To scale n8n with queue mode, run a main instance, a shared production database, Redis, and one or more worker processes. Set EXECUTIONS_MODE=queue and the same N8N_ENCRYPTION_KEY on every participating n8n process, point them all at the same Redis and database, then start workers with n8n worker. Add workers to increase execution capacity; add webhook processors only when incoming webhook traffic—not workflow execution—is the bottleneck.
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What queue mode changes
In a single-process deployment, the process that accepts webhook requests and handles scheduled work also runs workflows. Queue mode separates coordination from execution. The main process handles timers and webhook calls, creates an execution, and places its ID in Redis. A worker picks up the job, reads workflow data from the shared database, runs the workflow, writes results, and reports completion through Redis. The n8n documentation describes queue mode as providing “the best scalability.”
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The components have distinct roles:
- Main: coordinates the instance, handles timers and requests, and enqueues executions.
- Redis: holds the queue used to pass jobs between the coordinating process and workers.
- Workers: execute queued workflows. Increasing their number can increase parallel throughput, subject to database and other resource limits.
- Shared database: stores n8n state and workflow data that participating processes need to access.
- Optional webhook processors: accept production webhook traffic separately from the main process. They help with ingress pressure; they do not execute workflows in place of workers.
A typical path is: client or webhook → load balancer → webhook processor or main → Redis queue → worker pool → shared database and storage → completion notification. Queue mode is therefore a distributed deployment, not simply a switch that makes one n8n process faster. Each component must be reachable and configured consistently.
Plan the deployment before switching modes
Choose shared state services
Use a production database that every n8n process can reach, plus a Redis instance reachable by the main process, workers, and any webhook processors. The official n8n queue-mode guidance does not recommend SQLite for queue-mode use. A database or Redis outage can interrupt coordination or execution, so their availability and connection capacity are part of the scaling plan.
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Decide where binary data will live
Filesystem-only binary storage is unsuitable when independently scaled instances need to access the same files: a file written on one machine may not exist on another. Use shared storage, such as S3 or Azure, for persisted binary data. Keep the storage configuration and access consistent across the processes that need those files. External storage features and supported configurations can vary by n8n edition; confirm the applicable n8n documentation and license for your deployment.
Separate execution capacity from webhook ingress
Workers address the number of queued executions that can run concurrently. Webhook processors address how much incoming webhook traffic can be accepted without making the main process carry all of that ingress load. If workflows are accumulating in Redis, investigate worker capacity and downstream constraints. If incoming requests are the bottleneck, consider webhook processors behind a load balancer instead of adding workers alone.
Set up queue mode step by step
1. Prepare Redis, the database, and shared storage
Provision Redis and a production database first. Verify that the main process and every planned worker can reach both services. If workflows persist binary data, configure a shared storage strategy before distributing execution across machines. SQLite is not the recommended database choice for queue mode.
2. Set one encryption key for every process
Set N8N_ENCRYPTION_KEY on the main instance, workers, and webhook processors. Use the same key value everywhere; it is how those processes decrypt credentials stored in the shared database. If the values differ, a worker may not be able to use credentials needed by a workflow. Protect the key as a secret and distribute it through your deployment’s secret-management mechanism rather than maintaining different values per container.
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3. Enable queue mode consistently
Set EXECUTIONS_MODE=queue on the main process and workers. Webhook processors also need this setting. A process omitted from queue-mode configuration can behave differently from the rest of the deployment, so verify the effective environment for every role rather than only checking the main instance.
4. Configure Redis connectivity
Set QUEUE_BULL_REDIS_HOST and QUEUE_BULL_REDIS_PORT for the Redis service. The documented defaults are localhost and 6379; in a multi-container or multi-host deployment, localhost usually refers to the individual process’s own environment, not the shared Redis service, so configure the reachable service address. Configure Redis username, password, TLS, or cluster-node settings as required by your Redis deployment. Those values must work consistently for all queue participants.
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Start the main instance with the shared database, Redis, key, and queue-mode environment. Start each worker with the same relevant environment and the worker command. The documented CLI form is:
./packages/cli/bin/n8n workerFor a Docker deployment, use the n8n image with the worker command and pass the same shared environment that the worker needs. Ensure the worker can reach the database, Redis, and any shared binary-data storage. Do not treat a running worker as proof that it can execute a real workflow: verify that it can access credentials and required files as well.
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6. Set concurrency with database capacity in mind
Concurrency controls how many executions a worker can handle at the same time. The documented default is 10. You can set a worker’s value explicitly with a command such as:
n8n worker --concurrency=5n8n recommends a concurrency of 5 or higher per worker. That is a recommendation, not a universal optimum: higher concurrency increases simultaneous work and can also increase pressure on the database and services called by workflows. Many workers with very low concurrency can exhaust database connection pools. Choose a worker count and concurrency together, then watch connection-pool pressure and execution behavior as load changes. N8N_CONCURRENCY_PRODUCTION_LIMIT can impose a production limit across queue-mode workers.
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7. Add webhook processors only when ingress needs them
When the main process is overloaded by incoming production webhooks, run optional webhook processor processes behind a load balancer. Keep the main process out of that load-balancer pool when possible so the editor and API remain responsive. If production webhooks should never execute on the main process, set N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true. Workers are still required to execute the jobs placed in the queue.
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8. Account for large webhook responses
Queue-mode webhook responses pass through Redis. The documented default for N8N_WEBHOOK_RESPONSE_RELAY_SIZE_MAX is 64 MiB. Redis may hold several copies of an in-flight response, so the n8n guide recommends budgeting about 1.5 times the configured limit per in-flight response. This is a memory-planning allowance, not a claim that every response occupies exactly that amount.
For larger response bodies, workers can offload them by enabling N8N_WEBHOOK_RESPONSE_RELAY_OFFLOAD_ENABLED=true. This option requires every main and webhook instance to run n8n 2.34.0 or later, and every relevant instance must be able to read the selected storage. If versions or storage access differ, do not assume offloading will work across the deployment.
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How many workers should you run?
There is no fixed worker count that suits every n8n installation. Size the pool according to execution demand and the capacity of the shared services, then adjust based on observed behavior. Start with a concurrency setting that respects n8n’s recommendation of 5 or higher per worker, and treat the documented default of 10 as a starting default—not a promise of optimal performance. Keep the production concurrency limit in view if you use N8N_CONCURRENCY_PRODUCTION_LIMIT.
| What is constrained? | What to change or investigate | What else can limit the result |
|---|---|---|
| Queued workflows wait for execution capacity | Add worker capacity or adjust worker concurrency deliberately. | Database connection pools, workflow resource demands, Redis, and downstream services. |
| Incoming webhook traffic overwhelms the main process | Consider webhook processors behind a load balancer; keep main out of the pool where possible. | Webhook processor capacity and Redis availability; workers still need to execute queued jobs. |
| Database connections are under pressure | Review the number of workers and their concurrency, and the database connection-pool capacity. | Many low-concurrency workers can also exhaust pools; adding processes is not automatically safer. |
| Large webhook responses cause memory pressure | Review the response relay limit, Redis memory budget, and whether supported response offloading is configured. | Offloading requires n8n 2.34.0 or later on all main and webhook instances, plus shared readable storage. |
Increase capacity in controlled increments and compare queue depth, completion behavior, database connection pressure, and resource use before and after each change. If a queue is growing while workers are healthy, adding workers may help only if the database and workflow dependencies can absorb the additional parallel work. If executions are completing but webhook requests are slow to arrive, scaling the worker pool alone does not address ingress.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When webhook processors and multiple main instances make sense
Webhook processors
Webhook processors are an optional ingress tier. Put them behind a load balancer when webhook traffic needs to be distributed separately from the main process. They share the queue-mode requirements, including the encryption key, Redis connectivity, and queue execution mode. Keep the main process available for the editor and API rather than routing production webhook traffic to it when the deployment design allows.
Multiple main instances
Multi-main queue mode is documented as a self-hosted Enterprise feature. The documented enablement setting is N8N_MULTI_MAIN_SETUP_ENABLED=true; leader-key time-to-live and check-interval settings are also available. Treat this as a separate high-availability design, not as a prerequisite for adding ordinary queue workers. Confirm the current Enterprise requirements and configuration details with n8n before deploying multiple main instances.
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Monitor and troubleshoot a queue-mode deployment
Monitor the components and handoffs, not just whether the n8n main process is running. A worker can be alive while lacking access to a credential, database, Redis, or binary file required by an execution.
- Redis reachability: check that main, workers, and webhook processors can connect using the configured host, port, and any required authentication, TLS, or cluster settings.
- Queue depth and worker health: a growing backlog alongside unavailable workers points to an execution-capacity or worker-connectivity problem; a growing backlog with workers active calls for checking concurrency, database pressure, and workflow dependencies.
- Database connection-pool pressure: look for connection exhaustion when increasing workers or concurrency. Revisit the balance between worker processes, concurrency, and database capacity.
- Execution failures: verify that every worker uses the same encryption key as main and has access to shared state and storage. Credential decryption or missing binary files can disrupt work even when the queue itself is functioning.
- Webhook response-size errors or memory pressure: compare response sizes with
N8N_WEBHOOK_RESPONSE_RELAY_SIZE_MAXand plan Redis memory for multiple in-flight copies. Consider the documented offload option only when its version and shared-storage requirements are met. - Webhook latency: distinguish slow acceptance of incoming requests from slow workflow completion. If ingress is the issue, consider webhook processors; if queued work is waiting, investigate workers and execution dependencies.
When using external storage for webhook response offloading, upgrade main, workers, and runners together. Mixed versions can create protocol incompatibilities. After configuration changes, validate a representative workflow that uses credentials, binary data, and a webhook response if those features are part of the deployment.
Quick Recap
Queue mode compared with a single-process deployment
| Deployment choice | Execution throughput | Webhook ingress | State and storage needs | Operational trade-off |
|---|---|---|---|---|
| Single process | Execution and request handling share a process; no separate worker pool. | Main process receives requests and handles production work. | Does not gain queue mode’s distributed worker design. | Simpler topology, but less separation between ingress and workflow execution. |
| Queue mode with workers | Can increase parallel execution capacity by adding workers and setting concurrency, within database and dependency limits. | Main can accept webhook calls and enqueue work. | Shared production database and Redis; shared storage is needed for persisted binary data across instances. | More components to configure, monitor, and keep consistent. |
| Queue mode with webhook processors | Worker pool still provides execution capacity. | Optional processors distribute inbound webhook load behind a load balancer. | Processors share queue-mode configuration and access requirements. | Separates ingress from main, with added process and load-balancer operations. |
| Queue mode with multiple main instances | Availability design in addition to the worker pool; not a substitute for sizing workers. | Depends on the overall deployment design. | Multi-main queue mode is a self-hosted Enterprise feature. | Requires Enterprise configuration and additional high-availability operations. |
Deployment checklist
- Every participating process can reach the same production database and Redis.
N8N_ENCRYPTION_KEYmatches on main, workers, and webhook processors.EXECUTIONS_MODE=queueis set on main and workers, and on webhook processors if used.- Redis host and port are configured for the actual shared service, with required security and cluster settings.
- Workers start with the documented worker command and a concurrency value chosen with database capacity in mind.
- Persisted binary data uses storage accessible to independently scaled instances rather than local-only files.
- Webhook response relay limits and Redis memory budgets account for the response sizes and in-flight copies expected.
- If response offloading is enabled, all main and webhook instances meet the version requirement and can read the selected storage.
- Queue depth, worker health, database connections, Redis, execution failures, and response-size errors are monitored.
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