DBOS
Open-source durable execution on Postgres for AI agents and workflows.
If you're already on Postgres and want durable execution without managing extra infra, DBOS is a compelling choice. Its tight agent framework integration and generous free tier make it easy to start. But skip it if you need multi-region replication or extreme throughput—Temporal remains better for those cases.
Verified 10h ago · liveness 95/100 · cite: rightaichoice.com/tools/dbos
- AI agent engineers wanting fault-tolerant autonomous workflows
- Backend teams on Postgres needing durable execution without extra infra
- Developers building human-in-the-loop approval systems
- Teams seeking a lightweight open-source alternative to Temporal
- Teams not using Postgres or unwilling to adopt it as state store
- High-throughput scenarios requiring millions of tasks per second
- Enterprises needing multi-region active-active replication out of the box
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Skip DBOS if you're not using Postgres or need multi-region active-active replication out of the box.
Going past 1M checkpoints/mo on Pro adds $50 per million, which can escalate for high-volume workflows.
DBOS's open-source core is free to use anywhere. For tooling and support, Pro at $99/mo (2 seats, 1M checkpoints) suits startups, while Teams at $499/mo (10 seats, 10M checkpoints) fits growing teams. Enterprise is custom. Compared to Temporal Cloud (which starts around $0.10 per workflow hour), DBOS can be cheaper for checkpoint-light workloads, but overage charges add up. Self-hosting avoids these costs entirely.
In short
DBOS — Open-source durable execution on Postgres for AI agents and workflows. Best for AI agent engineers wanting fault-tolerant autonomous workflows, Backend teams on Postgres needing durable execution without extra infra, Developers building human-in-the-loop approval systems. Free to start; paid plans from $99/mo.
What's new in DBOS
Checked 4 days agoAcross the latest 6 updates: 2 feature updates, 1 changelog entry and 3 news mentions.
Mike Stonebraker & Cockroach Labs: Agents on ACID?
DBOS blog post discussing ACID properties for AI agents.
Integrating Workflow Observability via OpenMetrics
DBOS introduces OpenMetrics endpoint for workflow observability with Datadog, Grafana.
What's New in DBOS - June 2026
June product update covering recent DBOS improvements.
Postgres Transactions are a Distributed Systems Superpower
DBOS architecture blog on leveraging Postgres transactions for distributed workflows.
Just Use Postgres for Job Queues
How-to guide on using Postgres as a job queue with DBOS.
Postgres is All You Need for Durable Workflows
Architecture post arguing Postgres suffices for durable workflow execution.
What independent users actually report about DBOS
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
70 mentions across 3 sources (Hacker News, Bluesky, Lemmy).
- +Simple setup for Postgres-native teams: no extra orchestrator needed.
- +Dependency count reduced to just 6 packages in latest releases.
- +Free self-hosted UI for local workflow debugging and visualization.
- +Drop-in Temporal replacement (DBOSify) built entirely on Postgres.
- +Native integrations with OpenAI Agents SDK, LlamaIndex, and Pydantic AI.
- −VC-backed business model sows distrust about long-term viability.
- −Lacks multi-region active-active replication out of the box.
- −Not designed for extremely high throughput or global-scale HA.
- −Tight coupling to Postgres limits database choice flexibility.
- −Some managed Postgres flavors (e.g., DSQL) miss features DBOS requires.
- • Self-hosting at scale may require Postgres expertise and infrastructure investment.
Viability Score
How likely is DBOS to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Durable execution with workflow/step decorators
- Durable queues with configurable concurrency
- Human-in-the-loop pause/resume via send/recv
- Dynamic cron schedule creation and backfill
- Real-time workflow monitoring dashboard (Conductor)
- Automatic failure recovery and restart
- Native integration with OpenAI Agents SDK
- Native integration with LlamaIndex
- Native integration with Pydantic AI
- MCP server for debugging via coding agents
- Role-based access control (RBAC)
- OpenMetrics support for Datadog, Prometheus, Grafana
- Workflow versioning and forking
- Drop-in Temporal replacement (DBOSify)
- Supports TypeScript, Python, Go, Java
About DBOS
DBOS is an open-source durable execution library and cloud platform that makes AI agents and workflows resilient to any failure by storing execution state in Postgres. Designed for backend teams and AI engineers already using Postgres, it eliminates the need for separate queue or orchestration infrastructure. You annotate functions as workflows and steps, and DBOS automatically handles retries, recovery, and observability. Key features include durable queues with configurable concurrency, dynamic cron schedule creation and backfill, human-in-the-loop pause/resume via send/recv, and a real-time monitoring console (DBOS Conductor) for workflow execution. Recent updates added role-based access control, OpenMetrics support for Datadog, Prometheus, and Grafana, and DBOSify—a drop-in Temporal replacement built on Postgres. DBOS integrates natively with OpenAI Agents SDK, LlamaIndex, Pydantic AI, and Google ADK, plus an MCP server for debugging via coding agents. DBOS supports TypeScript, Python, Go, and Java. You can self-host the open-source library or use DBOS Cloud for serverless hosting. Pricing starts free (open-source library), with Pro ($99/month), Teams ($499/month), and Enterprise (custom) tiers for additional tooling and support. DBOS is SOC 2, HIPAA, GDPR, and CCPA compliant. Compared to Temporal, DBOS is more lightweight and simpler to set up for Postgres-native teams, but lacks multi-region active-active replication out of the box and is not designed for the highest throughput scenarios.
Behind the Verdict
DBOS is a strong pick for teams that live in Postgres and want to add durable execution without spinning up Kafka, RabbitMQ, or Temporal Server. The open-source library is free and works with any Postgres database you already have. The DBOSify drop-in replacement for Temporal is particularly interesting if you're considering a migration away from Temporal's operational overhead. Where it falls short is high throughput and multi-region setups. DBOS relies on a single Postgres instance for state, so you won't get the active-active replication Temporal offers out of the box. If you're processing millions of tasks per second, Temporal is still the safer bet. Also, DBOS Cloud (serverless hosting) requires contacting sales, which may frustrate devs wanting to click and go. For agent workflows, native integration with Pydantic AI, LlamaIndex, and OpenAI Agents SDK means you can build fault-tolerant agents without gluing together disparate systems. The real-time monitoring dashboard (Conductor) gives good observability into workflow execution. The human-in-the-loop pattern (send/recv) is well-designed for approval flows. In practice, we'd reach for DBOS when we have a Postgres-centric stack and need durable workflows with minimal new infrastructure. It's also great for teams that want to experiment with durable execution without committing to a full Temporal setup. But for enterprises that need geographic redundancy or extreme scalability, Temporal's maturity still wins.
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Real-world workflow fit
Concrete scenarios for the personas DBOS actually fits — and what changes day-one when you adopt it.
Build a customer support agent that researches queries, checks inventory, and books appointments. Annotate the agent's function calls as steps; DBOS automatically retries on API failures and recovers after process restarts.
Outcome: Agent runs reliably without manual intervention; observability via Conductor shows each step's status and history.
Implement a checkout workflow that creates an order, reserves inventory, processes payment, and fulfills. If payment fails, the workflow releases inventory and cancels the order—all within a single Postgres transaction.
Outcome: Atomic, fault-tolerant checkout that survives server crashes; no separate queue or orchestration service needed.
Set up a nightly data sync that processes 10,000 records using a durable queue with concurrency limit of 10. If the process crashes mid-way, it resumes from the last checkpoint without data loss.
Outcome: Reliable batch processing with built-in retries and progress tracking, all backed by Postgres.
Use Cases
- Orchestrate multi-step AI agent tasks with retry and recovery on failure
- Build a durable queue for indexing documents or processing webhooks reliably
- Schedule cron jobs that survive process crashes and can be backfilled
- Create a checkout workflow that reserves inventory, processes payment, and fulfills orders atomically
- Monitor and debug production workflows with versioning, replay, and fork capabilities
- Implement human-in-the-loop approval workflows for sensitive operations
Limitations
- DBOS is intrinsically tied to PostgreSQL, so applications requiring other database backends may face friction.
- The Conductor dashboard is proprietary, while the core library is open-source.
- Multi-cloud or multi-region deployment is not yet a first-class feature.
as of 2026-07-02
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published DBOS tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
DBOS Pro
$99/month
Ideal for
Solo developers or small startups needing durable workflow tooling for up to 3 apps with Conductor monitoring.
What this tier adds
Starting paid tier; adds Conductor console, workflow tracing, and MCP server over free open-source.
DBOS Teams
$499/month
Ideal for
Growing teams with multiple apps needing RBAC, custom alerts, and compliance support (SOC 2, HIPAA).
What this tier adds
Adds RBAC, custom alerting, OpenMetrics, metadata-only mode, and dedicated Slack support with 2-day SLA.
DBOS Enterprise
Custom
Ideal for
Large organizations requiring SSO/SAML, self-hosted Conductor, air-gapped deployments, and same-day support.
What this tier adds
Custom pricing with option to self-host Conductor, SSO/SAML, and dedicated technical account management.
DBOS Cloud
Contact sales
Where the pricing makes sense
The company stage and team size where DBOS's pricing actually pencils out — and where peers do it cheaper.
DBOS's open-source core is free to use anywhere. For tooling and support, Pro at $99/mo (2 seats, 1M checkpoints) suits startups, while Teams at $499/mo (10 seats, 10M checkpoints) fits growing teams. Enterprise is custom. Compared to Temporal Cloud (which starts around $0.10 per workflow hour), DBOS can be cheaper for checkpoint-light workloads, but overage charges add up. Self-hosting avoids these costs entirely.
Setup time & first value
How long it actually takes to get something useful out of DBOS — broken out by persona, not the marketing-page minute.
For a developer familiar with TypeScript or Python, installing the DBOS library and connecting to a Postgres database takes about 10 minutes. The first workflow with @workflow and @step decorators can be running within an hour. For Teams evaluating the Conductor console, full setup including RBAC and OpenMetrics may take 2–4 hours.
Switching to or from DBOS
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Temporal: use DBOSify to migrate workflows with minimal code changes—DBOSify is a drop-in Temporal replacement built on Postgres.
- →From Celery: rewrite task functions as DBOS steps and replace Celery queues with DBOS durable queues; Postgres replaces Redis/RabbitMQ.
- →From AWS Step Functions: export state machine definitions and reimplement as DBOS workflows in code; benefits from simpler retry logic and local debugging.
- ↗To Temporal: workflows may need refactoring due to different workflow/step semantics and Temporal's SDK concepts.
- ↗To AWS Step Functions: rewrite orchestration logic as JSON state machines; loses Postgres-native state co-location.
Integrations
Resources & Guides
Tutorials & Learning
Official links
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Frequently Asked Questions
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