Temporal vs Apache Airflow: when each one fits
Choose Temporal when the work is application logic: service calls, approvals, payments or onboarding steps that must keep their state and resume correctly after failures, possibly for days. Choose Apache Airflow when the work is scheduled data movement and transformation, where pipelines run on a calendar or on dependencies between batch jobs and the unit of work is a DAG of tasks. The deciding question is the shape of the workload, not the license. Both projects are open source, with Temporal Server under MIT and Airflow under Apache-2.0, so licensing rarely separates them. What separates them is how state is held, who writes the logic, and what must keep running when something fails.
Four factors settle most decisions. First, state: if a workflow must wait for a human or an external system for days and resume exactly where it stopped, Temporal's ordered event history and replay model is designed for that. If the pipeline is a set of batch tasks that can be rerun for a given data interval, Airflow's scheduler model fits. Second, scheduling: Airflow's scheduler, DAG processor and API server are built around recurring runs, while Temporal workflows are typically started by application events. Third, ownership: Temporal workflows are written as code in general-purpose languages by application engineers, while Airflow pipelines are Python DAGs usually owned by data engineering. Fourth, whether both are needed. Many organizations run Temporal for service orchestration and Airflow for analytics pipelines, and they should confirm with their own teams which platform owns each workflow before standardizing on one.
Before you shortlist either platform, ask your engineering leads which workloads are on the roadmap and who will maintain them after go-live. Confirm which persistence and database stack your operations team can support, since Temporal and Airflow each depend on their own stores. Then ask DeployOpen for a fixed-scope written plan that names the components, environments and handover tasks for each platform you select. For version details, check the Temporal releases page and the Apache Airflow supported versions page before you commit to a release line.
Which scenarios favor Temporal and which favor Airflow
This table maps common workloads to the platform that fits better and explains the reason in terms of durable event history versus DAG scheduling. Read each row as a question about where the state lives and who triggers the work, then check the split row if your scenario mixes both. The main takeaway is that Temporal fits long-lived, event-driven business steps, while Airflow fits scheduled, dependency-ordered data jobs.
| Scenario | Better fit | Reason |
|---|---|---|
| Order or payment saga across services | Temporal | Each step, retry and compensation is recorded in an ordered event history, so a workflow can resume after a service or worker failure without re-running completed steps. |
| Human approval wait lasting days | Temporal | A workflow can pause on a signal and wait for a person to respond, with its state kept durably rather than held in a running task slot. |
| Nightly ETL from source systems to a warehouse | Airflow | A fixed schedule and task dependencies map directly to a DAG, and the scheduler and task logs show each run's status and reruns. |
| ML feature refresh on a timetable | Airflow | Feature jobs run on a cadence with upstream data-readiness dependencies, which DAG scheduling expresses more directly than an event-driven workflow. |
| Dependency-heavy batch jobs with backfills | Airflow | Backfilling past date intervals and rerunning failed tasks in a DAG are core scheduling behaviors, so operators can reason about runs by logical date. |
| Event-driven order fulfillment with partner callbacks | Temporal | Long waits for external callbacks with timeouts are handled inside the workflow's durable state rather than by polling from a scheduler. |
| Split: transactional events feed a nightly analytics load | Both | Temporal runs the live business process and emits completed events, while Airflow loads and transforms those records on a schedule into the warehouse. Confirm data volumes and ownership before splitting. |
How Temporal and Airflow are built and what that means for operations
Temporal and Airflow both need a database and several running services, but the services do different jobs. Temporal keeps application workflow state durable across many services, while Airflow runs scheduled pipelines around a scheduler and a metadata database. The cards below cover the components of each and what they mean for upgrades, on-call and database operations.
Temporal Service component map
A self-hosted Temporal Service runs Frontend, History, Matching and Worker services, backed by persistence and visibility stores. Your application workers run separately from these services and execute your workflow code. Before you commit, map which team owns each service, which of them your on-call rota will page for, and how each is scaled and restarted. Ask the vendor documentation for the current sizing guidance, since it changes with load.
Persistence choices for Temporal
Temporal's tested persistence options are Cassandra, PostgreSQL and MySQL, and SQLite is for development only. The store holds the ordered event history that lets workflows replay after a failure, so it is the component whose loss or slowdown affects every running workflow. Confirm which database your platform team already operates well, and plan backup and restore tests against it, not against the Temporal services alone.
Airflow scheduler and DAG processor
An Airflow installation needs a scheduler and a DAG processor, which parse Python DAG files and decide what runs and when. Because DAGs are Python code loaded from files, parsing load and import errors surface in these components rather than in a running pipeline. On-call engineers should know how to read scheduler and DAG processor health, and upgrades should be tested against your DAG files, since parsing behavior can change between major versions.
Airflow API server, workers and triggerer
The API server serves the web interface and API, while workers and the triggerer are optional parts you add based on how tasks execute and how deferred waits are handled. Decide early whether tasks run on one executor or a distributed worker pool, because that choice changes the number of processes to patch and monitor. Ask who owns the API server exposure and authentication, since it is reachable by users, not only by the scheduler.
Metadata database operations
Airflow's metadata database, typically PostgreSQL or MySQL, stores DAG runs, task state and configuration, and the scheduler writes to it continuously. Its growth depends on run history, so retention and cleanup need an owner from the start. Ask your DBA how migrations run during Airflow upgrades, how long they can lock tables, and whether backups are tested with restores in the same window as your other scheduled pipelines.
Upgrade paths and version lines
Temporal Server is on a v1.x line that receives frequent releases, so upgrade cadence and compatibility with your SDK versions need a standing owner. Airflow 3 is the maintained line, and Airflow 2 reached end of life on April 22, 2026, so any Airflow 2 estate needs a migration plan now. Put both upgrade plans on the same calendar, with rollback steps and a test environment that mirrors production database versions.
How Temporal keeps state through failures and what Airflow retries instead
Temporal and Airflow both recover from failure, but they recover different things. Temporal recovers the state of a workflow execution: it records every step in a complete, ordered event history, and after a worker crashes, another worker replays that history from the start to rebuild the workflow's local variables and position, then continues from the last recorded step. That replay only works if the workflow code is deterministic, meaning that given the same history it makes the same decisions every time. Code that reads the current clock, generates random values, or calls external systems directly during replay can diverge from the recorded history and fail. Temporal's documentation on workflows describes the rules: side-effecting work belongs in activities, which Temporal retries and records, while the workflow function only coordinates them. Teams should review workflow code for these patterns before production, and confirm versioning practice for long-running workflows that will outlive a code deployment.
Airflow's recovery model is scoped to tasks within a scheduled DAG run. When a task fails, Airflow can retry that task according to its retry settings, and operators can rerun failed tasks or clear a run from the UI or API. The scheduler, DAG processor and metadata database record the state of each task instance, so a later run can pick up where a failed task left off, but Airflow does not replay Python code to rebuild in-memory state inside a task. That is the right model for batch pipelines where each task is an idempotent unit of data work, such as extracting a file, running a transform, or loading a table, and where the schedule itself is the unit of work. It is less suited to a single business transaction that waits days for a human approval and must resume mid-logic. Confirm, against the Airflow architecture overview, which components store task state in your deployment, and test that each task is safe to rerun before relying on retries.
Capabilities that decide the choice
This table compares how Temporal and Apache Airflow differ on authoring, execution, state, databases, licensing and current versions. Read each row as a question to answer for your own workload, and treat cells marked as items to confirm as open points for your architecture review.
| Capability | Temporal | Apache Airflow |
|---|---|---|
| Authoring model | Workflows written as code in Go, Java, TypeScript or Python | Pipelines defined as Python DAGs |
| Execution model | Event-driven durable workflows that run until completion, including waits on signals and timers | Scheduled DAG runs triggered on a schedule or by an external event, with task-level retries |
| State storage | Complete ordered event history used to replay and resume workflows after failures | Run and task state kept in a metadata database managed by the scheduler |
| Supported databases (per facts) | Cassandra, PostgreSQL and MySQL tested; SQLite for development only | Metadata database typically PostgreSQL or MySQL |
| Core components to operate | Frontend, History, Matching and Worker services plus persistence and visibility stores | Scheduler, DAG processor, API server and metadata database, with optional workers and a triggerer |
| License | MIT for the Temporal Server | Apache-2.0 |
| Current version line | v1.32 line; v1.32.1 released October 8, 2026 | Airflow 3 maintained, current 3.3.2; Airflow 2 reached end of life April 22, 2026 |
| Confirm before deciding | Client SDK support, upgrade path and visibility store choice for your volume | Executor and worker sizing, and whether your data tooling needs the triggerer |
What the licenses allow and what is paid for
Temporal Server is released under the MIT license, which allows your team to run, modify and embed the server without a license fee for the software itself. The current line is v1.32, with v1.32.1 dated October 8, 2026, according to the Temporal releases page. Workflows are written in general-purpose languages, so your application code and the SDKs you use sit alongside the server in your own repositories.
- Server and SDKs: Confirm the license of each component you adopt, including the SDK for your language and any add-on tooling, since the server license does not automatically cover them.
- Operating cost: You run the Frontend, History, Matching and Worker services and a persistence store yourself, so staff time and infrastructure are the recurring costs.
- Commercial terms: Any hosted or supported offering from Temporal is a separate agreement. Confirm with the vendor what it covers and what it excludes.
- Upgrade planning: The MIT license sets no upgrade schedule, so your own test and rollout effort determines how quickly you move between release lines.
Check the license file in the exact release you deploy and the terms of any support agreement before you build a budget.
Apache Airflow is licensed under Apache-2.0, and the facts available for this comparison show no paid edition of Airflow itself. Airflow 3 is the maintained line, first released April 22, 2025 and currently at 3.3.2. Airflow 2 reached end of life on April 22, 2026, so teams still on it no longer receive fixes from the project. The license removes any software fee for the scheduler, DAG processor, API server or metadata database, but those components still need hosting, patching and a PostgreSQL or MySQL database.
- Version currency: Plan migrations off Airflow 2 now, since an end-of-life release leaves you carrying security risk on your own.
- Managed Airflow: Some cloud providers and third parties sell hosted Airflow. Their pricing, support scope and upgrade handling are set by their own terms, not by the Apache license.
- Provider packages and plugins: Check the license of each third-party operator, hook or provider package that your DAGs import.
- Vendor support: The Apache license does not include vendor support. Any paid support must be contracted separately.
Confirm the Airflow version your platform team will run, its support window, and whether any hosted offering is in scope for your environment.
Neither core project carries a software license fee in the facts supplied, so the spend sits in people, infrastructure and services. Staff time to design, build and operate workflows is usually the largest recurring item. Infrastructure includes compute, databases, storage, monitoring and backup. Implementation services, such as those from a partner like DeployOpen, are paid for through a written, fixed scope, and ongoing support or upgrades are typically handled under a separate retainer.
- One-time costs: Design, integration with identity and data sources, initial hardening, documentation and handover.
- Recurring costs: Hosting, database operations, on-call coverage, upgrades, patching and continuous testing.
- Cost drivers: The number of workflows, the volume of state or task history, high-availability requirements, the number of environments and the evidence your compliance team needs.
- Quote requests: Ask for a line-by-line scope covering components, high availability, environments, handover and who patches what after go-live.
Before you compare quotes, ask each vendor or partner to state in writing which costs are one-time and which recur each year.
Open-source licenses grant the right to use and modify the code, but they do not promise response times, patch windows or data handling for a hosted service. If you want those commitments, they come from a contract. For Temporal, confirm directly with the vendor whether a managed or supported offering exists and what it includes. For Airflow, the question goes to each cloud provider or third party that offers hosted Airflow.
- Support hours and response targets: Ask for documented targets in writing, and confirm whether they apply to your severity levels and time zones.
- Security patch commitments: Confirm how quickly critical fixes are delivered and whether they apply to every release line you run.
- Version support windows: Confirm how long each release line receives fixes, so your upgrade calendar matches the vendor's.
- Data location and exit terms: For hosted services, confirm where data is stored and how you export workflow state and history if you leave.
Do not assume that a hosted offering carries the same terms as the open-source project, and request the contract language before you rely on any commitment.
Both projects are permissively licensed, so license terms seldom decide between them for an internal platform. Most teams are better served by asking which system fits their workloads, who will build and operate it and how it recovers from failure. License review still matters when you embed the engine in a product you distribute to customers, because you must follow the notice and attribution requirements of each license. Your legal team should confirm those obligations for the exact components you ship.
- Internal use: Under either license, running the software inside your own infrastructure for your own workloads carries no royalty.
- Distribution: If you ship a product that includes the engine or its SDK, review notice and attribution obligations for each component.
- Dependencies: Transitive libraries carry their own licenses, so include them in your software bill of materials.
- Skills and staffing: The cost of learning and operating each system usually outweighs any license consideration.
If your legal review clears both licenses, shift the budget discussion to people, infrastructure and contracted support.
What drives the running cost of each platform
The running cost of a Temporal deployment starts with the self-hosted Temporal Service, which runs Frontend, History, Matching and Worker services alongside a persistence store. Temporal Server is MIT licensed, so the software itself carries no license fee, but you pay for the compute and storage behind those services and for the database. The tested persistence options are Cassandra, PostgreSQL and MySQL, and the choice changes what you operate: a Cassandra cluster has different staffing and backup needs than a managed PostgreSQL instance. For Airflow, the recurring infrastructure includes the scheduler, the DAG processor, the API server, any workers and the triggerer, plus the metadata database, typically PostgreSQL or MySQL. Both platforms also carry an engineering cost. Temporal workflows are written in Go, Java, TypeScript or Python, so the people you pay are service engineers who already know one of those languages well. Airflow pipelines are Python DAGs, which usually means data engineers who can maintain scheduling logic, connections and dependencies.
Upgrade effort recurs on both sides, and it is where teams most often underestimate cost. Airflow 2 reached end of life on April 22, 2026, so any installation still on it needs a planned move to the maintained Airflow 3 line, including DAG changes where the code relies on removed behavior. Temporal releases new server lines regularly, and each upgrade of a self-hosted cluster needs database schema changes, rolling restarts and compatibility checks against your SDK versions. Ask any implementation partner to price these as separate line items: the initial build, the first production hardening, and each upgrade cycle. Support contracts are another recurring item. Ask for the scope of ongoing support, who handles on-call for the persistence layer, how security patches are applied, and whether testing of upgrades is included. Initial setup is largely one-time, while infrastructure, on-call and upgrades recur for as long as the platform runs. Confirm current Temporal and Airflow release lines directly with the vendors before you build a budget around them.
What drives running cost for each platform
This table compares the cost drivers for self-hosted Temporal and self-hosted Apache Airflow across infrastructure, skills, upgrades, support, editions and monitoring. Read each row as a question to settle with your team before asking for a quote, since the main takeaway is that the two platforms carry different kinds of running cost.
| Driver | Temporal drivers | Airflow drivers |
|---|---|---|
| Infrastructure | Cost rises with the Frontend, History, Matching and Worker services, the persistence store (Cassandra, PostgreSQL or MySQL) and a visibility store sized for event history volume. Ask how history retention and shard count affect sizing. | Cost rises with the scheduler, DAG processor, API server, metadata database and any worker pool sized for concurrent tasks. Ask how many DAGs run in parallel and how the triggerer is sized if deferrable operators are used. |
| Skills | Cost rises when the team must write and maintain workflow code in Go, Java, TypeScript or Python and operate a multi-service cluster. Ask whether the vendor or partner covers both application code reviews and cluster operations. | Cost rises with Python DAG authoring and Airflow internals such as executors and scheduler tuning. Ask who owns DAG review, connection secrets and metadata database maintenance. |
| Upgrades | Cost comes from tracking server release lines and testing workflow determinism when worker code changes. Ask how versioning of running workflows is handled in your rollout plan and who runs upgrade testing. | Cost comes from moving between Airflow major versions, since Airflow 2 reached end of life on April 22, 2026 per the Airflow supported versions page. Ask what the migration of existing DAGs and provider packages is expected to require. |
| Support options | Ask whether support covers the self-hosted cluster, the SDKs and your persistence layer, and whether it is a fixed-scope project or a recurring retainer. Confirm response expectations in writing. | Ask whether support covers the scheduler and metadata database as well as DAG code, and whether it is a fixed-scope project or a recurring retainer. Confirm which provider integrations are covered. |
| Editions and licensing | Temporal Server is MIT licensed, so license fees are not the driver. Cost comes from any managed or commercial add-ons you choose. Ask what is included in any paid offering before you commit. | Airflow is Apache-2.0 licensed, so license fees are not the driver. Cost comes from the managed service or commercial distribution you choose, if any. Ask what is included in any paid offering before you commit. |
| Monitoring | Cost rises with metrics, logging and alerting across services and with the visibility store used for workflow search. Ask how stuck workflows, task queue backlogs and history size are alerted on. | Cost rises with scheduler health, DAG parse time, task failure alerting and log storage for many task runs. Ask how scheduler lag and task queue depth are monitored and who responds to alerts. |
How to move workflows from one platform to the other
This sequence shows how a team moves a scheduled job into Temporal or an application workflow into Airflow without copying code that does not belong on the new platform. It ends with a cutover that both platforms have been proven to support.
Inventory and classify every job and workflow
List each scheduled job, DAG, and long-running application process, with its owner, trigger, runtime, upstream and downstream dependencies, and the systems it touches. Classify each item as data pipeline work (batch transforms, loads, reporting) or application work (multi-step business transactions, human waits, saga-style compensation). Items that fit neither category are usually the ones that need a design decision before any migration work starts. The output is a reviewed register signed off by the data or application owner. Confirm that the list includes jobs that run only on a schedule nobody remembers setting up, since these are the most common gaps.
Decide what transfers and what is rebuilt
For each item, decide which parts move as they are and which are redesigned. Business logic that lives in plain Python or Go functions can usually be reused, wrapped as an Airflow task or a Temporal Activity. Schedules, connection definitions, secrets and retry settings do not transfer one to one: Airflow expresses them through DAG definitions, connections and task settings, while Temporal expresses them through Workflow and Activity options and its schedule feature. Orchestration logic, state handling and failure recovery are rebuilt on the target platform rather than ported line by line. Record the decision for each item so reviewers can see what was reused and what was redesigned.
Rebuild state handling and retry behavior
Redesign how each workflow keeps state and recovers from failure. In Temporal, workflow code must be deterministic, because the engine replays the complete event history to resume after a failure, so side effects belong in Activities and non-deterministic calls must be recorded through the platform's facilities. In Airflow, tasks are expected to be idempotent and retries are configured per task, with state held in the metadata database rather than inside a running process. Define what a retry means for every external write, including how duplicate submissions are detected. The check is a documented table of each step, its retry policy, and the behavior after a partial failure, reviewed by the engineers who will own the code.
Build the target platform and its operating components
Stand up the components each platform needs in the client's own infrastructure. A self-hosted Temporal Service requires Frontend, History, Matching and Worker services plus a persistence store, with Cassandra, PostgreSQL and MySQL as the tested options and SQLite limited to development. An Airflow installation requires a scheduler, a DAG processor, an API server and a metadata database, typically PostgreSQL or MySQL, with optional workers and a triggerer. Confirm the current versions and supported configurations against the vendor documentation before procurement, and confirm backup, monitoring and upgrade responsibilities for each component. The output is a running non-production environment that passes a smoke test of one representative item from each category.
Run old and new in parallel with reconciliation
Execute the migrated items alongside the originals for a period agreed in the written scope, comparing outputs, timing, and failure behavior. For data pipelines, reconcile row counts, checksums and downstream reports between the old and new runs. For application workflows, replay representative transactions, including failure and retry cases, and confirm that both paths reach the same end state. Keep the original schedule active, writing results to a separate target, until the comparisons are clean. The check is a signed reconciliation report per item that shows matched outputs and an explained list of any differences.
Cut over item by item and retire the old path
Move production traffic one item at a time, starting with the lowest-risk jobs, and keep a documented rollback path for each cutover. Disable the original schedule only after the new path has run successfully under production conditions and the owner has accepted the result. Remove retired jobs from the old scheduler or workflow engine, then archive their code, configuration and run history according to the client's retention policy. Record what was rebuilt, what was reused, and what remains on the old platform for documentation and audit evidence.
Where implementation projects with each platform usually run long
Temporal projects most often slip on workflow code changes. Temporal replays each workflow's recorded event history to rebuild its state, so a change to the order of activities, branching or timers can break executions that are already running after a deployment. Plan a versioning approach before the first production release, and confirm in the Temporal Workflows documentation which determinism rules and versioning mechanisms apply to your chosen SDK language. Add a replay test against recorded histories to every release pipeline. Also confirm who reviews workflow code, because these defects usually pass unit tests and surface only after deployment.
Temporal's self-hosted footprint is the second common delay. A Temporal Service runs Frontend, History, Matching and Worker services plus persistence and visibility stores, and each needs sizing, network segmentation, TLS and monitoring decisions. Confirm which tested persistence option your standards allow, since SQLite is for development only. On the Airflow side, the usual slip is DAG sprawl: many teams add Python DAGs that the DAG processor keeps parsing. Assign owners for DAG review, naming and retirement, and measure parse behavior in staging before onboarding each new team.
Both platforms place a database and an identity layer on the critical path. Airflow's metadata database, typically PostgreSQL or MySQL, holds scheduler state, task instances and run history, so connection limits, indexing and cleanup of old records need tuning before go-live. Confirm these settings against your installed version's guidance rather than relying on defaults. For identity, confirm how the Temporal UI and API and the Airflow API server authenticate users and service accounts, and map operator, developer and auditor roles to your identity provider groups. Ask each vendor for the supported authentication options in writing before the design is frozen.
Questions buyers ask before hiring outside help
How much effort does self-hosting Temporal take compared with running Apache Airflow?
Both are services you run yourself, so the effort depends on how many components you operate and how much of the data layer you own. A self-hosted Temporal Service runs Frontend, History, Matching and Worker services plus persistence and visibility stores, with Cassandra, PostgreSQL and MySQL as tested production options. An Airflow installation needs a scheduler, a DAG processor, an API server and a metadata database, typically PostgreSQL or MySQL, with optional workers and a triggerer. Effort rises with the number of environments, the persistence choice, the upgrade cadence and the number of teams publishing workflows or DAGs. Ask any provider to list each component, its owner and its upgrade path.
Can our in-house platform team run Temporal or Airflow without outside help?
Often yes, if the team already operates stateful services and has engineers who can write and review the code that defines the work. With Temporal, application engineers own workflow code in Go, Java, TypeScript or Python, while the platform team owns the cluster and its persistence. With Airflow, data engineers own Python DAGs and the platform team owns the scheduler and metadata database. Gaps usually appear in upgrades, backup and restore of the persistence layer, and on-call coverage. Airflow 2 reached end of life on April 22, 2026, so a team still on Airflow 2 has a migration to plan before anything else. Assess skills against the component list before deciding.
What work do our own engineers need to do during an engagement?
Most DeployOpen engagements are fixed-scope, so your engineers' time should be written into the scope before work starts. Typical inputs include access to your cloud accounts, data center or air-gapped network, identity provider details, database standards, an inventory of workflows or DAGs, and a named business owner for each process. For Temporal, expect input on workflow code ownership and worker deployment. For Airflow, expect input on DAG repositories, connections and secrets handling. Ask for a written list of each input, its owner and the date it is needed. Ongoing support can then be agreed separately as a retainer.
How long does a fixed-scope implementation usually take?
No researched duration applies to this comparison, so the timeline should come from the written scope rather than a general figure. The main drivers are the number of workflows or DAGs, the number of systems each one touches, the number of environments, the persistence choice, identity integration, and how much existing code or pipeline logic must be moved. Compliance evidence requirements also add time when assessors need specific records. Ask for milestones, the dependencies on your own teams, and the acceptance criteria for each milestone. Timelines are agreed in writing before work begins, so treat any number quoted earlier as provisional.
Can Temporal and Airflow run together in one architecture?
Yes, teams can run both, with Airflow handling scheduled batch pipelines and Temporal handling durable application processes. One pattern has Airflow start a Temporal workflow when a pipeline produces an event that needs long-running handling, while another has a Temporal workflow trigger an Airflow run. The integration is design work, not a default feature, so decide which system owns retries, timeouts and where state lives. Running both also means two component sets, two upgrade cycles and two sets of monitoring. Confirm the exact trigger mechanism against each project's documentation before committing to it.
What should we confirm about licensing before choosing either platform?
Temporal Server is MIT licensed, and Apache Airflow is Apache-2.0 licensed, so both permit self-hosting under permissive terms. Confirm the license of every SDK, provider package, plugin and container image you pull, because dependencies can carry different terms from the core project. Check whether your architecture depends on any hosted or managed offering, since its terms are separate from the open-source code. Ask your legal or compliance team to review the notice files in the exact versions you deploy, such as Temporal v1.32.1 or the Airflow 3 line, rather than relying on a summary.
Does DeployOpen handle upgrades and support after go-live?
Ongoing support, upgrades and continuous testing are available as retainers, so post-go-live work is scoped separately from the initial implementation. Both platforms change regularly: Temporal Server has active release lines, including v1.32.1 released on October 8, 2026, and Airflow 3 is the maintained major line. Each upgrade can touch persistence schemas, workers, DAG code and the integrations between them. Ask for the scope of upgrade testing, the notice period for security fixes, the handling of rollback, and who is on call for production incidents. A retainer defines these responsibilities in writing, but it does not remove the need for your own operations staff.
How DeployOpen helps choose, implement or migrate
DeployOpen is a Chainscore Labs company that plans, implements, integrates, hardens, backs up and documents self-hosted open-source platforms inside the client's own infrastructure. For Temporal vs Apache Airflow, we start by agreeing which workload needs which engine, then scope the work in writing before any build starts.
How we scope
- Map which workflows are application-level durable processes and which are scheduled data pipelines
- Inventory the target infrastructure, cloud accounts, data centers or air-gapped networks, and persistence choices
- Agree deliverables, timelines and responsibilities in a fixed-scope written document before work begins
How we build
- Deploy the Temporal Service components and the Airflow scheduler, DAG processor, API server and metadata database
- Integrate workers, identity, secrets and logging with your existing internal systems and access controls
- Test failure recovery, retries and scheduling behavior against scenarios agreed during scoping
How we hand over
- Document architecture, configuration, runbooks and the reasoning behind each component choice
- Deliver backup and restore procedures and walk your platform and engineering teams through operations
- Prepare compliance-relevant evidence and controls for your own independent auditors to assess
How we support
- Offer ongoing support, upgrades and continuous testing as a retainer after handover
- Plan version upgrades around each product's supported release lines and end-of-life dates
- Re-run security testing of workflow APIs and workers as your pipelines and integrations change
