Temporal replaces legacy orchestrators for durable execution

Abandoning batch schedulers for durable state

Temporal replaces batch schedulers like Apache Airflow for applications that require long-running, stateful workflows. I find that teams often mistake these tools for direct competitors, but Airflow targets scheduled data pipelines and ETL tasks through Python-based DAGs. When a process must survive a crash, Temporal uses event history to rebuild the state and resume execution exactly where it left off. Grab illustrates this shift. The company switched to Temporal for its GrabUnlimited subscription platform to replace an architecture using Amazon RDS, SQS, and Redis. This move reduced production incidents by 80% for a platform serving millions of users. The switch addressed issues where the previous architecture led to corrupted membership states and failed renewals because of 5-minute Redis lock limitations. Grab’s team addressed concurrency challenges by using Temporal’s built-in workflow-execution capabilities, assigning the same workflow ID to multiple instances running mutually exclusive operations. You know cron jobs struggle with state, so can one manage a multi-day renewal? This was necessary because the original daily cron job became too slow after the subscriber base grew by over 1000% between January 2022 and June 2023.

Moving beyond visual modeling and state machines

Teams building TypeScript-native microservices often move away from Camunda to avoid the rigidity of BPMN modeling. Twilio transitioned its A2P messaging compliance platform to Temporal to escape the complexity of a custom state machine. Before this migration, Twilio engineers had to manage every possible combination of states and events manually, which became error-prone as the number of messages grew. I would skip Camunda if your team wants to define workflows in code rather than through visual diagrams. Acquia uses Temporal and LangGraph to build agentic AI workflows that orchestrate, rather than just advise, to automate complex digital workflows within governance guardrails. This allows them to build stateful, multi-agent systems that require reliability across many steps, such as resolving customer context through semantic retrieval and knowledge graphs.

Technical constraints and the final verdict

The distinction between these platforms relies on whether the coordinator must be stateless or stateful. Temporal’s model uses workers to poll task queues and execute work, while Airflow relies on a scheduler and workers to run discrete tasks. You should pick Temporal if your microservices need to coordinate long-running processes like order pipelines or if your workflow must wait days for a signal. The integration with the OpenAI Agents SDK allows developers to build AI agents that automatically handle real-world operational challenges such as LLM rate limits, network disruptions, and unexpected crashes without adding any complexity to their code.

I noticed a single user on a subreddit expressed concern that library code should not control activities in an implicit way. Despite this, I would recommend Temporal for any team building agentic AI workflows that require reliability across multi-step inference or tool calls. Developers prioritize Temporal because workflow logic lives in the same language as the service itself. Airflow 3.0 arrived in 2025 with DAG versioning, event-driven scheduling, and task isolation via the Task Execution API, yet it remains primarily for the data engineering plane. Temporal provides SDKs for eight languages, including Go, Java, .NET, PHP, Python, Ruby, Rust, and TypeScript. For teams using Temporal Cloud, the pay-as-you-go plan charges $50 per million actions plus storage by the GB-hour. If you are managing a polyglot microservices stack, how will you handle the operational overhead of multiple different orchestration languages?