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Should you keep a microservice always on or let it scale to zero? Scale to zero can cut idle resource costs when traffic is intermittent, but a request that arrives after the service has gone idle may wait while a new execution environment starts. Keeping some capacity warm can reduce that delay, at the cost of paying for capacity that may sit unused. The right choice depends on your latency target, traffic, startup work, and the specific provider’s billing and scaling controls.
What is a cold start?
A cold start is the work required to prepare a new execution environment or container to handle a request. Depending on the platform and application, that can include provisioning runtime capacity, starting the process, loading code and dependencies, and initializing connections or other resources.
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That work matters most when a request arrives after the service has scaled to zero, or when incoming demand exceeds the environments already ready to serve it. A warm environment avoids some initialization for that request, but does not guarantee low latency: requests can still be affected by application work, network conditions, contention, or demand beyond ready capacity.
Scale to zero versus keeping capacity warm
| Approach | What it does | Main benefit | Main trade-off |
|---|---|---|---|
| Scale to zero | Allows the service to stop running instances or environments when there is no demand. | Can reduce charges for idle capacity. | A request after idle time may wait for provisioning and initialization. |
| Minimum or provisioned capacity | Keeps a configured amount of runtime capacity ready, using a provider-specific control. | Can reduce initialization-related delays for requests served by that capacity. | Ready capacity can incur charges even when it is not busy; excess demand may still require scaling. |
“Always on” is shorthand, not one universal setting. Providers differ in what remains ready, how capacity scales beyond that amount, and how idle and active time are billed. Check the behavior of the exact plan and billing mode you use rather than assuming that warm capacity is free or that every service can be kept warm in the same way.
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How the controls differ by platform
Google Cloud Run
Cloud Run normally scales instances in response to incoming load. You can configure minimum instances to keep some service capacity available and reduce slow container starts. Google describes this as a way to “avoid slow container start times and reduce service latency,” while noting that minimum instances incur charges. The exact billing effect depends in part on whether the service uses request-based or instance-based billing, so there is no single idle price that applies to every Cloud Run service. See Google’s minimum instances documentation, instance autoscaling guidance, and Cloud Run overview.
For function-style workloads on Cloud Run, Google recommends minimum instances when low latency matters and notes that load-time initialization affects startup latency. Keep startup work focused on what the first request needs; defer nonessential work where the application design allows it. See Google’s Functions best practices.
AWS Lambda
AWS Lambda’s reserved concurrency and provisioned concurrency solve different problems. Reserved concurrency sets a concurrency limit and reserves capacity for a function, but it does not pre-initialize execution environments. Provisioned concurrency pre-initializes environments to reduce cold-start latency, and AWS charges for it. AWS describes it as useful for reducing cold starts and designed to make functions available with double-digit-millisecond response times; that is design intent, not a latency SLA. See AWS’s provisioned concurrency documentation and Lambda concurrency documentation.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAWS says cold starts “typically occur in under 1% of invocations” and that their duration ranges from under 100 milliseconds to over 1 second. These are AWS’s general statements about Lambda, not a benchmark or guarantee for a particular function, and they should not be applied to Cloud Run, Azure Functions, or other providers. The same AWS lifecycle documentation explains the environment setup involved in an invocation: Understanding the Lambda execution environment lifecycle. AWS also notes that asynchronous workloads often have less need for provisioned concurrency than interactive workloads.
Microsoft Azure Functions
Azure Functions behavior depends on the hosting plan. The Consumption plan can scale to zero, with possible startup latency; Premium supports always-ready instances; and Dedicated can run continuously on prescribed instances. “Azure Functions has cold starts” or “Azure Functions is always on” is therefore too broad without naming the plan. Compare the relevant plan’s scaling and hosting behavior in Microsoft’s Azure Functions scaling and hosting documentation.
How to decide for your service
Choose based on measured workload behavior, not the label “serverless” or a general claim that one approach is faster or cheaper. Work through these questions:
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- What latency must you meet? Identify whether the requirement applies to average response time or to a tail percentile, and whether the first request after an idle period is especially important. A user-facing interactive request may make startup delay more consequential than a background task.
- How does demand arrive? Look at how often requests arrive, how long idle periods last, how bursty traffic is, and how much concurrency it creates. A small amount of warm capacity may help with predictable baseline demand, while bursts beyond it can still require new environments.
- How much work happens before the first request? Inspect startup and initialization, including dependency loading and connection setup. Reducing unnecessary load-time work can improve startup behavior without paying to keep more capacity ready.
- How much ready capacity is actually needed? Size minimum or provisioned capacity against observed traffic and the concurrency needed to meet the latency objective. More warm capacity costs more and does not remove every source of latency.
- What does this configuration cost while idle and active? Check the provider’s current billing rules for your region, plan, and billing mode. Compare total spend for the actual workload; do not infer an exact saving or premium from a setting name alone.
Measure the trade-off in production-like conditions
Compare latency percentiles and total spend under the traffic pattern and configuration you expect to run. Include requests after realistic idle periods, ordinary traffic, and bursts that may exceed the ready capacity. If you change initialization or warm capacity, measure again: a lower cold-start delay is useful only if it helps meet the service’s target enough to justify its ongoing cost.
For intermittent workloads that can tolerate startup delay, scale to zero is a reasonable starting point, provided the selected billing mode actually reduces idle charges. For interactive services where the first-request delay has a meaningful user impact, test a minimum or provisioned capacity setting and keep only the amount supported by observed need. Neither approach wins for every microservice.
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