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 runs workflows through a separate orchestration server (self-hosted with its own database, or Temporal Cloud), 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.

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Billions

of durable workflows per day

Zero

new infrastructure required

Zero

lock-in

Why DBOS?

Simpler to adopt. Simpler 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 Workflow Queues with Built-in Flow Control

Both DBOS and Temporal support durable queueing, priorities, and rate limiting. Unlike Temporal, DBOS also provides built-in workflow-level concurrency limits, globally and per queue, without requiring custom coordination logic.

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.

Fewer moving parts

Unlike DBOS, which runs inside your application using Postgres, self-hosted Temporal requires a separate orchestration server. Production deployments typically run four independently scalable Temporal services, plus a database and optional search infrastructure.

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 restructure your code into workflows and activities, run it on workers, and operate or subscribe to a Temporal server.

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 its database (Cassandra, Postgres or MySQL)

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. Restructure code into Temporal workflows and activities

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 (one Postgres write)

Tens to hundreds of ms

Infrastructure required

Postgres only

Temporal server & its database, or Temporal Cloud

New points of failure

None (only Postgres)

Temporal server (4 services), database, optional search

Adoption effort

Install library, annotate code

Restructure code into workflows, activities and workers

Workflow introspection

SQL queries + Web UI

Temporal UI + custom queries

Fork / restart from step

Yes, first-class feature

Reset to an earlier event in workflow history

Data privacy

Your Postgres, your data

Via Temporal server

Durable queues

Built-in with flow control

Built-in with flow control

Cost Comparison

Is DBOS less costly than Temporal?

Running DBOS is often 2-10x less expensive than running Temporal.

Cost

DBOS

Temporal

Incremental infrastructure costs?

$0

$$$, to host and run the Temporal server and its database, or Temporal Cloud fees.

Incremental people costs

$0

$$$ - 2 to 10 full time employees required to operate Temporal cluster (source: Temporal)

Workflow concurrency, capacity, throughput, scheduling, and storage limits.

$0 or none

Yes (Temporal Cloud). Extra cost to increase.

Is DBOS a Temporal alternative?

DBOS as a Temporal Alternative

What you need to know

DBOS is a popular alternative to Temporal. Customers like Yutori, Roche, Notion, and others have chosen DBOS over Temporal.

DBOS is based on years of R&D at Stanford and MIT, led by the creator of Postgres, Mike Stonebraker and Databricks CTO and co-founder, Matei Zaharia. The big idea behind DBOS is to embed durability into your code via a library, rather than having to run a heavyweight workflow orchestration server external to your application as with Temporal.

The bottom line: Temporal requires an orchestration service, self-hosted or Temporal Cloud, and restructuring your application around it. DBOS requires minimal coding changes and no external orchestration server to operate, making it simpler and much less costly than Temporal.

DBOS Conductor console showing the step timeline of an agentic research workflow, with each step's duration as a bar

FAQs

Quick answers

Yes. DBOS supports durable workflow use cases similar to Temporal, with a simpler Postgres-native architecture. Python developers can use DBOSify for Temporal SDK compatibility or migrate to native DBOS APIs.

Usually not. DBOS runs as a library inside your existing application, so you don't need to deploy a separate orchestration server or worker fleet. If you already use Postgres, you can use it to store workflow state. Otherwise, you'll need to provision a Postgres database.

DBOS runs directly inside your application and uses Postgres for durable execution. This eliminates the need for a separate orchestration service, reducing infrastructure complexity and operational overhead, especially for teams already using Postgres.

DBOS lets you restart workflows from a specific step without repeating earlier completed work. Fork creates a new workflow execution with a new ID, while rewind re-executes part of an existing workflow while preserving its ID. These features are useful for recovering from outages, fixing failed workflows, and rerunning workflows after code changes.

Yes. DBOS executes workflow steps directly inside your application and checkpoints their progress to Postgres. This minimizes orchestration overhead, making DBOS well-suited for latency-sensitive applications.

DBOS uses Postgres for production workflow state storage. It supports standard Postgres and compatible services, including Amazon RDS, Aurora, Supabase, Neon, Google Cloud SQL, AlloyDB, and CockroachDB. If you already use Postgres, you can use your existing infrastructure without deploying a separate database system.

Less infrastructure. Less cost. Same durability.

Get durable workflows without the operational overhead.