Temporal vs Apache Airflow: which to choose

Temporal vs Apache Airflow: when each one fits the job

Choose Temporal when the unit of work is a long-running business process inside an application, such as an order that waits days for a payment callback, a provisioning flow that must survive a worker restart, or a saga that compensates across several services. Temporal workflows are ordinary code in Go, Java, TypeScript or Python, and the server keeps a complete ordered event history so a workflow can replay and resume after failures. Choose Apache Airflow when the work is a scheduled batch pipeline: extract, transform and load jobs, DAG-shaped data dependencies, and a data team that writes Python and wants a scheduler UI for backfills and reruns. Airflow pipelines are defined as Python DAGs, so the people who own the data tend to own the code.

The decision turns on four questions. First, the unit of work: is it a single business instance that may run for weeks, or a dated batch run over a dataset? Second, state handling: do you need the process to remember exactly where it stopped inside application logic, or is task-level state and retry around discrete jobs enough? Third, who writes and maintains the code: application engineers building services in one of Temporal's SDK languages, or data engineers writing DAGs in Python? Fourth, the operating footprint. A self-hosted Temporal Service runs Frontend, History, Matching and Worker services plus a persistence store and, for advanced visibility, a separate search store. An Airflow installation runs a scheduler, a DAG processor, an API server and a metadata database, with optional workers and a triggerer. Confirm which of these your platform team already operates before choosing.

Run a short check against your own workloads before committing. Map three real jobs onto each model: write the failure path for one, including a worker restart in the middle, and see whether the retry and recovery behavior matches what your business expects. Confirm the database choice with your DBA, since Temporal and Airflow both need supported production stores and Airflow explicitly rules out SQLite in production. Ask your security team to review the access model early, because a self-hosted Temporal cluster with no Authorizer configured accepts every API request, so authentication and namespace roles must be set up on purpose. Teams that run both kinds of work often keep Airflow for the data platform and Temporal for application processes, with the two connected through well-defined interfaces rather than shared scheduling.

Which scenarios favor Temporal and which favor Airflow

This table maps common workloads to the platform that usually fits them better and the condition that tips the decision. Read each row as a starting point, then check the condition against your own process shape, team languages and operating model.

Scenario	Better fit	Condition that makes it so
Order or onboarding process that waits days for a person or external event	Temporal	The workflow holds state across human approvals, partner callbacks and timers, and code paths must resume exactly where they paused after a restart or deploy.
Nightly ETL and warehouse loads on a fixed schedule	Apache Airflow	Work is batch-oriented, dependencies are between tasks and datasets, and operators already exist for the warehouse, storage and transformation tools in use.
ML training pipelines with scheduled retraining and artifact dependencies	Apache Airflow	Steps are data-processing jobs scheduled on a cadence and the team wants DAG views of runs, backfills and task logs. Temporal fits only if training is triggered by application events and needs long-lived, signal-driven state.
Service-to-service sagas with compensating actions across microservices	Temporal	Each saga step calls an internal service, failures need compensation logic in the same language as the service, and the team needs durable retries without writing its own state store.
Mixed estate with both data pipelines and application workflows	Both, split by workload	Run Airflow for scheduled data work and Temporal for transactional application flows, and confirm who owns each platform, upgrade cycle and on-call rota before adding the second system.
Team that writes most logic in Python data tooling with few engineers on Go or Java	Apache Airflow	Engineers are comfortable writing Python DAGs and operators, and the workflow does not need a deterministic code model across long-running service calls.
Team that writes services in several languages and wants one workflow engine	Temporal	Workflow code is written in general-purpose SDK languages, and the team can accept the determinism rules that come with replay-based execution.
Workflows that need a strict, auditable retry and state history per business object	Temporal	Each business entity maps to a workflow with an ordered event history, and the team needs to explain exactly what happened to a single order or case.

How each platform is built and what that means for operations

Temporal and Airflow both need several cooperating services and a database before the first workflow runs, but they divide the work differently. The cards below cover each component, the deployment options, and the operational choices that follow from them, so you can estimate the run burden before you commit.

Temporal Service components

A self-hosted Temporal Service runs Frontend, History, Matching and Worker services, backed by persistence and visibility stores. The Frontend receives API calls, History holds workflow state, Matching routes tasks to workers, and the Worker service runs internal system workflows. Each service scales on its own, so ask who will size and monitor all four before you pick a deployment shape.

Temporal deployment options

You can run self-hosted Temporal with Docker Compose, Go binaries, or Helm charts that place each server service in its own Kubernetes pod, or import the server package into a custom Go build. Helm chart versions below 0.73.1 do not work with server images 1.30 and later. Confirm your chosen method against the upgrade path your platform team can support.

Temporal persistence and visibility choice

Persistence holds workflow history and tested options are Cassandra, PostgreSQL and MySQL, with SQLite for development only. Visibility, which powers search over executions, is a separate store. Cassandra visibility was removed in 1.24, and Elasticsearch or OpenSearch is recommended beyond a few workflow executions. Choose the visibility store early, because it adds a second system to back up and operate.

Airflow components and metadata database

An Airflow installation needs a scheduler, a DAG processor, an API server and a metadata database, with optional workers and a triggerer. The metadata database, typically PostgreSQL or MySQL, holds DAG runs, task state and connections, so it is the component most likely to need backup and tuning. MariaDB is not supported, and SQLite is for testing only.

Airflow scheduler locking model

Airflow can run more than one scheduler at once for performance and resiliency, coordinating through database row-level locks. This means scheduler capacity depends heavily on the health and tuning of the metadata database, not only on the scheduler hosts. Ask your DBA how lock contention will be monitored before you add schedulers.

Executor choice and upgrade exposure

Airflow ships several executors, including LocalExecutor, CeleryExecutor and KubernetesExecutor, and since Airflow 2.10.0 more than one can run in a deployment. The hybrid CeleryKubernetesExecutor and LocalKubernetesExecutor are no longer supported as of Airflow 3.0.0. Teams migrating older DAGs should confirm their executor setup before the upgrade.

Temporal server upgrade discipline

Temporal guarantees backward compatibility only between two successive minor versions, so you should step through one minor version at a time and move to the latest patch first. Schema upgrades must run before server upgrades when release notes require them. Budget a repeatable upgrade runbook and a staging cluster that mirrors production persistence.

How Temporal keeps state and retries compared with Airflow task runs

Temporal and Airglow handle state in fundamentally different places, and that difference decides how you write retries, recovery and long waits. Temporal records every step of a workflow in an ordered event history, and workers rebuild the workflow's position by replaying that history after a crash or restart. Because replay must reach the same decisions every time, workflow code has to be deterministic: it cannot read the clock, generate random values, or call external systems directly, and any change to the code path must be guarded with versioning so that in-flight workflows still replay correctly. Activities, which hold the side-effecting work, run outside the workflow and carry their own retry policies. Teams should confirm, with a test replay of a sample workflow after each code change, that their versioning discipline holds before they deploy.

Airflow organizes work as a Python DAG of tasks, and each scheduled run is recorded as a DAG run with task instances whose states live in the metadata database, typically PostgreSQL or MySQL. Task retries are configured per task, and the scheduler decides when a task is queued again based on that stored state. Airflow is built around scheduled runs and bounded pipelines rather than processes that wait for an external signal for days. Its model is simpler to reason about when each run has a clear start, a clear end and idempotent tasks that can be rerun for a given logical date. Temporal is simpler when a single process must wait on human approval, a payment callback or a partner event, and must survive worker restarts without a rerun from the start.

The practical check is to list each business process and ask two questions: does it need to pause for an external signal and continue in the same logical instance, and does a rerun of a failed task from its last stored state satisfy the business rule? If the answer to the second question is yes and the process is scheduled, Airflow's task state is usually the simpler tool. If the process must keep one identity across days of waiting, Temporal's replay model fits better, but your engineers must accept the determinism rules and plan for workflow versioning from the first release. Confirm the exact retry and timeout settings in the current Airflow scheduler documentation and the Temporal workflows documentation before you commit.

Capability comparison: execution, languages, security and infrastructure

This table compares how Temporal and Apache Airflow handle programming model, languages, persistence, visibility, executors, authentication defaults and upgrade lifecycle. Read each row as a question to answer for your own workload, and treat any cell marked as a point to confirm as open until your team verifies it against current documentation.

Capability	Temporal	Apache Airflow
Programming model	Workflows are general-purpose code with a complete ordered event history used to replay and resume after failures	Pipelines are Python DAGs scheduled by a scheduler, with tasks run by executors
Language support	Go, Java, TypeScript and Python workflow SDKs	Python DAG authoring only
Metadata or state store	Persistence on Cassandra, PostgreSQL or MySQL; SQLite for development only	Metadata database on PostgreSQL or MySQL; MariaDB not supported; SQLite not for production
Visibility store	Elasticsearch or OpenSearch recommended beyond a few workflow executions; Cassandra visibility removed in 1.24	Confirm the UI and metadata query model your team needs against the Airflow API server documentation
Execution infrastructure	Frontend, History, Matching and Worker services run as a dedicated cluster, deployable by Docker Compose, Go binaries or Helm	Scheduler, DAG processor, API server and metadata database, with optional workers and triggerer; several schedulers supported
Executors and Kubernetes	Kubernetes support through Helm charts that run server services in separate pods; Helm charts below 0.73.1 do not work with server images 1.30 and later	LocalExecutor, CeleryExecutor, KubernetesExecutor and others; Kubernetes 1.30 to 1.35 listed as prerequisites for Airflow 3.3.2
Authentication default	Without a configured Authorizer, the noopAuthorizer allows every API request with no authentication	Confirm the auth manager and access settings for your version in the Airflow auth manager documentation
Upgrade lifecycle	Upgrade one minor version at a time; schema upgrades run before server upgrades when release notes call for them	Airflow 3 is maintained; Airflow 2 reached end of life on April 22, 2026 and receives no fixes

Licensing and editions: what is open and what is paid

The Temporal Server is MIT licensed, so you can run, modify and redistribute it inside your own infrastructure without a license fee from the project. The cost of self-hosting is the infrastructure and the people who run it. A self-hosted Temporal Service runs Frontend, History, Matching and Worker services, plus a persistence store and a visibility store, and you own the upgrades. Temporal Technologies also sells Temporal Cloud, which is a managed service with its own commercial terms. Those terms are separate from the server license, so the MIT grant does not cover them.

Server license: The server code is MIT licensed according to its GitHub repository, with no paid edition of the server itself.
Self-hosted cost: You pay for compute, databases, storage and the staff who operate the cluster, and these recur every month.
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, per Temporal's pricing page.
Cloud SLA tiers: Temporal Cloud lists a 99.9% SLA, or 99.99% with high availability, according to the same pricing page.
Cloud SSO: SAML SSO is listed as a Temporal Cloud feature, while self-hosted OIDC SSO for the Web UI is configured on your side.

Confirm current Temporal Cloud rates, enterprise terms and support options directly with Temporal before you model costs, because pricing pages change. Ask for the action count and storage estimate that your workload would produce.

Moving work from Airflow to Temporal or the reverse

This sequence separates scheduled data pipelines from application processes, so each side moves only what fits its engine, and both run in parallel until the cutover is verified.

Inventory every DAG and workflow
List each Airflow DAG and each candidate Temporal workflow, with its owner, trigger, schedule, upstream and downstream systems, external connections, and the data it reads and writes. Tag each item as scheduled data work (batch extracts, transformations, reporting loads) or application process work (multi-step business operations that wait on people, services or external events for long periods). Your platform team and the owners of each pipeline or service should review the list. The step is done when every item has an owner and a tag, and nobody is left with an undocumented job.
Separate scheduled data work from application processes
Decide which items belong on which engine. Airflow suits Python DAGs that run on a calendar, depend on data partitions and are operated by data engineers. Temporal suits code that must survive restarts, retry individual steps with state, wait for signals and run for long periods inside an application's own language stack. Items that fit neither should stay where they are for now. Record the decision and the reason for each item in a shared table, and review borderline cases with the engineers who will run them after the move.
Decide what transfers and what is rebuilt
For each item, state what moves as-is and what must be rewritten. Business logic in Python can be reused only where the rewrite is a clear port; Airflow DAGs are not Temporal workflows, and Temporal workflows are code in Go, Java, TypeScript or Python with a determinism requirement that a DAG does not have. Schedules, connection definitions and secrets need a separate inventory, because Airflow connections and Temporal namespaces are configured differently. Execution history does not transfer in either direction. Confirm with your engineers which run records you must keep for audit, and export them to your own archive before cutover.
Plan for history and run metadata loss
Temporal event histories and Airflow run metadata do not transfer directly between the two systems, and neither engine's history is a drop-in replacement for the other's. Decide how in-flight work is handled: let running Airflow runs finish before disabling their DAG, or let running Temporal workflows complete on the old workers before the new code takes over. Agree on the retention and export approach for historical records with your compliance or audit owners. Document the record of each completed job in a system you control, so reviewers can answer questions about past runs without querying the retired engine.
Build both paths and run them in parallel
Stand up the target engine in your own environment with the same database, secret and network controls as the source, following the vendor's deployment and security guidance. For Temporal, that includes a persistence store, restricted network access and an authorization configuration, because the default setup allows every API request without access control. Run the migrated items alongside the originals, comparing outputs, timing, retries and alerting for the same inputs. The step is done when outputs match for an agreed set of test runs and the on-call team can see failures on both paths.
Verify outputs, alerts and recovery
Test failure paths deliberately, including a worker restart mid-run, a failed external call, a database outage and a rerun of a completed job. Confirm that retries, timeouts and alerts behave as the design says on the new engine, and that a rerun does not duplicate side effects. Have someone outside the build team check the results against the agreed test list. Sign-off comes from the owners of the data and the services involved, recorded in writing.
Cut over and retire the old path
Switch traffic or schedules for each item in a planned order, keeping the old definition disabled but available for rollback until the owners confirm stable operation. Update runbooks, dashboards and access rights for the new engine. Retire the old definition only after the agreed observation period and after the archived records have been verified. Ask your implementation partner or internal team to confirm the cutover checklist, and treat any item that failed verification as a stop, not a workaround.

Where Temporal and Airflow projects usually run long

On the Temporal side, the longest work is usually workflow determinism and versioning. Workflow code is replayed from a complete ordered event history, so changes to workflow logic must keep replays of already-running executions producing the same decisions. Teams should plan a versioning approach for workflows that will outlive a deployment, and should test replay against recorded histories before each release. Persistence and visibility also need sizing early. Confirm which persistence store your team will operate (Cassandra, PostgreSQL or MySQL, using the versions Temporal tests), and whether visibility will run on Elasticsearch or OpenSearch once the number of workflow executions grows. Moving visibility later is itself a migration project.

Identity is the other large Temporal task. A self-hosted cluster with no Authorizer configured uses the default noopAuthorizer, which allows every API request with no authentication or access control, so replacing it is a day-one requirement rather than a later hardening step. Plan the ClaimMapper that maps JWT or certificate claims to namespace roles, the Authorizer plugin, mTLS for internode and frontend traffic, and OIDC single sign-on for the Web UI. Confirm with your identity team which claims your identity provider can issue, and with your security team whether payload encryption through a custom codec and codec server is required for data the cluster will store.

On the Airflow side, the first decision is the metadata database and its upgrade path. SQLite is for testing only, MariaDB is not supported, and the prerequisites list specific PostgreSQL and MySQL versions, so confirm your database platform against the current prerequisites before you build. Executor selection comes next: Airflow ships several executors, and since Airflow 2.10.0 more than one can run in a single deployment, while the hybrid CeleryKubernetesExecutor and LocalKubernetesExecutor are no longer supported from Airflow 3.0.0. The largest effort is usually the Airflow 3 migration from Airflow 2, which reached end of life on April 22, 2026. Inventory every DAG against the upgrading guide, and refactor DAGs that depend on removed or changed behavior before cutover.

Questions buyers ask before choosing or hiring help

How much effort does it take to self-host Temporal compared with Airflow?

Both need more than a single install, but the work sits in different places. A self-hosted Temporal Service runs Frontend, History, Matching and Worker services, plus a persistence store and, for advanced visibility, a separate visibility store such as Elasticsearch or OpenSearch. The Temporal deployment docs say these services should run on hosts not reachable from the public internet. An Airflow installation needs a scheduler, a DAG processor, an API server and a metadata database, with optional workers and a triggerer. Effort for both grows with the number of environments, the database you choose, and how often you upgrade. Ask each vendor for its current component list and upgrade path, then price your own cluster, backups and monitoring against it.

Can our own team run Temporal or Airflow without outside help?

A team with strong platform engineering and database operations skills can run either one, but the practice needs to be deliberate. Temporal requires you to plan server upgrades one minor version at a time, run schema upgrades before server upgrades when release notes call for them, and secure the default setup, since a missing Authorizer leaves the noopAuthorizer in place, which allows every API request. Airflow requires PostgreSQL or MySQL in production, a supported Python and Kubernetes version, and careful executor choice. Outside help is most useful for the first deployment, the security baseline, and the upgrade runbook. Scope any help in writing before work starts, since most DeployOpen engagements are fixed-scope.

What skills do our engineers need for each platform?

The skills follow the programming model. Temporal workflows are written as code in general-purpose languages such as Go, Java, TypeScript and Python, so application engineers must understand determinism, replay and versioning of workflow code. Airflow pipelines are Python DAGs, so data engineers who already write Python can start quickly, but they must understand scheduling, task idempotency and executor behavior. On the operations side, Temporal calls for experience with the persistence store and the visibility store, while Airflow calls for database administration and worker scaling. Check which language and database skills your team already has before choosing.

How long does a typical implementation of Temporal or Airflow take?

No supplied fact gives a reliable implementation duration for either platform, so this answer cannot give a figure. The main drivers are the number of workflows or pipelines, the persistence and visibility choices, the identity and network integration, the number of environments, and how much of the upgrade and backup process must be built. Ask any provider for a timeline tied to a written scope, with named deliverables and the assumptions behind them. Your own pilot is the best input, so start with one representative workflow or DAG and measure what it takes before committing to a full rollout.

Should we run both Temporal and Airflow?

Many organizations keep both because they solve different problems. Temporal suits long-running application workflows that need retries, durable state and signals across services. Airflow suits scheduled data pipelines with dependencies between batch jobs. Running both adds a second platform to patch, back up and monitor, so the case is strongest when the two workloads are truly different and each has a clear owner. If one platform could cover both in your environment, test that before adding the second. Document the boundary between them so that teams do not split the same process across two engines.

Does Temporal Cloud change the comparison with Airflow?

Yes, it changes the operations side, but not the programming model. Temporal Cloud is a managed service sold by Temporal Technologies, and the Temporal pricing page lists pay-as-you-go pricing starting at 50 US dollars per million actions, plus storage charges and SLA tiers. With a managed service, you pay the vendor for the cluster and move the upgrade and persistence work to them, while your team still writes and tests workflow code. Airflow has no paid edition from the project itself, so a managed Airflow would come from another vendor. Compare the recurring fees, data residency needs and the control you want over the cluster before deciding.

Official sources for this comparison

Start with the Temporal persistence documentation before you size anything. It lists the database versions Temporal tests against (Cassandra 3.11, 4.0 and 5.0.4 or later, PostgreSQL 13 to 16, and MySQL 5.7 and 8.0.19 or later) and states that SQLite is meant only for development and testing. Compare those versions with the database standard your platform team already operates. Then read the Temporal self-hosted deployment guide to see which deployment paths exist (Docker Compose, Go binaries, Helm charts, or a custom Go build) and to confirm the Helm chart version floor for server images 1.30 and later, since that check decides whether your Kubernetes rollout works at all.

Next, read the Temporal self-hosted security documentation and treat the default noopAuthorizer as a deployment blocker, not a footnote. Without a configured Authorizer, every API request is allowed with no authentication or access control. Confirm who will configure mTLS for internode and frontend traffic, how the ClaimMapper will map identity claims to namespace roles, and how OIDC single sign-on for the Web UI will be wired in. Pair that review with the Temporal server upgrade guide, which requires one minor version at a time and schema upgrades before server upgrades when the release notes call for them. Write that sequence into your runbook before the first production release.

On the Airflow side, start with the supported versions page, because Airflow 2 reached end of life on April 22, 2026 and gets no fixes. If you are migrating an existing estate, check the Airflow 3.3.2 prerequisites and the production deployment guide before you commit to a metadata database, since SQLite is for testing only and MariaDB is not supported. Finally, read the architecture overview to map the scheduler, DAG processor, API server, metadata database, workers and triggerer onto the hosts and network zones your team will actually run, and confirm those components with your Airflow contact or your own environment before you size the platform.

How DeployOpen helps you choose, implement or migrate

DeployOpen plans, implements, hardens, documents and supports self-hosted open-source platforms inside your own infrastructure, so a Temporal or Airflow decision can be tested against your real network, identity and data constraints. Engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts.

How we scope

Map which workloads are long-running application processes and which are scheduled data pipelines before choosing an engine.
Agree in writing which languages, SDKs and teams will write and own workflow code.
Confirm persistence, visibility and hosting targets such as Kubernetes, VMs or air-gapped networks.
Define identity, mTLS and authorization requirements, since self-hosted defaults need explicit configuration.

How we build

Deploy the chosen engine with its services, metadata database and supported version pairings in your environment.
Configure authentication, network restriction and payload encryption to match your written security requirements.
Build a reference workflow or DAG to exercise retries, state recovery and upgrade paths in your environment.
Run security testing against the APIs and interfaces exposed by the deployed platform before handover.

How we hand over

Document the architecture, component inventory, upgrade order and backup procedure for your operations team.
Provide runbooks for scheduler, worker and database failure scenarios specific to your deployment.
Prepare control and evidence material for compliance assessments, while independent auditors issue any formal reports.
Walk your engineers through workflow authoring standards and the review process for future changes.

How we support

Offer ongoing support, upgrades and continuous testing as a retainer after the initial handover.
Plan minor and patch upgrades against the vendor's published lifecycle and release notes for your version.
Revisit the scope in writing when workloads move between application and data pipeline use cases.
Ask for a quote built from the written scope, since DeployOpen does not publish a price list.

Temporal vs Apache Airflow