---
title: "Kafka vs Temporal: When to Use Each, and When to Use Both"
description: "Kafka is a durable event stream; Temporal is a workflow orchestrator. Learn which solves your problem and why many teams use them together."
---

# Kafka vs Temporal: When to Use Each, and When to Use Both

**Type:** [How it works](https://yomu.fyi/visual-explainers/category/how-it-works.md)

![Diagram showing Kafka and Temporal side by side with their core functions, then a unified flow showing how they work together in a production system.](https://yomufyi.s3.us-east-1.amazonaws.com/visual-explainers/a1ec43d9-de02-4d2a-9b13-69a12e1aa444/image-3da53681-b5d7-4cdf-88c2-44c795873e1d.webp)

> Kafka is a durable event stream; Temporal is a workflow orchestrator. Learn which solves your problem and why many teams use them together.

## Explanation

Kafka excels at capturing, storing, and replaying sequences of events across distributed systems with durable, ordered delivery. Temporal orchestrates long-running, stateful workflows—retries, timeouts, saga compensation, and distributed coordination—without rebuilding those primitives yourself. They solve different problems: Kafka moves data reliably; Temporal manages work reliably. Real-world systems often pair them: Kafka ingests events, Temporal orchestrates responses, and together they handle both event durability and workflow resilience at scale.

**Tags:** [Data Engineering](https://yomu.fyi/topic/data-engineering.md), [Data Pipelines](https://yomu.fyi/topic/data-pipelines.md), [Distributed Systems](https://yomu.fyi/topic/distributed-systems.md), [Kafka](https://yomu.fyi/topic/kafka.md), [Temporal](https://yomu.fyi/topic/temporal.md)

## Related reading

### [How AI-Powered Attacks Led Salesforce to Reinvent Hyperscale DDoS Defense](https://yomu.fyi/post/how-ai-powered-attacks-led-salesforce-to-reinvent-hyperscale-ddos-defe.md)
- Company: [Salesforce](https://yomu.fyi/company/salesforce.md)
- Author: Scott Nyberg
- Published: Aug 28, 2026
- Type: Explainer

Salesforce built DREAM (DDoS Response and Mitigation) to defend its distributed cloud against hyperscale, machine-speed DDoS attacks, with a stated goal of responding in seconds while protecting more than 4.5 million domains and 3 million customer orgs at 99.99% availability. The platform targets rapidly evolving application-layer attacks in a shared, multi-tenant environment by combining AI-driven inference with distributed orchestration for telemetry, policy deployment, notifications, retries, and fault-tolerant execution across regions. The team selected Temporal rather than rebuilding distributed-systems primitives, and used AI coding assistants to migrate about 100,000 lines at roughly ten times prior velocity; more than 90% of the rewrite was AI-assisted, with human reviews and test coverage above 95%. Production lessons moved bulk data outside orchestration and bounded workflow history, while DREAM reduced time to first mitigation fivefold.


### [A Tale of Two Flink Autoscalers](https://yomu.fyi/post/a-tale-of-two-flink-autoscalers.md)
- Company: [Netflix](https://yomu.fyi/company/netflix.md)
- Author: Netflix Technology Blog
- Published: Aug 21, 2026
- Type: Problem & solution

Netflix operates over 30,000 Apache Flink jobs across multiple AWS regions, ranging from managed pipelines to complex stateful DAGs. Its initial in-house autoscaler used external telemetry from Mantis and Atlas to scale TaskManagers as a single unit, cutting resource usage by 25–45% but failing on multi-operator stateful topologies. To address these limitations, Netflix adopted the Apache Flink Autoscaler library, which calculates True Processing Rate and per-vertex parallelism using internal metrics. The architecture uses Temporal workflows orchestrated within a Spring Boot application to evaluate jobs individually and prevent cross-job blast radiuses. Implementing the open-source autoscaler reduced annualized compute expenditures by 58% for client telemetry and logging, saving approximately $1.1 million.


### [Shopify powers observability for global-scale commerce with ClickHouse](https://yomu.fyi/post/shopify-powers-observability-for-global-scale-commerce-with-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 20, 2026
- Type: Problem & solution

Shopify rebuilt its observability infrastructure after metrics, logs, and traces became fragmented across vendors, with unpredictable costs and limited cross-signal analysis. It created Observe, a unified platform that runs metrics, logs, traces, profiles, and exceptions on self-hosted ClickHouse, treating them as structured, time-ordered, high-dimensional events and serving roughly 50 million events per second normally and 100 million at BFCM peak, or about 110 GB/s. Kafka buffers telemetry for large synchronous durable writes, while materialized views support flexible schemas, autocomplete, typed hot keys, and identifier-based correlation; the latter reportedly makes targeted searches 10x faster. Shopify reports a 16x query improvement out of the box, exceeding 30x at peak performance, with data queryable in under a minute, and says it would likely choose ClickHouse Cloud if starting again because managed operations could reduce the self-hosting burden.


### [How Stripe uses graph search and state machines to auto-remediate a global database fleet](https://yomu.fyi/post/how-stripe-uses-graph-search-and-state-machines-to-auto-remediate-a-gl.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Pragya Mehta
- Published: Jul 16, 2026
- Type: Problem & solution

Stripe describes how its Document Database fleet previously handled degraded MongoDB shards with single-step, hard-coded plugins that struggled with combined failures, intermediate states, and more than 40 shard layouts. The replacement models shard topology and health as a directed graph, with infrastructure operations as edges and safety invariants defining valid states. A simulator uses breadth-first search to find minimum-operation recovery paths, while an abstraction over Temporal contexts lets the same remediation logic run in memory without real infrastructure calls; later, the system also uses Dijkstra's algorithm. The system reduced pager volume by 30%—about 200 pages annually—reclaimed 12 days of fleet health each year, and handled a new layout with zero code changes, according to the post.


### [What we’ve learned building cloud agents](https://yomu.fyi/post/what-we-ve-learned-building-cloud-agents.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Josh Ma
- Published: Jun 2, 2026
- Type: Explainer

Cloud agents have evolved from local-agent extensions into systems running on dedicated virtual machines, with isolated environments, dependencies, network access, parallel execution, and unattended long-running work. That shift makes the development environment a product: reliable execution increasingly depends on rebuilding tools, VM hibernation and resumption, image checkpointing and forking, integrations, and controlled access to secrets and networks. After a fragile work-stealing design produced roughly one 9 of reliability, the team migrated the agent loop to Temporal, whose retries and scheduling across failures helped exceed two 9s; Temporal now handles more than 50 million actions daily across over 7 million workflows. Decoupling agent-loop, machine, and conversation state enables independent pod lifecycles, asynchronous subagents, and retry-aware streaming, while shorter workflows simplify upgrades. The next goal is self-healing environments that can report and resolve missing secrets, blocked network access, or other obstacles.