A Practical Guide to Temporal: What It Does, How It Compares, and When to Use It

Introduction Creating distributed systems might need implementation of special retry mechanisms, state machines, dead letter queues, etc. Despite best practices in software design, unexpected issues may still arise in production systems, requiring manual intervention. Temporal's approach to workflow orchestration is different from other solutions by providing durability and state management as part of the system. It has been adopted by companies such as Netflix, NVIDIA, Snap, and Airwallex in their production systems. While it is essential to understand the advantages and disadvantages of Temporal prior to adoption, particularly when considering complexity, learning curve, and when a simpler solution might be better suited to a particular situation, this article provides a comprehensive analysis of the advantages and disadvantages of Temporal versus other well-established approaches in the industry (Apache Airflow, AWS Step Functions, and Kafka) in order to help readers determine whether Temporal will meet their needs. What is Temporal? Temporal is an open-source workflow orchestration system allowing software developers to write fault-tolerant workflows with regular programming languages (Go, Java, Python, TypeScript, .NET). A primary benefit of utilizing Temporal is its ability to maintain durable code; enabling a workflow to pause for days, weeks, months, etc., while the underlying infrastructure fails, and then resumes at the exact point in time it was paused. This is accomplished through the utilization of event-sourcing (i.e. each decision made by the system is captured as an immutable event). A Basic Example from temporalio import workflow from datetime import timedelta @workflow.defn class OnboardingWorkflow: @workflow.run async def run(self, user_id: str) -> str: # Send welcome email await workflow.execute_activity( send_welcome_email, user_id, start_to_close_timeout=timedelta(minutes=5), ) # Wait 3 days (yes, really) await workflow.sleep(timedelta(days=3)) # Send follow-up if they haven't activated user = await workflow.execute_activity( get_user, user_id, start_to_close_timeout=timedelta(seconds=30), ) if not user.activated: await workflow.execute_activity( send_reminder_email, user_id, start_to_close_timeout=timedelta(minutes=5), ) return "onboarding_complete" from temporalio import workflow from datetime import timedelta @workflow.defn class OnboardingWorkflow: @workflow.run async def run(self, user_id: str) -> str: # Send welcome email await workflow.execute_activity( send_welcome_email, user_id, start_to_close_timeout=timedelta(minutes=5), ) # Wait 3 days (yes, really) await workflow.sleep(timedelta(days=3)) # Send follow-up if they haven't activated user = await workflow.execute_activity( get_user, user_id, start_to_close_timeout=timedelta(seconds=30), ) if not user.activated: await workflow.execute_activity( send_reminder_email, user_id, start_to_close_timeout=timedelta(minutes=5), ) return "onboarding_complete" This example highlights the advantages of using the Temporal system in achieving the core features of workflow orchestration. The 3-day sleep in the code works as expected, even in the face of infrastructure failure. When an activity fails within Temporal, it will automatically attempt to execute again based on the failure policy configured in the code, and all historical activity execution information is available via query within the Temporal UI. However, the above simple example also contains some disadvantages, which we will discuss in the next section. Where Temporal Excels 1. Long-Running, Stateful Workflows Temporal really excels for workflows running over the course of hours, days, or weeks. Traditional cron jobs and the like struggle here because they're inherently stateless, meaning each execution begins with a clean slate and requires external management of the state. Temporal manages the state internally. Evidence: The case study by Netflix (December 2025) found Temporal cut their video encoding pipeline code by 60% compared to their own custom solution. 2. Multi-Step Transactions with Compensation Temporal supports the SAGA pattern for financial systems: @workflow.defn class MoneyTransferWorkflow: @workflow.run async def run(self, amount: float, from_account: str, to_account: str): # Debit source account await workflow.execute_activity( debit_account, from_account, amount, start_to_close_timeout=timedelta(seconds=30), ) try: # Credit destination account await workflow.execute_activity( credit_account, to_account, amount, start_to_close_timeout=timedelta(seconds=30), ) except Exception: # If credit fails, automatically refund await workflow.execute_activity( credit_account, # Refund the source from_account, amount, start_to_close_timeout=timedelta(seconds=30), ) raise return "transfer_complete" @workflow.defn class MoneyTransferWorkflow: @workflow.run async def run(self, amount: float, from_account: str, to_account: str): # Debit source account await workflow.execute_activity( debit_account, from_account, amount, start_to_close_timeout=timedelta(seconds=30), ) try: # Credit destination account await workflow.execute_activity( credit_account, to_account, amount, start_to_close_timeout=timedelta(seconds=30), ) except Exception: # If credit fails, automatically refund await workflow.execute_activity( credit_account, # Refund the source from_account, amount, start_to_close_timeout=timedelta(seconds=30), ) raise return "transfer_complete" The SAGA pattern is a cleaner design than implementing compensating transactions manually. 3. AI Agent Orchestration Temporal has found a great product-market fit with the emerging AI agent space. Multi-agent systems need to handle the hardest things in coordination over long periods of time, LLM API failures, and context. @workflow.defn class TradingAgentWorkflow: @workflow.run async def run(self) -> None: # Market analysis agent runs every hour while True: market_data = await workflow.execute_activity( fetch_market_data, start_to_close_timeout=timedelta(minutes=5), ) # AI agent analyzes data decision = await workflow.execute_activity( analyze_with_ai, market_data, start_to_close_timeout=timedelta(minutes=10), ) # Execute trade if confidence is high if decision.confidence > 0.8: await workflow.execute_activity( execute_trade, decision, start_to_close_timeout=timedelta(seconds=30), ) # Sleep for an hour await workflow.sleep(timedelta(hours=1)) @workflow.defn class TradingAgentWorkflow: @workflow.run async def run(self) -> None: # Market analysis agent runs every hour while True: market_data = await workflow.execute_activity( fetch_market_data, start_to_close_timeout=timedelta(minutes=5), ) # AI agent analyzes data decision = await workflow.execute_activity( analyze_with_ai, market_data, start_to_close_timeout=timedelta(minutes=10), ) # Execute trade if confidence is high if decision.confidence > 0.8: await workflow.execute_activity( execute_trade, decision, start_to_close_timeout=timedelta(seconds=30), ) # Sleep for an hour await workflow.sleep(timedelta(hours=1)) Limitations and Trade-Offs 1. Operational Complexity Temporal's biggest drawback is operational complexity. Running Temporal requires: Temporal Server cluster (3-5 nodes for High Availability) Persistent database (PostgreSQL, MySQL or Cassandra) Elasticsearch cluster for visibility/search Worker infrastructure (your code) Monitoring/alerting setup Distributed systems operational expertise Temporal Server cluster (3-5 nodes for High Availability) Persistent database (PostgreSQL, MySQL or Cassandra) Elasticsearch cluster for visibility/search Worker infrastructure (your code) Monitoring/alerting setup Distributed systems operational expertise Compare this to AWS Step Functions, which needs zero infrastructure work. You just write a JSON state machine and AWS handles everything else. AWS handles the rest. Temporal Cloud provides a solution for those who do not wish to run infrastructure by using a fully-managed solution, although the cost will be $200-$2,000+/month depending on throughput. 2. Steep Learning Curve Temporal's programming model doesn't match the typical request-response pattern most developers know. Teams often struggle with these concepts: Determinism requirements: The workflow has to be deterministic. No direct API calls or random number generation is allowed. These have to be performed within an activity. Breaking this rule will result in failures during replays, which are difficult to debug. Determinism requirements: Workflow versioning: When changing workflow logic and allowing old-running workflows to proceed, versioning has to be performed correctly. Messing up the versioning will break workflows that are already running. Workflow versioning: Real-world impact: Most developers take 2-4 weeks before they're comfortable with Temporal. With AWS Step Functions or Apache Airflow, you are usually productive in under a week. Real-world impact: 3. Debugging Complexity Debugging workflows is complex. When something fails, it requires an understanding of event history replays, worker logs, distributed infrastructure, correlation of activities across multiple services, and decoding of determinism violations. Although Temporal provides an interface with execution history, with complex workflows, there may be thousands of events, making it difficult to debug. Comparison: Debugging microservices with distributed tracing tools such as Jaeger or Zipkin may be easier since it is similar to something developers are used to. Comparison: 4. Performance Limitations Temporal prioritizes durability over speed. Their documentation lists these performance caps: Workflow execution rate tops out around 1,000-2,000 per second per cluster Activities can handle 2,000-5,000 executions per second Workflow history hits performance problems after 50,000 events Workflow execution rate tops out around 1,000-2,000 per second per cluster Activities can handle 2,000-5,000 executions per second Workflow history hits performance problems after 50,000 events This becomes a problem when you are handling thousands of events per second or need really fast response times. In those cases, go with message queues like Kafka or RabbitMQ instead. 5. Cost Structure Total Cost of Ownership includes: Self-hosted: Self-hosted: Infrastructure: $500-$5,000+ per month (depending on scale) Engineering time: 20-40 hours/month for maintenance Expertise requirement: Senior level distributed systems knowledge Infrastructure: $500-$5,000+ per month (depending on scale) Engineering time: 20-40 hours/month for maintenance Expertise requirement: Senior level distributed systems knowledge Temporal Cloud: Temporal Cloud: Base: $200+ per month Additional throughput: $0.025 per action Costs are high at scale compared to self-hosted models Base: $200+ per month Additional throughput: $0.025 per action Costs are high at scale compared to self-hosted models Comparison: With AWS Step Functions, you pay $0.025 per 1,000 state transitions. For typical workloads, that's a lot cheaper. Temporal vs. Alternatives: When to Choose What Temporal vs. Apache Airflow Airflow Advantages: Airflow Advantages: Massive ecosystem with over 1,000 integrations Built for batch data pipelines Scheduling is stronger (cron, etc.) Easier to learn DAG visualization is more user-friendly Massive ecosystem with over 1,000 integrations Built for batch data pipelines Scheduling is stronger (cron, etc.) Easier to learn DAG visualization is more user-friendly Temporal Advantages: Temporal Advantages: Ideal for event-driven workflows Dealing with long-running tasks is more elegant No Python pickle serialization problems Failure recovery is more reliable Ideal for event-driven workflows Dealing with long-running tasks is more elegant No Python pickle serialization problems Failure recovery is more reliable When to Choose Airflow: You are building data pipelines, ETL, batch-oriented scheduled work. Airflow is well understood in these domains with 8+ years of real-world usage. When to Choose Airflow: When to Choose Temporal: You are building event-driven workflows, need complex state machines, or want workflows that survive infrastructure problems. When to Choose Temporal: Temporal vs. AWS Step Functions Step Functions Advantages: Step Functions Advantages: No operational overhead required Integration with other AWS services is deep Cost-effective for moderate workload sizes Faster time to production Error handling and timeouts are well supported No operational overhead required Integration with other AWS services is deep Cost-effective for moderate workload sizes Faster time to production Error handling and timeouts are well supported Temporal Advantages: Temporal Advantages: Works anywhere, not just AWS (cloud-agnostic) Write workflows with real code, not JSON Complex business logic is supported well Activity timeouts and retries are more flexible Being open-source keeps you free from vendor lock-in Works anywhere, not just AWS (cloud-agnostic) Write workflows with real code, not JSON Complex business logic is supported well Activity timeouts and retries are more flexible Being open-source keeps you free from vendor lock-in When to Choose Step Functions: You are on AWS already, want quick deployment, or prefer not dealing with infrastructure. JSON state machines feel limiting for complex stuff, but work fine for straightforward workflows. When to Choose Step Functions: When to Choose Temporal: You want to run anywhere, your workflow logic doesn't fit well in JSON, or You are worried about getting locked into AWS. When to Choose Temporal: Temporal vs. Kafka + Custom State Machines Kafka Approach Strengths: Kafka Approach Strengths: Throughput goes way higher (over 100,000 messages/second) Response times stay low (under 10ms) Better suited for event streaming More mature platform overall Throughput goes way higher (over 100,000 messages/second) Response times stay low (under 10ms) Better suited for event streaming More mature platform overall Temporal Approach Strengths: Temporal Approach Strengths: Don't need to implement your own workflow orchestration State management is built-in Easier to reason about your workflow logic Better developer ergonomics overall Don't need to implement your own workflow orchestration State management is built-in Easier to reason about your workflow logic Better developer ergonomics overall When to Use Each: When to Use Each: Use Kafka when: You are streaming events, processing real-time data, or building event-sourced systems where you need to define the event schema. Use Temporal when: You want workflow orchestration without building the E2E system. Kafka provides you with the raw materials; Temporal gives you the complete package. Use Kafka when: You are streaming events, processing real-time data, or building event-sourced systems where you need to define the event schema. Use Kafka when: Use Temporal when: You want workflow orchestration without building the E2E system. Kafka provides you with the raw materials; Temporal gives you the complete package. Use Temporal when: Critical Gaps and Missing Features 1. Limited Multi-Tenancy Support Temporal has very basic Multi-tenancy support. Namespaces are provided, but resource isolation is not great. For a SaaS application supporting multiple tenants, you would often need multiple Temporal clusters, one per tenant. This creates significant operational overhead. Competitor advantage: AWS Step Functions provides strong isolation per AWS account. Competitor advantage: 2. No Built-in Scheduling UI Airflow comes with a scheduling UI. Temporal doesn't. You'll trigger workflows through code or command-line tools. If you need a scheduling UI, Airflow is recommended. 3. Limited Observability Integrations Temporal exports metrics, but hooking them into your existing observability tools (Datadog, New Relic, Grafana) takes extra work. Airflow and Step Functions handle this better out of the box. 4. Workflow Update Limitations Although updating running workflows is possible, this is very complex. Need to change workflow logic often (like updating business rules)? This gets painful fast. When NOT to Use Temporal Let's be honest about when Temporal is the wrong choice: Simple CRUD APIs: Massive overkill. Use a normal web framework. Sub-second Latency: Each activity in Temporal adds 50-200ms of overhead. If you need responses faster than that, Temporal won't be useful. High Frequency Event Handling: Processing more than 5,000 events per second? Kafka and Kinesis will serve you way better than Temporal. Limited DevOps Resources: Teams without access to infrastructure expertise will experience a tremendous operational burden to support Temporal. Greenfield Projects: Starting something new with unclear requirements? Begin with cron jobs or Step Functions. Move to Temporal only after you understand what complexity You are actually dealing with. Teams New to Distributed Systems: The learning curve is steep and the operational work is heavy. If distributed systems are new territory for your team, this might be too much too soon. Simple CRUD APIs: Massive overkill. Use a normal web framework. Simple CRUD APIs: Sub-second Latency: Each activity in Temporal adds 50-200ms of overhead. If you need responses faster than that, Temporal won't be useful. Sub-second Latency: High Frequency Event Handling: Processing more than 5,000 events per second? Kafka and Kinesis will serve you way better than Temporal. High Frequency Event Handling: Limited DevOps Resources: Teams without access to infrastructure expertise will experience a tremendous operational burden to support Temporal. Limited DevOps Resources: Greenfield Projects: Starting something new with unclear requirements? Begin with cron jobs or Step Functions. Move to Temporal only after you understand what complexity You are actually dealing with. Greenfield Projects: Teams New to Distributed Systems: The learning curve is steep and the operational work is heavy. If distributed systems are new territory for your team, this might be too much too soon. Teams New to Distributed Systems: The Verdict: When Temporal Makes Sense Temporal is an awesome tool that solves many problems in the area of distributed systems orchestration. However, it is not a silver bullet, and the hype often ignores many important limitations. Temporal is suitable when: Temporal is suitable when: You are building complex, multi-step workflows with many hours, days, or weeks of runtime You need high durability and reliability You are an expert in distributed systems You are willing to invest time and resources in operations Alternatives like Airflow and Step Functions don't meet your requirements You are building complex, multi-step workflows with many hours, days, or weeks of runtime You need high durability and reliability You are an expert in distributed systems You are willing to invest time and resources in operations Alternatives like Airflow and Step Functions don't meet your requirements Temporal is NOT suitable when: Temporal is NOT suitable when: You are building simple scheduled jobs (use cron jobs, Airflow, Cloud Scheduler instead) You need sub-second latency or very high throughput You lack operational expertise and can't afford Temporal Cloud Your team is small and needs to move quickly Your workflows are simple enough for AWS Step Functions You are building simple scheduled jobs (use cron jobs, Airflow, Cloud Scheduler instead) You need sub-second latency or very high throughput You lack operational expertise and can't afford Temporal Cloud Your team is small and needs to move quickly Your workflows are simple enough for AWS Step Functions The ecosystem is growing quickly, documentation is improving, and Temporal Cloud relieves operational burden. Yet, teams should approach the use of Temporal with their eyes open to the benefits and the costs.