If you’re orchestrating serverless workflows in AWS, chances are you’ve used or considered AWS Step Functions. While it’s a solid service for coordinating AWS resources, many developers eventually hit limitations that send them looking for alternatives.
In this guide, we’ll explore why teams outgrow Step Functions and why Temporal often emerges as the preferred alternative for modern workflow orchestration.
What is AWS Step Functions?
AWS Step Functions is a managed workflow orchestration service that lets you coordinate multiple AWS services into serverless workflows using a visual interface or JSON/YAML definitions based on the Amazon States Language (ASL). Each step in your workflow can trigger Lambda functions, interact with other AWS services (like SQS, SNS, DynamoDB, etc.), or wait for callbacks, with built-in error handling and retry logic.
Step Functions comes in two workflow types:
- Standard workflows for long-running, durable processes (up to 1 year execution time, with a 25,000 event history limit).
- Express workflows for high-volume, short-duration, event-driven processes (up to 5 minutes execution time). When Step Functions aligns with the use case, it can be effective. If your application lives primarily within the AWS ecosystem and you need reliable serverless orchestration, it can be a reasonable choice — particularly for:
- Simple microservice orchestration: It works reasonably well for basic coordination tasks. However, implementing complex patterns like Sagas for distributed transactions quickly becomes challenging within ASL’s constraints.
- Data processing pipelines: Especially simpler ones. Originally, Step Functions was targeted more towards data analysts looking for a low/no-code solution, rather than developers building complex, stateful applications.
- Basic process automation involving multiple AWS services.
The Limitations That Drive Teams to Seek Alternatives
Despite its advantages for certain AWS-centric tasks, many development teams eventually encounter frustrating limitations with Step Functions:
AWS-Only (Vendor Lock-In)
Step Functions is inherently tied to the AWS cloud. It can only run within AWS and is primarily designed to orchestrate other AWS services. If you need a multi-cloud strategy, want to run workflows on-premises, or simply desire platform independence, Step Functions isn’t an option outside Amazon’s ecosystem.
Limited Logic Complexity and Developer Experience
Workflows are defined using a JSON or YAML–based Domain-Specific Language (DSL), not a general-purpose programming language. This declarative approach makes expressing complex business logic cumbersome.
- Unwieldy Definitions: Defining non-trivial workflows often results in large, verbose, and hard-to-maintain JSON/YAML files. Implementing standard programming constructs like complex loops, sophisticated error handling, or patterns like saga becomes extremely difficult, often requiring developers to push core business logic into numerous Lambda functions, fragmenting the overall workflow logic.
- Code vs. Configuration: Developers often prefer writing logic in familiar programming languages with access to existing libraries, testing frameworks, and debugging tools, rather than wrestling with the constraints of ASL configuration.
State Size, Duration, and Concurrency Constraints
Step Functions imposes hard limits that can block more demanding use cases:
- Payload Size Limit: Input/output data passed between states cannot exceed 256 KB, often requiring workarounds using S3 for larger payloads.
- Duration Limits: Express workflows are capped at 5 minutes. Standard workflows, while lasting up to a year, have a history limit of 25,000 events, which can be exhausted by long-running or highly iterative processes, forcing complex workarounds like chaining workflow executions.
- Concurrency Limits: Parallel execution within a Map state (Standard workflows) is limited in concurrency (currently 40, though this can change), meaning large-scale parallel jobs might run slower than desired due to forced batching.
Debugging Complexity
While Step Functions provides a visual execution history, detailed debugging can be challenging. Understanding the root cause of a failure often requires manually correlating execution IDs and digging through CloudWatch Logs for each involved Lambda function or service. There’s no built-in capability to step through workflow logic, inspect local variables mid-flight, or easily pause and resume a specific workflow instance for debugging.
Cost Concerns at Scale
Step Functions charges primarily based on state transitions (Standard) or execution duration and requests (Express). For complex workflows with many steps, frequent executions, or long waits, the costs can accumulate rapidly, sometimes becoming unpredictable or prohibitive at high scale.
Why Temporal is the Leading Alternative
When development teams need capabilities beyond what Step Functions offers, or are starting new projects requiring robust, scalable orchestration, many turn to Temporal. Let’s explore why Temporal often emerges as the superior choice.
Code-First Developer Experience
Unlike Step Functions’ JSON/YAML-based approach, Temporal empowers developers to write Workflows as code in familiar programming languages (Java, Go, TypeScript, Python, etc.). This means you can use standard programming constructs (loops, conditionals, try/catch), libraries, and design patterns directly within your Workflow definition.
This immediately solves one of Step Functions’ biggest pain points — expressing complex logic becomes as straightforward as writing regular application code, rather than fighting with DSL syntax and limitations. Teams migrating from Step Functions consistently cite Temporal’s code-based approach as far superior for managing real-world complexity.
Durable Execution and Unmatched Reliability
While Step Functions persists state between steps, Temporal provides true Durable Execution. This means the entire execution state of a Workflow — including local variables, call stacks, and thread state — is automatically and continuously persisted by the Temporal Platform.
A Temporal Workflow can run for seconds, days, months, or years, reliably surviving infrastructure failures, process crashes, restarts, and even deployments of new worker code. If a Worker process executing a Workflow crashes, the Temporal Platform ensures it resumes seamlessly on another available Worker, preserving the exact state and guaranteeing progress without data loss. This provides effective “exactly-once” execution semantics for Activities, going far beyond simple state persistence between predefined steps.
Cloud-Agnostic and Multi-Platform
One of Temporal’s strongest advantages is its platform independence. It is not locked into any specific cloud provider. You can run the open-source Temporal Server on your own infrastructure (Kubernetes, VMs, bare metal) across any cloud or on-premises, or use the fully managed Temporal Cloud service.
This platform-agnostic nature is crucial for organizations adopting multi-cloud or hybrid-cloud strategies. For instance, ZoomInfo adopted Temporal during a migration initiative partly because they needed a consistent orchestration layer that wasn’t tied to AWS Step Functions or Google Cloud Workflows, providing flexibility as their infrastructure evolved.
Flexibility in Triggers, Timers, and Scheduling
Temporal Workflows aren’t tied to AWS event sources. You can start them through SDK calls, HTTP requests, Temporal’s built-in scheduler, or signals from other processes. Temporal offers powerful and precise timer capabilities (down to the second) and supports dynamic scheduling logic defined in code, allowing far more flexibility than the cron-like scheduling or event triggers available for Step Functions.
Superior Scalability and Cost-Effectiveness at Scale
Temporal is designed for high scalability. Workflows execute on fleets of Worker processes that you control and can scale horizontally based on load. Companies like Datadog, Snap, and Netflix run massive, mission-critical workloads on Temporal.
While Temporal Cloud offers usage-based pricing, self-hosting the open-source Temporal Server means your primary cost is the underlying infrastructure (compute, storage, networking), though it’s important to also factor in the human operational cost required to maintain and scale the platform.
Open Source with Growing Community
Temporal is an open-source project (MIT license) governed by a dedicated company but driven by a vibrant and growing community. You benefit from transparency, aren’t locked into a single vendor’s roadmap, can run it for free (self-hosted), and can even contribute to the platform and its ecosystem (SDKs, tools).
Real-World Examples: Moving from Step Functions to Temporal
Case Study: High-Scale SaaS Platform
A SaaS company initially used AWS Step Functions for orchestrating background jobs tied to user actions. As their platform grew, they encountered significant challenges:
- Complex Waiting Logic: Some workflows needed to wait indefinitely (hours or days) for external events or specific times.
- Scheduling Precision: They required sub-second scheduling precision, whereas Step Functions event rules offer minute-level granularity at best.
- Prohibitive Costs: Step Functions costs were escalating rapidly with increased usage and workflow complexity.
- Execution Limits: Express workflow duration limits and Standard workflow history limits were becoming problematic. After evaluating alternatives, they migrated to a self-hosted Temporal deployment on Amazon EKS. The results were compelling:
- ~80% Cost Reduction: Significant savings compared to their projected Step Functions costs at scale.
- Enhanced Reliability: Maintained or improved end-to-end reliability.
- Infrastructure Control: Gained full control over their orchestration platform.
- Removed Limits: Eliminated constraints on execution duration, history size, and scheduling precision.
- Improved Developer Productivity: Consolidated fragmented Lambda logic into clearer, more maintainable Workflow code using familiar programming languages.
Case Study: ZoomInfo
ZoomInfo, a leading B2B intelligence platform, needed a robust solution to manage dynamic marketing audience workflows that continuously update based on complex data changes. Their existing homegrown system struggled to scale and maintain reliability.
During a strategic initiative involving multi-cloud considerations, they evaluated various orchestration options, including native cloud services like AWS Step Functions and Google Cloud Workflows. They ultimately selected Temporal Cloud due to its:
- Cloud-Agnostic Architecture: Providing flexibility beyond a single cloud provider.
- Superior Developer Experience: Enabling engineers to write complex logic in their preferred languages.
- Workflow-as-Code Paradigm: Aligning better with modern development practices. ZoomInfo’s engineers highlighted that defining intricate Workflows in the same languages used for application development was a “no-brainer” for productivity. Furthermore, Temporal’s built-in observability features drastically reduced the time spent diagnosing Workflow issues. Incidents that previously took days of log combing with their old system were virtually eliminated after adopting Temporal.
When to Choose Temporal Over Step Functions
Consider Temporal (either Temporal Cloud or self-hosted) when:
- Starting new projects requiring resilience: For greenfield applications where durable execution, fault tolerance, and long-running capabilities are critical from the outset, Temporal provides a fundamentally more robust foundation than Step Functions.
- You’re hitting complexity walls: When your workflow logic becomes too complex to manage effectively in ASL (JSON/YAML) and requires extensive use of Lambda functions to work around limitations, Temporal’s code-first approach offers a much cleaner and more powerful solution.
- You need robust testability: Temporal Workflows, being code, can be unit-tested using standard frameworks (like JUnit, Jest, PyTest, etc.). You can mock activities and test complex logic paths, enabling comprehensive end-to-end testing that is significantly harder to achieve with Step Functions’ declarative model.
- You need multi-cloud, hybrid-cloud, or on-prem capabilities: If your architecture spans multiple clouds or includes on-premises components that need orchestration, Temporal’s platform independence is essential.
- Cost concerns at scale: If Step Functions’ per-state-transition or per-execution pricing model is becoming expensive due to high volume, complex workflows, or long wait times, Temporal can offer substantial cost savings and predictability.
- You need advanced Workflow features: Temporal provides dynamic parallelization beyond static Map state limits, sophisticated timers/scheduling, built-in human-in-the-loop patterns, complex compensation logic (Sagas), and seamless Workflow versioning and upgrades.
- Developer productivity and experience matter: If your development team prefers writing code over configuring JSON/YAML, values using familiar tools and testing practices, and is frustrated by the limitations and debugging challenges of Step Functions, Temporal offers a significantly better developer experience.
Best Practices for Implementing Temporal
Whether migrating from Step Functions or starting fresh with Temporal, these best practices will help ensure success:
Design for Idempotency
Ensure your Workflow Activities (the units of work) are idempotent — meaning they can be safely executed multiple times with the same input, producing the same result or side effect. This is crucial because Temporal guarantees at-least-once execution for Activities during retries.
Leverage Built-in Error Handling and Retries
Use standard try/catch blocks within your Workflow code to handle Activity failures. Configure appropriate Retry Policies (backoff intervals, maximum attempts) on Activities to automatically handle transient issues. Implement compensation logic (e.g., using the saga pattern) within your Workflow for business-level rollbacks if needed.
Distinguish Workflow vs. Activity Code
Keep Workflow definition code deterministic and free of external I/O or non-deterministic calls. All side effects (API calls, database interactions, etc.) should happen within Activities. Optimize by performing CPU-intensive work or frequent external calls within Activities, while keeping the core orchestration logic lean within the Workflow definition.
Optimize for Parallelism
Use Temporal’s capabilities (e.g., Promise.all in TypeScript, Async.invoke in Java, Goroutines in Go) to execute independent Activities in parallel where possible, reducing overall Workflow latency.
Invest in Observability
Use Temporal’s Web UI and visibility APIs to monitor Workflow executions, inspect history, and query Workflow states. Integrate with your existing monitoring stack (e.g., Prometheus, Grafana, Datadog) by emitting metrics from your Workers. Effective observability is key to understanding and debugging Workflows in production.
Plan for Workflow Versioning
As your business logic evolves, you’ll need to update Workflow definitions. Temporal provides strategies for deploying new versions and handling long-running instances started on older versions (e.g., using patched API or versioning signals). Test versioning strategies thoroughly.
Get Started with Temporal Cloud
If you’re feeling the limitations of AWS Step Functions, Temporal offers a more powerful, flexible, and developer-centric alternative. Whether you’re building complex microservice sagas, long-running business processes, critical data pipelines, or high-volume event processing systems, Temporal provides the reliability, scalability, and developer experience modern applications demand.
Ready to explore Temporal? Check out our documentation. You can build your first Workflow in minutes with language-specific tutorials and see how it compares to AWS Step Functions for your specific use case.
Get started with Temporal Cloud today and get $1,000 in free credits. Join thousands of developers choosing Temporal as the foundation for resilient, scalable, and maintainable applications.
Frequently Asked Questions
Can Temporal integrate with AWS services I already use?
Absolutely. Although Temporal itself is platform-agnostic, your Workflow Activities (written in languages like Python, Java, Go, etc.) can use the standard AWS SDKs to interact with any AWS service (S3, DynamoDB, SQS, Lambda, etc.) just like any other application code. The integration logic lives within your Activity code, giving you full control, rather than relying on predefined Step Functions service integrations.
How hard is it to migrate from Step Functions to Temporal?
It involves rewriting the workflow logic from ASL (JSON/YAML) into your chosen programming language using Temporal’s SDK. While it’s a manual code translation, your existing Step Functions definition serves as a blueprint for the control flow (sequences, parallel branches, choices). Logic previously embedded in Lambda functions can often be reused or adapted within Temporal Activities. The effort depends on the complexity but typically results in more maintainable and testable code.
How do the costs compare?
Step Functions pricing is primarily based on state transitions (Standard — e.g., $0.025 per 1,000 transitions) or duration/requests (Express), which generally grows with usage and can become costly at high scale or with complex/long-running workflows.
Temporal Cloud offers usage-based pricing (per action, storage, support) starting around $25 per million actions, with volume discounts available that can make the per-action cost decrease significantly as usage scales. In contrast, self-hosted Temporal’s cost is primarily your infrastructure cost, plus the operational overhead needed to maintain, upgrade, and scale the Temporal cluster itself. For many use cases involving complexity, long durations, or high throughput, Temporal (Cloud or self-hosted) often provides a lower total cost of ownership than Step Functions.