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overview python dependencies go overview go dependencies java overview java dependencies net ruby php local functions development function triggers tutorials create a function that returns bigquery results create a function that returns spanner results integrate with cloud databases codelabs build and test build sources to containers build functions to containers local testing serve http requests deploy services deploy container images continuous deployment from git deploy from source code deploy from compose use the cloud run remote mcp server deploy functions serve web traffic mapping custom domains serving static assets with cdn serving traffic from multiple regions automate failover with service health enable session affinity frontend proxying using nginx manage services view copy or delete services view or delete revisions traffic migration gradual rollouts rollbacks configure services overview capacity memory limits cpu limits gpu gpu configuration gpu performance best practices request timeout maximum concurrent requests about maximum concurrent requests per instance configure maximum concurrent requests billing optimize service configurations with recommender environment container port and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes cifs smb ephemeral disk execution environment sandboxes container health checks http 2 requests secrets service identity scaling about instance autoscaling for services maximum instances about maximum instances for services configure maximum instances minimum instances configure custom scaling controls manual scaling metadata description labels tags source deploy configurations supported language runtimes and base images configure automatic base image updates build environment variables build service account build worker pools invoke and trigger services invoke with https requests host a webhook target stream with websockets overview build a websocket chat service tutorial invoke asynchronously invoke services on a schedule create a workflow invoke services as part of a workflow connect a series of services from cloud functions and cloud run tutorial execute asynchronous tasks call a service from a pub sub push subscription trigger service from pub sub integrate image processing into pub sub sample tutorial trigger from events create triggers with eventarc pub sub triggers create pub sub eventarc triggers trigger functions from pub sub using eventarc trigger functions from routed log entries cloud storage triggers create triggers with cloud storage trigger services from cloud storage using eventarc trigger functions from cloud storage using eventarc firestore triggers create triggers with firestore trigger functions from events in a firestore database connect with other services using grpc best practices general development tips for services cost optimization optimize java services optimize python services optimize node js services load testing best practices understand zonal redundancy functions best practices overview configure event driven function retries execute job tasks to completion create jobs execute jobs execute jobs execute scheduled jobs execute jobs from workflows configure jobs container entrypoint cpu limits memory limits gpu gpu configuration gpu best practices environment variables container health checks volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk labels maximum retries parallelism secrets service identity task timeout tags manage jobs view or delete jobs view or stop job executions best practices jobs retries and checkpoints cost optimization perform continuous background work deploy worker pools deploy worker pools deploy worker pools from source code manage worker pools view or delete worker pools view or delete worker pool revisions instance splits and rollbacks configure worker pools capacity memory limits cpu limits gpu gpu configuration gpu best practices environment container and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk container health checks secrets service identity instance count metadata description labels scale based on external metrics autoscale worker pools with external metrics kafka autoscaler host github runners with worker pools autoscale worker pools based on prometheus metrics autoscale worker pools with pub sub pull subscriptions automate scaling with workflows cost optimization configure networking best practices for cloud run networking configure private networking send traffic to vpc network overview direct vpc register private ips for worker pools using cloud dns dual stack ipv4 and ipv6 migrate standard vpc connector to direct vpc vpc connectors send traffic to shared vpc network overview direct vpc migrate shared vpc connector to direct vpc connectors in service projects connectors in host project static outbound ip address network security restrict endpoint ingress services use vpc service controls vpc sc cloud service mesh secure security design overview authenticate requests overview allow public access custom audiences authenticate developers service to service authenticate users end user authentication tutorial secure your resources access control with iam configure iap for cloud run introduction to service identity protect services with cloud armor use binary authorization use cloud run threat detection use customer managed encryption keys manage custom constraints for projects view software supply chain security insights secure cloud run services tutorial multi tenant platforms running untrusted code monitor and log monitoring and logging overview view built in metrics write prometheus metrics write opentelemetry metrics log and view logs audit logging error reporting use distributed tracing for services run ai solutions overview explore resources ai agents overview build and deploy a2a agents overview deploy a2a agents build and deploy adk agents build and deploy n8n agents mcp servers overview build and deploy a remote mcp server tools code execution browser automation inference with gpus overview services run llm inference on cloud run gpus with ollama run agents with gemma 4 models on cloud run run opencv on cloud run with gpu acceleration run llm inference on cloud run gpus with hugging face transformers js jobs fine tune llms using gpus with cloud run jobs run batch inference using gpus with cloud run jobs gpu accelerated video transcoding with ffmpeg ai assisted development and vibe coding introduction to cloud run for ai assisted developers cookbook migrate an existing web service from app engine from cloud run functions 1st gen from aws lambda from heroku from cloud foundry migration overview choose an oci compliant strategy migrate to oci containers migrate configuration sample migration spring music from vmware tanzu from a vm using migrate to containers from kubernetes to gke troubleshoot introduction troubleshoot errors local troubleshooting tutorial known issues samples all cloud run code samples all cloud run functions code samples code samples for all products ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools home documentation application hosting cloud run guides send feedback load testing best practices stay organized with collections save and categorize content based on your preferences this page provides best practices for load testing your cloud run service to determine whether it scales successfully during production use and to find any bottlenecks that prevent it from scaling tests to run before load testing identify and address concurrency problems in a development or small test environment before proceeding to load testing measure container concurrency before performing a load test and make sure that your cloud run service starts up reliably focus your container tests on small incremental counts in manually scaled runs you can approximate manual scaling in cloud run by setting maximum instances to the value that you wish to scale to if you have only recently built your container image or recently changed the container image test that independently before performing a load test you should also check other kinds of performance problems such as excessive latency and cpu utilization before running a large scale load test use max instances appropriately cloud run enforces a maximum instances to limit the scaling of a service the default maximum number of instances is 100 if you expect your load test to exceed this default make sure you work with your account team at google and set a new maximum if you do not yet have a relationship with an account team contact google cloud sales the maximum number of instances that you can select depends on your cpu limits and memory limits as well as the region you are deploying to these limits are managed by a quota limit and can be increased by making a quota limit increase request load test in the region europe west1 the google cloud region europe west1 offers a high quota limit so google recommends load testing in europe west1 coordinate with your account team and submit a support case with details of the time and scale of the test if you expect to approach quota limits test an appropriate cpu utilization and service initialization profile in an ideal scenario you deploy a test version of your service to cloud run and load test it directly however in some cases you might be unable to deploy a test version of your service for example your cloud run service might be part of a complex ecosystem that is hard to replicate in a test environment for these cases you can approximate the performance of your service by simulating it with a simpler service that has comparable cpu usage and comparable initialization times initialization time is particularly important for rapid scaling keep in mind that testing with something too simple is also problematic for example avoid testing with a simple hello world service that returns received requests without any processing use a test harness to generate loads you can generate test loads causing a controlled spike in traffic using a test harness such as jmeter you can use the number of jmeter thread groups and delay between requests in the jmeter test to increase the load you can also send simple http requests or you can record a browser session with jmeter cloud run lets you test your service without internet access by using developer authentication this allows access from a test harness like jmeter running on a compute engine virtual machine attached to a virtual private cloud vpc associated with the project do not generate load from tools where the rate and concurrency cannot be controlled pub sub is a poor choice of tool to generate load because you cannot control the rate of the traffic and number of clients if you do not know the rate and concurrency then you will not know what you are testing use detailed log analysis using exported logs you need a second by second analysis of events to understand your cloud run service s response to rapid traffic spikes log analysis is needed to do this because the granularity of monitoring data is not sufficiently fine grained log analysis also allows you to investigate the reasons for requests with high latency when you write logs you can get better logging performance by writing directly to stdout instead of using a cloud logging client library to set up a log export before starting the test create a log sink with the destination bigquery and an inclusion filter such as resource type cloud_run_revision resource labels service_name your app avoid spurious cold starts to minimize cold starts experienced by users set the minimum number of instances to at least 1 ensure that your service scales out linearly repeat the test at different loads to make sure that your cloud run service scales out linearly with load and does not reach a limiting bottleneck at a load less than you expect in production analyze and visualize the results in colaboratory use the summary monitoring charts to get a high level understanding of results to supplement the detailed log analysis using exported logs the monitoring charts can help you discover how quickly to the nearest second are new instances created and initialized how evenly are requests distributed across different instances how quickly can the latency at different percentiles be drawn down to a steady state value you can use the google cloud console user interface for bigquery to introspect the exported log schema and preview results run the queries and plot results using colab which has ready integration with bigquery pandas and matplotlab colab also integrates easily with rich data visualization tools like seaborn find bottlenecks load tests can help you discover the existence of both inefficient code and scaling bottlenecks inefficient code leads to higher costs as it needs to handle more traffic but does not necessarily prevent scaling for example a dependency on a database translation with table level locking can be a bottleneck that will prevent the cloud run service from scaling because only one transaction can execute at a time check performance as experienced by the client you can query logs captured by jmeter where the logs include latencies measured at the client however because server testing tools like jmeter are not the same as a browser or mobile client you may also want to run a test with a browser based framework such as selenium webdriver or a mobile client testing framework be careful of excessive maximum latencies due to tls connection initialization that may skew results with outliers summary of best practices perform a load test to determine whether migrating to cloud run is the right choice and that your service can scale to the maximum expected traffic run the test with a harness like jmeter export the logs to bigquery for detailed analysis what s next cloud run development tips use cloud trace with cloud run send feedback except as otherwise noted the content of this page is licensed under the creative commons attribution 4 0 license and code samples are licensed under the apache 2 0 license for details see the google developers site policies java is a registered trademark of oracle and or its affiliates last updated 2026 07 10 utc need to tell us more easy to understand easytounderstand thumb up solved my problem solvedmyproblem thumb up other otherup thumb up hard to understand hardtounderstand thumb down incorrect information or sample code 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