2026 Comparison

DBOS vs. Temporal

DBOS is the leading Temporal alternative for teams that need durable, fault-tolerant workflows without adding high operational and infrastructure costs. Unlike Temporal, which requires a separate orchestration server and Cassandra data cluster to host, DBOS runs as a library inside your existing application and stores workflow state directly in Postgres, which makes it faster to adopt, cheaper to run, and simpler to operate.

Billions

of durable workflows per day

Zero

new infrastructure required

Zero

lock-in

Why DBOS?

Simpler to adopt. Simper to operate.

DBOS is a library you install, not a service you run. If you already use Postgres, you already have everything you need.

Built on Postgres

Install the open-source library, connect to Postgres, annotate your workflows and steps. No cluster to provision, no orchestration server.

Durable queues

Control concurrency globally, per-worker, or per-tenant. Temporal has no comparable queueing abstractions.

Sub-2ms step latency

Each step checkpoint is a single Postgres write. Temporal's async dispatch model adds tens to hundreds of milliseconds per step.

Your data stays yours

Workflow checkpoints live in your Postgres. Nothing is sent to an external server. No sensitive data ever leaves your infrastructure.

SQL-powered introspection

Search, pause, cancel, or resume workflows using SQL. Fork any workflow from any step, programmatically or from the web UI.

Single point-of-failure

DBOS only depends on Postgres. Temporal adds two: the Temporal server and its Cassandra data store each a new operational risk.

How it works

Durable in minutes. Not weeks.

Take DBOS to prod with simple annotations on the functions that already exist in your codebase. With Temporal, you move your code into workers, configure a server, and require a rewrite on your app & workflows.

DBOS

~20 minutes

Temporal

~2 weeks

1. Install the open-source library

1. Provision and operate a Temporal server

2. Connect to your existing Postgres

2. Set up Cassandra as the backing data store

3. Add @DBOS.workflow() and @DBOS.step() decorators

3. Build and scale a cluster of Temporal workers

4. Deploy your application exactly as before

4. Rewrite app & workflow interactions via Temporal API

dbos-build.py

# Annotate what you already have
@DBOS.step()
def validate_payment(): ...


@DBOS.step()
def ship_order(): ...


@DBOS.workflow()
def checkout_workflow():
    validate_payment()
    check_inventory()
    ship_order()
    notify_customer()

temporal-build.py

import asyncio
from datetime import timedelta
from temporalio import activity, workflow
from temporalio.client import Client
from temporalio.worker import Worker

# 1. Define Activities (Equivalent to DBOS Steps)
@activity.defn
async def validate_payment() -> str:
    pass

@activity.defn
async def check_inventory() -> str:
    pass

@activity.defn
async def ship_order() -> str:
    pass

@activity.defn
async def notify_customer() -> str:
    pass

# 2. Define the Workflow
# Temporal requires workflows to be defined as classes with a run method
@workflow.defn
class CheckoutWorkflow:
    @workflow.run
    async def run(self) -> None:
        # Activities must be explicitly awaited and require timeout configurations
        await workflow.execute_activity(
            validate_payment,
            start_to_close_timeout=timedelta(seconds=10),
        )
        await workflow.execute_activity(
            check_inventory,
            start_to_close_timeout=timedelta(seconds=10),
        )
        await workflow.execute_activity(
            ship_order,
            start_to_close_timeout=timedelta(seconds=10),
        )
        await workflow.execute_activity(
            notify_customer,
            start_to_close_timeout=timedelta(seconds=10),
        )

# 3. Setup the Client and Worker
# This is the boilerplate required to connect your code to the Temporal Server, 
# which in turn talks to the Cassandra/Postgres datastore.
async def main():
    # Connect to the Temporal server (assuming it is running locally or via Cloud)
    client = await Client.connect("localhost:7233")

    # Initialize and run the worker to host the workflow and activities
    worker = Worker(
        client,
        task_queue="checkout-task-queue",
        workflows=[CheckoutWorkflow],
        activities=[validate_payment, check_inventory, ship_order, notify_customer],
    )
    
    print("Worker starting. Listening to Temporal Server...")
    await worker.run()

if __name__ == "__main__":
    asyncio.run(main())

DBOS vs. Temporal Feature Comparison

Feature by Feature

Feature

DBOS

Temporal

License

MIT

MIT / Apache 2.0

Architecture

LIbrary (no server)

External orchestration service

Step latency

1 to 2 ms (Postgre write)

Tens to hundreds of ms

Infrastructure required

Postgres only

Temporal server & Cassandra

New points of failure

None (only Postgres)

2+ (Temporal server, Cassandra)

Adoption effort

Install library, annotate code

Full application rearchitecting

Workflow introspection

SQL queries + Web UI

Temporal UI + custom queries

Fork / restart from step

Yes, first-class feature

Not available

Data privacy

Your Postgres, your data

Via Temporal server

Durable queues

Built-in with flow control

Built-in with flow control

Infrastructure costs

$0 incremental costs

$$$, to host and run Temporal/Cassandra

Incremental people costs

$0 incremental costs

$$$, with 2-10 FTEs to operate Temporal Cluster

FAQs

Quick answers

Is DBOS a drop-in replacement for Temporal?

DBOS covers the same core use case — durable, fault-tolerant workflows — but with a fundamentally different architecture. The programming model is similar (annotate workflows and steps), but migrating requires updating annotations and removing Temporal-specific SDK calls. See the migration guide at docs.dbos.dev for a step-by-step walkthrough.

Do I need to change how my application is deployed?

No. DBOS is just a library that runs inside your existing application process. You don't need to provision new servers, change your deployment pipeline, or run separate worker fleets. If you have Postgres, you're ready.

How is DBOS more reliable than Temporal in production?

DBOS has exactly one point of failure: Postgres. Most teams already run Postgres and have established monitoring, backups, and SLAs for it. Temporal adds two new failure domains — the Temporal server and its Cassandra data store — that your team must operate independently.

What is 'fork' and why does it matter for recovery?

Fork lets you restart a workflow from any specific step, either programmatically or from the web UI. This is invaluable when a batch of workflows fail due to a bug or third-party outage: once the issue is resolved, you can resume all affected workflows exactly from the failed step, without rerunning earlier work.

Is DBOS suitable for latency-sensitive workflows?

Yes. This is one of DBOS's key advantages. Because each step checkpoint is a single Postgres write (1–2ms), DBOS is well-suited for interactive or otherwise latency-sensitive workflows. Temporal's async dispatch architecture adds tens to hundreds of milliseconds per step, making it less suitable for these scenarios.

What database does DBOS require?

DBOS works with any Postgres-compatible database, including Amazon Aurora, Supabase, Neon, and standard Postgres. If your application already connects to Postgres, no additional database infrastructure is required.

Less infrastructure. Less cost. Same durability.

Get durable workflows without the operational overhead.