What do DBOS and Temporal have in common?
Both are open-source durable execution systems with managed cloud offerings. Both guarantee that a workflow's completed steps persist, so a failure at step nine resumes at step nine rather than at step one. Both scope retries to the step that failed, support waits measured in hours or days without holding a process open, and let a workflow pause for an external signal such as a human approval. For a pipeline built around slow, expensive, failure-prone model and tool calls, that shared guarantee is the point.
How do the architectures differ?
Temporal separates orchestration from execution. A workflow service records every event in a workflow's history, and worker processes reconstruct state by deterministically replaying that history. Workflow code lives in workers; the service is a separate system with its own database. DBOS inverts this: there is no orchestrator. Durability lives in a library inside your application, which checkpoints each step's result to Postgres and, on recovery, resumes the workflow by skipping steps whose results are already recorded.
What does each take to operate?
Temporal means running the workflow service and its database, or paying for Temporal Cloud, plus deploying and versioning workers. That overhead buys strong separation of concerns and a control plane built for large fleets of long-running workflows, which is why it suits organizations with platform teams. DBOS needs only Postgres, which most teams already run. The application and its durability ship as one deployable unit, which is the smallest possible operational footprint but couples workflow capacity to application capacity.
How do they handle AI pipeline steps?
The needs of an LLM pipeline are the same on either runtime: a model call that takes seconds and costs real money must not be re-executed because an unrelated step failed, and an approval may arrive days after the run started. On Temporal, model and tool calls are activities with per-activity retry policies, and approvals arrive as signals. On DBOS, they are checkpointed steps, and the workflow resumes from the last completed one. Neither makes a retained model call deterministic; a retried LLM judge can still answer differently.
When should you pick each one?
Pick Temporal when you have many teams sharing workflow infrastructure, histories that run long, an existing cluster, or a platform team that wants one control plane for all durable work. Its multi-language SDKs, including Go, Java, TypeScript, Python, and .NET, also matter in polyglot organizations. Pick DBOS when you want the fewest moving parts, already operate Postgres, and your pipeline lives comfortably inside one application in Python or TypeScript. Teams outgrow operational simplicity less often than they expect.
Where does Rote fit?
Rote is not a third option in this comparison; it compiles onto both. From one proven agent skill, rote compile emits a Temporal project or a DBOS project, with the same declared control flow and the same typed judgment boundaries. The workflow definition is runtime-agnostic, so a team that starts on DBOS for simplicity can re-emit the same pipeline for Temporal when their platform standardizes on it, reviewing runtime-specific code rather than rebuilding the workflow.
--runtime dbos → main.py, one checkpointed step per node, Postgres-backed
--runtime dbos-ts → the same shape in TypeScript
One validated pipeline definition, re-emittable across targets
Frequently asked questions
Is DBOS an alternative to Temporal?
Yes, in the sense that both provide durable execution and most teams run one or the other. They differ in where durability lives: Temporal in a dedicated workflow service with replaying workers, DBOS in a library backed by Postgres inside your own application. The guarantee is comparable; the operational model is not.
Which is better for LLM workflows, DBOS or Temporal?
Neither is universally better. Both give LLM pipelines scoped retries, durable waits, and human approval gates. Choose Temporal for polyglot organizations, shared workflow infrastructure, and platform teams; choose DBOS for the smallest operational footprint on Postgres you already run. The workflow-design decision, which steps are code and which retain a model, is the same on either.
Can I move a workflow between DBOS and Temporal later?
Hand-written workflows migrate the way any code does: manually. With Rote the pipeline definition is runtime-agnostic, so the same validated workflow can be re-emitted for the other runtime. Runtime-specific behavior and any integration code you wrote by hand still need review, but the control flow and deterministic modules carry over.
Compile a workflow
Inspect the open-source CLI or run a compilation in Rote Cloud.