If your Google Cloud VM only relays one already-encoded video stream, e2-standard-2 is a sensible starting point in Mumbai or Delhi. If it must capture, compose or encode video in real time, there is no single VM size you can safely choose without testing that exact workload.
The distinction matters more than the channel being live all day. A relay forwards prepared media; an encoder has to do video work continuously. Start by identifying which job the VM performs, then validate CPU headroom, stream continuity and the complete regional cost before you rely on it overnight.
First decide whether the VM relays or encodes
A 24/7 YouTube channel describes how long viewers can watch, not what the computer must do. Your VM may simply pass an already encoded stream to YouTube, or it may be part of a production pipeline that captures sources, adds overlays, decodes media and creates the outgoing video. Those jobs place very different demands on the machine.
In a relay or stream-copy setup, the media file or feed already has its video and audio encoded. The running process reads that stream and forwards it to YouTube without encoding every frame again. This still uses CPU, memory, disk and network capacity, but it avoids the central computational cost of software video encoding.
With software encoding, the machine takes source video and compresses it into an output stream in real time. Resolution, frame rate, codec, encoder preset, overlays, audio processing and other running processes all affect the work. A VM size that is adequate for forwarding one prepared feed should not be assumed to encode a particular format continuously.
YouTube automatically transcodes live streams into formats for viewers, but that happens downstream of your own upload. It does not remove the work required to capture, compose or encode your own feed before sending it. YouTube’s overview of live streaming describes the production workflow, while its encoder settings and bitrate guidance help you plan the outgoing signal. Neither page is a Compute Engine performance benchmark.
If you are using a VM to automate a prepared playlist, it is worth separating playback and relay from media preparation. The practical distinctions in this guide to automating prerecorded YouTube live streams on Linux can help clarify which work belongs on the always-on machine.
Why e2-standard-2 is a relay starting point
For one pre-encoded stream that the VM only relays, begin with Google Cloud’s e2-standard-2. Google lists it with 2 vCPUs and 8 GB of memory, and describes the E2 family as cost-optimised general-purpose machines. That gives a straightforward starting configuration without pretending that a machine specification alone proves a channel will run reliably.
The distinction between standard and shared-core E2 matters for a process intended to stay active. Google’s documentation lists e2-small as sustaining 0.5 vCPU and e2-medium as sustaining 1 vCPU, even though the guest sees two vCPUs. The visible vCPU count can therefore be misleading when comparing those options with a standard machine. For a continuously running relay, e2-standard-2 is the more conservative initial choice; a shared-core size may suit a lighter workload, but validate it rather than treating its guest CPU count as equivalent.
This recommendation is deliberately limited. It is a place to start a relay test, not a claim that e2-standard-2 is the best machine for every channel, or that it can encode every resolution and frame rate. If encoding takes place on the VM, measure the exact process first and select capacity from observed behaviour.
For a closer walkthrough of how the platform can fit into a prerecorded music channel, see this guide to using a Google Cloud VM for a 24/7 YouTube music radio stream. Use it to think through the workflow, not as proof that another channel’s machine size will suit your media and settings.
Choose Mumbai or Delhi based on the source and operators
Google lists E2 availability in both Mumbai (asia-south1) and Delhi (asia-south2). Either region is a valid place to investigate for an India-based channel. Availability does not establish which will give your particular upload source lower latency, more reliable connectivity or a better experience for people operating the channel.
Choose a candidate region based on where your prepared media or source feed originates, where you will manage the VM from, and the results you observe when testing the actual connection to YouTube. If your source is in one city and your operator is in another, there may be a trade-off between those paths. Do not infer an ideal location from the region name alone.
Check current availability and configuration in Google’s regions and zones documentation. Then use the same stream file, relay settings and upload path to compare candidate regions. A latency observation can help with the decision, but a short test is not proof of overnight continuity. You still need to run a sustained test and watch the stream itself.
Your region choice also changes the estimate. Google presents Compute Engine general-purpose machine prices by region and consumption model, and directs customers to its pricing calculator and general-purpose pricing information. Include the intended continuous runtime, boot disk, network use and any attached resources in the estimate. A VM’s displayed hourly price is not the complete bill, and there is no useful all-in amount without specifying those choices and the date of pricing.
Match the machine to the complete workload
Before selecting a size, write down what will run on the VM at the same time. Include the relay or encoder process, playlist or file handling, overlays, audio processing, monitoring, remote access and any maintenance scripts. A process that looks light in isolation may share capacity with several other tasks over a long session.
For a relay, begin with the e2-standard-2 recommendation and check CPU, memory, disk activity and network behaviour while the stream is active. If you run multiple channels or handle several feeds, do not extrapolate from a one-stream test. Each additional process can change the load and compete for resources.
For real-time encoding, identify the exact codec, resolution, frame rate and encoder settings before sizing. YouTube’s bitrate table varies by codec and output format. For example, its H.264 recommendations include 5–14 Mbps for 1080p at 30 fps, 6–17 Mbps for 1080p at 60 fps, and 3–8 Mbps for 720p at 30 fps. Those are encoder bitrate recommendations, not VM requirements or a promise that a particular network or instance can sustain them.
Also plan the upload path with room to spare. YouTube Help recommends leaving 20% of available upload bandwidth unused relative to the total stream bitrate and warns that connection disruption can break a stream. That guidance concerns the network path, not CPU capacity. A machine can have spare CPU and still lose the broadcast if its source connection cannot consistently carry the outgoing stream.
The amount of detail in your media workflow matters. A single prepared devotional video being relayed is not equivalent to an OBS session combining a camera, lyrics and several audio sources. If you are still deciding what to send, this comparison of SD and HD streaming choices is useful for thinking about the output you need before committing to an encoding workload.
Test with the actual continuous stream
A useful validation test uses the same file or live source, encoder settings, relay process, audio, overlays and schedule you intend to run. Test the real output rather than a short sample that omits the most demanding parts of your programme. A quiet static image may behave differently from a video with motion, changing scenes or additional production elements.
For a relay, leave the intended feed running long enough to observe regular operation and the points where the workflow changes files, loops a playlist or recovers from an interruption. Confirm that audio and video reach YouTube, that the broadcast remains live, and that the process does not gradually consume resources or stop advancing through the media. A successful initial connection only proves that it connected once.
For an encoder, test the exact output settings and look for whether it keeps up with real time. Watch the encoder’s own status alongside system CPU use and YouTube’s stream health indicators. If frames fall behind, CPU remains heavily occupied or the stream health report shows instability, the instance may lack capacity, the settings may be too demanding, or the upload path may be at fault. Diagnose which limit is being reached before resizing blindly.
Do not treat a test on your laptop as evidence that the VM will behave the same way. The software version, settings, media, competing tasks and network path are part of the workload. Likewise, an apparently quiet CPU reading during an idle period says little about what happens once the full channel schedule is running.
If the point of using a VM is to avoid keeping a personal computer on all night, consider whether you need to operate that relay workflow yourself. StreamNeo can remove the task of leaving your own computer running by turning an uploaded video into a YouTube live stream, which is relevant when the pain is managing an always-on relay rather than tuning a VM’s real-time encoder.
Monitor CPU headroom and stream health
During a continuous test, monitor both the machine and the broadcast. CPU use indicates whether the process is consuming the available compute, while memory and disk observations can expose other constraints. Stream health and the visible audio-video output tell you whether the audience-facing result is actually stable. Neither set of signals replaces the other.
For a relay, sustained high CPU use is a reason to investigate the process, its settings and other tasks rather than to assume the machine is fine because the picture looks acceptable at one moment. For encoding, headroom is particularly important: a process that only just keeps pace under one test condition may fall behind when another task runs or the content becomes more demanding. Do not use a single reading as a universal threshold.
Keep a simple record of the test conditions and observations: region, machine type, stream format, settings, time running, CPU and memory behaviour, and any stream-health warnings or interruptions. This makes later changes interpretable. If you change the region, media, bitrate or encoder preset at the same time as the machine size, you will not know which change affected the result.
The network deserves its own check. Confirm the bitrate you intend to send and compare it with the available upload path, leaving the headroom YouTube recommends. If the connection is disrupted, the stream can break even when the VM has ample compute capacity. A local network problem, a source issue and an overloaded encoder call for different remedies.
Resize only after you have evidence
If the relay test is stable and the machine retains useful headroom, there may be no reason to move to a larger VM merely because the channel runs all day. If the process struggles, first identify whether the constraint is CPU, memory, disk, network or the relay configuration. A larger machine will not fix an inadequate upload route or a damaged media file.
For software encoding, resize in response to observed inability to sustain the configured output, then rerun the same test. Increasing CPU capacity may help when encoding is the bottleneck, but the right change depends on the encoder and other work running alongside it. If you lower resolution, frame rate or encoding complexity instead, validate the resulting output and viewer requirements too.
Use a controlled comparison: keep the media and stream settings constant, change the machine configuration, then repeat the continuous test. Record the result before making another adjustment. This is more useful than choosing by vCPU count alone, especially when comparing shared-core and standard machine types.
Finally, revisit the estimate when you change region, runtime, disk or network usage. Google Cloud’s current calculator is the practical source for the selected configuration; check the figures when you make the decision rather than relying on an older example. The operational choice is the smallest configuration that has demonstrated the needed behaviour for your workload, not the smallest one that starts the stream.
Before committing, compare the operating options on the pricing page. When the file and channel are ready, start free — 24-hour trial, no card.
FAQ
Is e2-standard-2 enough for one 24/7 YouTube channel?
It is a reasonable starting point when the VM only relays one pre-encoded stream. Google lists 2 vCPUs and 8 GB of memory for the type, but that specification does not prove stability for your process. Run the actual continuous workload and check stream health before relying on it.
Can e2-standard-2 encode 1080p video all day?
There is no universal answer supported by the machine and YouTube guidance described here. Encoding demand depends on codec, frame rate, settings, overlays and other processes. Test the exact output you plan to use, then adjust capacity or settings based on observed results.
Should I use Mumbai or Delhi?
Google lists E2 availability in both regions. Choose based on your source, operating location, measured connection behaviour and current configured cost; region availability alone does not show which will work better for your channel.
Are YouTube’s recommended bitrates a VM sizing guide?
No. They are encoder output recommendations that vary with codec, resolution and frame rate, not compute benchmarks. Plan the upload path separately and leave the network headroom YouTube recommends.