation work differs from recurring operations, and a quote should separate them. One-time work includes platform design, persistence and visibility selection, deployment (Docker Compose, Go binaries or Helm, where chart versions below 0.73.1 do not work with server images 1.30 and later), identity wiring, and the first set of workflows or flows. Recurring work includes patching, upgrades, on-call, capacity changes and maintenance of flows and plugins. Paid editions and managed services change this split. Temporal Cloud is a managed alternative to self-hosting, and its pay-as-you-go pricing is published on the Temporal pricing page. Kestra Enterprise Edition is a paid license that unlocks SSO, RBAC, audit logs and high availability. This page does not estimate DeployOpen prices. Ask for a written scope listing each service and environment, the upgrade and test cycles included, the retainer terms, and which Kestra Enterprise or Temporal Cloud features are in or out of scope.
Moving a workflow portfolio from Kestra to Temporal, or back
This sequence sorts an existing Kestra flow inventory into what belongs in Temporal application code and what stays in Kestra's pipeline model, then moves each workflow with a tested cutover. The result is one platform per workflow, with triggers, secrets and schedules verified on the new side before the old ones are removed.
Inventory flows, triggers and dependencies
The platform or workflow owner lists every Kestra flow, its namespace, its triggers (schedules, event triggers, webhooks), the plugins and scripts each task calls, and the systems it writes to or reads from. Include flows that call other flows and any that feed AI agents or the MCP server. The step is done when every flow has one row with its trigger type, its external dependencies and its owning team, and nobody can name a flow missing from the list.
Classify each flow by workload type
Architects mark each flow as application logic (long-running business processes, human-in-the-loop steps, state that must survive restarts and be replayed) or as data, AI and infrastructure pipelines (scheduled batch jobs, file and database movement, plugin-driven tasks). Mixed flows are split at the point where the workload type changes. The output is a classified inventory that a reviewer signs off, with the reason recorded beside each decision.
Assign an owning platform to each workflow
For each classified flow, the platform owner decides whether Temporal or Kestra owns it going forward and records the reason. Ask whether developers who will maintain the logic write application code, whether the team relies on Kestra's plugins and YAML editor, and whether Kestra Enterprise Edition features such as SSO, RBAC, audit logs or worker isolation are required. Flows that need those paid features should stay on Kestra unless a supplied fact on the Temporal side covers the same control. The output is a decision register with one owner per flow.
Rebuild flows as code or YAML
Engineers rewrite each flow on its target platform. Business logic moves into Temporal workflows written in a supported SDK language, with side effects placed in activities and non-deterministic calls kept out of workflow code so replay stays safe. Pipeline flows stay as Kestra YAML, with plugin tasks reconfigured to the Kestra plugins the team already uses. Plugin-specific tasks have no direct equivalent in Temporal and must be implemented by hand. The step closes when each rebuilt flow passes unit tests and a sample run that matches the documented input and output of the original.
Run both platforms in parallel
Operators keep the Kestra flow and its replacement active side by side, with the new version writing to a staging target or a separate output location. Compare run outcomes, retry behavior, timing and output records over several scheduled cycles for each flow, and record every difference. This is the point where Temporal's replayable event history and Kestra's execution logs can be checked against each other for the same input. The step is done when every difference is explained and accepted by the workflow owner.
Cut over triggers and secrets
Platform engineers recreate each trigger on the target side and move credentials. On Kestra, secrets may come from the open-source secret handling or from external secrets managers available in Enterprise Edition. On self-hosted Temporal, confirm the Authorizer, ClaimMapper, mTLS and OIDC settings that protect the frontend, and confirm how payloads are encrypted with a codec. Switch webhooks and event sources to the new endpoint only after a test event produces the expected result. The output is a trigger inventory showing old and new endpoints, with access tested for each service account.
Retire old schedules and confirm handover
Once a flow has run cleanly on its new platform through the agreed comparison period, the team disables the Kestra schedule or trigger, records the date and the owner, and removes the flow from active namespaces. Keep the YAML or workflow code and the test results in version control so the change can be reversed. The step closes when the decision register shows every workflow with one active owner, no duplicate schedules remain, and operations has accepted the runbook.
Confirm effort with a scoped assessment
Before committing to a timeline, the client asks for a written, scoped assessment that counts flows by type, plugin dependencies, trigger types and secrets, and estimates the rebuild and parallel-run work for each group. DeployOpen plans and delivers migrations under a fixed scope agreed in writing, so the assessment is the basis for a quote. Ask the same question of any other provider. The effort cannot be estimated responsibly from a flow count alone, so treat any figure given before this assessment as provisional.
What buyers ask before choosing Temporal or Kestra
What does a self-hosted Temporal or Kestra deployment cost to run, and who pays for it?
Software licensing is not the main line item for either platform. Both open-source cores are free to download under permissive licenses: the Temporal Server is MIT licensed, and the Kestra open-source edition is Apache 2.0. The recurring costs are the infrastructure you run (for Temporal, the Frontend, History, Matching and Worker services plus a persistence store and, for advanced visibility, an Elasticsearch or OpenSearch cluster), the staff who patch and upgrade it, and any paid edition or managed service you add. Temporal Cloud publishes pay-as-you-go pricing, starting at 50 US dollars per million actions, per the Temporal pricing page, but a self-hosted cluster has no equivalent public figure. Ask any quote to separate one-time setup from monthly operations, and to state who carries the on-call and upgrade load.
How long does an implementation take, and what drives the timeline?
No supplied fact gives a duration for either platform, so treat any number you hear as unverified until it is written into scope. The timeline is driven by the number of workflows, the systems each one touches, the persistence and visibility choice, and how much identity, secrets and audit work the environment needs. Temporal projects usually spend more effort on workflow design, determinism and versioning in application code. Kestra projects spend more on plugin configuration, secrets integration and, if you need them, Enterprise features. DeployOpen plans and delivers implementations under a fixed scope agreed in writing before work starts, so ask for the deliverables, the acceptance tests and the assumptions behind the schedule.
Do we need Kestra Enterprise Edition for SSO and audit logs?
Yes, if you need those controls, according to Kestra's edition comparison. The open-source edition provides basic authentication only, manual secret and access management, and no audit logging. Kestra Enterprise Edition adds SSO, SCIM, RBAC, service accounts, API tokens, external secrets managers such as HashiCorp Vault and Azure Key Vault, audit logs, multi-tenancy and worker isolation. Confirm the current feature list against the Kestra open source vs paid editions page before you buy, and ask the vendor for the license terms and support scope in writing.
Does Temporal ship with authentication, and what must we configure?
No. If no Authorizer is configured, a self-hosted Temporal Service uses the default noopAuthorizer, which allows every API request without authentication or access control. You must configure the controls yourself. The documented options include mTLS with separate internode and frontend settings, a ClaimMapper that maps JWT or certificate claims to namespace roles, an Authorizer plugin, OIDC single sign-on for the Web UI, and payload encryption through custom codecs and a codec server. The deployment docs also say Temporal services should run on hosts not reachable from the public internet. Ask your security team to review these settings against your own threat model before go-live.
Can our data team maintain these workflows without developers?
It depends on the workflow type. Kestra defines flows in declarative YAML and offers a no-code editor that stays in sync with the YAML, so data and platform engineers often maintain scheduled pipelines without writing application code. Temporal workflows are written as code in Go, Java, TypeScript or Python, and their determinism rules mean changes need developer review and versioning discipline. Plan for a developer owner for Temporal workflows in any case. Ask a partner to show who edits each workflow type, how Git review is enforced, and what training your team receives.
Is Temporal Cloud a substitute for self-hosting?
Only if your constraints allow a managed service. Temporal Cloud is a managed offering from Temporal Technologies, and its published pricing page lists a 99.9% SLA, or 99.99% with high availability, and SAML SSO. Self-hosting keeps the cluster, data and network path inside your infrastructure, which matters for some regulated data residency or air-gapped requirements. Temporal Cloud changes who operates the cluster, not the workflow code you write. Confirm data location, encryption ownership and contractual terms directly with Temporal before deciding.
Can we move from Kestra to Temporal later, or the reverse?
Not as a direct export. Kestra flows are declarative YAML, while Temporal workflows are application code with deterministic replay, so a migration means redesigning each workflow, not converting files. The practical approach is to inventory the flows, classify them by trigger type and state needs, and rebuild the long-running application logic first. Kestra's Git integration helps keep the source of record portable, and Temporal's event history cannot be copied into another engine. Ask any implementation partner to estimate the rework per workflow in writing before you commit.
How DeployOpen helps teams choose, implement and migrate
DeployOpen plans, implements and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For Temporal vs Kestra, we start by mapping which workflows belong in code and which belong in declarative flows, then build and hand over only what your team will run.
How we scope
- Map which workflows are long-running application logic and which are scheduled data, AI or infrastructure flows
- Confirm who writes, reviews and maintains workflows, and in which languages or YAML
- Agree deliverables, timelines and exclusions in writing before any work starts, as fixed-scope engagements
How we build
- Deploy the chosen engine in your environment, with persistence and network access restricted to trusted internal networks
- Configure identity, authorization and secrets handling, including which features need Kestra Enterprise Edition
- Test failure recovery, retries and upgrade paths against your own workflows before go-live
How we hand over
- Document architecture, configuration, runbooks and the upgrade order for each component
- Provide evidence for compliance readiness reviews, which independent auditors then assess
- Train operators and workflow authors on the platform you actually run
How we support
- Offer ongoing support, upgrades and continuous testing as a retainer after handover
- Plan version upgrades one minor release at a time, with schema changes applied first where release notes require them
- Support migration planning if you later move workflows between engines, scoped in writing
