Can I run Render Workflows alongside my existing Render services?

Yes. Workflows integrate directly with your web services, private services, and Postgres databases on Render. Your task instances communicate over your private network, eliminating the need for cross-platform integrations or managing multiple cloud providers. Deploy your entire stack from a single Git repository.

Can I distribute work across multiple parallel tasks?

Yes. Tasks can spin up subtasks to distribute work efficiently. Use Python's asyncio.gather() to run subtasks in parallel, scaling to hundreds or thousands of concurrent instances. Render automatically handles queuing, provisioning, and orchestration. Each task instance runs in its own compute environment with 1 CPU and 2 GB of RAM.

How long can tasks run?

Each task instance can run for up to 2 hours, with plans to extend this limit further. This long-running compute handles AI inference, multi-step LLM chains, and large dataset processing without serverless timeout constraints. Tasks spin up in under one second and scale down to zero between runs.

Do I need to learn a new framework to use Workflows?

No. Convert your existing functions into durable tasks by adding the @task decorator from the Render SDK. You don't need to rewrite your application logic or adopt opinionated frameworks. Deploy with git push and Render registers your tasks automatically.

Which languages does Render Workflows support?

The Workflows SDK is currently available for Python with TypeScript support in the near future. SDKs for additional languages are planned for future releases. For languages without SDK support, you can run tasks by calling the Render API directly.

workflows

Durable Workflow Platforms for AI Agents and LLM Workloads

Q: What happens when a task fails?

Render automatically retries failed tasks with exponential backoff. You configure retry behavior for each task in your workflow definition. View execution logs, inspect retry attempts, and debug failures with full stack traces in the Render Dashboard. Your workflow maintains its progress and resumes from the last successful checkpoint.

January 14, 2026

TL;DR: Render Workflows give you durable task execution with automatic retries and distributed computing without managing control planes, worker infrastructure, or complex pricing. Convert your existing functions into durable tasks with a simple decorator, deploy with git push, and scale to thousands of concurrent runs.

AI agents and LLM-powered applications have created unprecedented demand for durable execution. When your application chains multiple LLM calls, handles unpredictable API rate limits, or processes long-running inference jobs, you need workflows that can recover from failures without losing progress. These workloads are inherently non-deterministic and prone to failures from model timeouts, quota exhaustion, and API errors. Teams building these systems face a choice: manage complex orchestration infrastructure or compromise on reliability.

Common approaches

Self-hosted orchestration platforms

Running platforms like Temporal provides full control and powerful guarantees. This involves deploying multi-service clusters, configuring datastores, managing worker pools, and handling upgrades. Teams need dedicated infrastructure expertise.

Managed orchestration services

Cloud-based platforms handle infrastructure but introduce usage-based pricing models (per step, per event, per developer seat). These work well when usage patterns align with pricing tiers.

Custom solutions

Building retry logic, dead-letter queues, and observability from scratch provides maximum flexibility. However, this requires ongoing maintenance and development resources that could otherwise go toward application features.

The orchestration landscape

Temporal: Heavy-lift, production-grade durability

Temporal delivers exactly-once semantics and workflows that can run indefinitely. It's battle-tested at companies like Netflix and Uber with strong consistency guarantees. This requires operating a multi-service cluster (Cassandra/Postgres plus multiple worker pools) or adopting Temporal Cloud. Teams need to learn deterministic coding constraints and adopt its opinionated framework, which provides powerful guarantees at the expense of operational complexity.

For AI and LLM workflows specifically, Temporal faces workflow history saturation issues due to large LLM payloads, requiring teams to implement payload codecs to offload data to external storage as a workaround.

Inngest: TypeScript-first orchestration

Inngest provides a TypeScript SDK with native async/await patterns through its step.run() API. The platform handles retries and observability out of the box. Pricing is based on steps executed, events processed, and per-developer seats, which can scale unpredictably with usage. While the developer experience is excellent with its TypeScript-first approach, teams should forecast costs carefully since each workflow step is charged individually. With per-developer seat fees at high volumes, monthly costs can scale significantly.

For AI and LLM workloads, the step-based pricing model can become expensive quickly when orchestrating multiple model calls, retries due to rate limits, and complex agent interactions that generate numerous billable steps.

DBOS: Postgres as orchestration

DBOS uses Postgres as the orchestration layer, allowing teams to annotate functions and get checkpoint-based recovery without additional infrastructure. The approach integrates naturally for teams already running Postgres-backed applications and includes automatic retries, exactly-once guarantees for DB operations, and observability via OpenTelemetry traces. As a newer entrant, it has a smaller community and ecosystem compared to established platforms.

AWS Lambda durable functions: Extending serverless for AI workloads

AWS recently introduced durable execution for Lambda, enabling fault-tolerant applications that can run for up to one year through a checkpoint-and-replay mechanism. Durable functions integrate with existing AWS infrastructure through IAM roles, allowing developers to run slow or chained LLM steps inside Lambda without waiting costs, starting containers, or managing extra compute paths.

However, the 15-minute invocation limit remains a significant constraint for AI and LLM workloads. Complex agent workflows, large-scale batch inference, or multi-step reasoning chains often exceed this window, requiring you to architect around frequent checkpointing. The replay mechanism also demands deterministic execution order, which conflicts with the inherently non-deterministic nature of LLM responses and agent behaviors.

What engineering teams need from orchestration

Effective orchestration systems should provide:

Automatic retries with exponential backoff when tasks fail, rather than requiring manual intervention
Visibility into execution paths through distributed tasks with clear error messages and stack traces, exportable to existing monitoring tools
Developer experience that allows defining workflows as code rather than YAML pipelines, with local testing and standard CI/CD deployment
Managed infrastructure for control planes and message brokers to reduce operational overhead
Long-running compute without serverless constraints for AI inference, data processing, and multi-step workflows

Render Workflows provide SDK-first durable task execution with fully managed infrastructure. You convert your existing functions into durable tasks by adding decorators from the Render SDK. Connect your Git repository in the Render Dashboard, and Render detects your tasks, builds your project, and registers them without requiring separate worker pools or orchestration infrastructure.

Workflows integrate directly with the rest of your stack on Render. Your tasks run alongside your web services, private services, and Postgres databases, communicating over your private network. You don't need to manage glue code or complex integrations between platforms.

Task instances support hours of execution time for processing large datasets, running ML inference, or executing multi-step LLM chains. This long-running compute gives you flexibility that serverless platforms can't match. Tasks spin up in under one second, distribute work across thousands of parallel instances, and scale down to zero between runs. Render manages scaling automatically, so your workflows handle whatever traffic you throw at them.

How it works in practice

Define your workflow

Render Workflows allow you to convert existing functions into durable tasks using decorators. You don't need to rewrite your application logic or learn a new framework.

python

from render_sdk.workflows import task, start

# Convert any function into a durable task with a decorator
@task
def calculate_square(a: int) -> int:
  return a * a

if __name__ == "__main__":
  start() # Workflow entry point

Deploy with Git

Create a new workflow service in the Render Dashboard. Link your repository. Render builds and registers your tasks automatically on every push.

Run from your application

python

from render_sdk.client import Client
import asyncio

async def run_task():

  client = Client()

  started_run = await client.workflows.run_task(
    task_identifier="my-workflow/calculate-square",
    input_data=[2]
  )

  print(f"Task run started: {started_run.id}")
  print(f"Initial status: {started_run.status}")

  finished_run = await started_run

  print(f"Task run completed: {finished_run.id}")
  print(f"Final status: {finished_run.status}")

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

Monitor execution

Track task progress in the Render Dashboard where you can view execution logs, inspect retry attempts, and debug failures with full stack traces.

Comparing orchestration platforms

Choose Render Workflows when you:

Need durable task execution for AI agents or LLM-powered workloads without managing infrastructure
Want to convert existing functions into durable tasks with simple decorators rather than rewriting code
Run workflows as part of a larger application stack on Render without managing cross-platform integrations
Need long-running tasks without serverless timeout constraints for inference or data processing
Prefer SDK-first development with automatic scaling managed for you

Evaluate other platforms if you:

Already operate a self-hosted orchestration platform successfully
Need the maturity and ecosystem of platforms like Temporal or AWS Step Functions
Require specific framework features available in more established platforms
Have compliance requirements beyond SOC 2 / HIPAA

Get started with Render Workflows

Render Workflows eliminate the operational overhead of managing orchestration infrastructure. Instead of configuring control planes, operating worker pools, and debugging distributed systems, you can focus on building application features that matter to your users.

To start building with Workflows:

Review the Workflows documentation for detailed guides and API reference
Explore example workflows in the Render examples repository
Join the Render community to discuss patterns and best practices with other developers

Deploy your first workflow today and experience durable task execution without the infrastructure complexity.