If you are sending one continuous encoder feed to YouTube, AWS Lightsail is usually easier to budget when its bundle has enough compute and regional transfer allowance. Google Compute Engine gives you more control, but you need to estimate the VM, disk and network items separately.
Neither service is a universal winner for this workload. The sensible choice depends on your encoder, bitrate, region, monthly outbound data and recovery plan, not on the provider name alone.
What this comparison covers
This comparison is about a cloud VM running an encoder or relay that sends a feed to YouTube. It is not about delivering the video to your viewers. YouTube receives the encoder input and handles playback delivery to viewers; the VM is responsible for producing and sending that input.
That distinction matters when you estimate traffic. Your cloud bill is affected by the stream leaving the VM towards YouTube, not by every viewer watching the YouTube broadcast. A devotional playlist with many viewers does not normally create one separate VM transfer charge per viewer in this setup.
The comparison also assumes that you control the media and have permission to stream it. A cloud VM does not change the copyright or platform rules applying to the videos, music, images or live sources in your broadcast. If your plan is a looping prerecorded channel, first consider the workflow described in how pre-recorded live works on YouTube.
The two services are general-purpose computing products. They do not automatically provide a complete 24/7 broadcast operation. You still need to choose an encoder, configure the YouTube stream key, supervise the process, deal with failures and test what happens after a restart.
Compare Lightsail bundles and included transfer
Lightsail groups compute, storage and a data-transfer allowance into a monthly plan. That makes the first estimate more readable: you can compare the bundle against the expected stream workload instead of starting with several independent billing lines.
AWS's current Linux public-IPv4 table lists the following bundles, as listed on AWS's site in September 2026:
| Lightsail bundle | Monthly price | Included transfer |
|---|---|---|
| Nano | $5/month | 1 TB |
| Micro | $7/month | 2 TB |
| Small | $12/month | 3 TB |
| Medium | $24/month | 4 TB |
| Large | $44/month | 5 TB |
These figures are the listed bundle values, not a promise that any particular encoder will fit comfortably on each size. AWS also states that the data-transfer allowance varies by Region, so confirm the selected location and current table before ordering. See the AWS Lightsail pricing page for the live details.
The allowance is useful only if it covers the stream and the instance's other traffic. A 24/7 channel has no quiet period in which the transfer stops accumulating. Updates, uploads, control traffic and any other use of the instance need room as well.
Lightsail's simplicity has a trade-off. A bundle that looks suitable by transfer may not have enough CPU or memory for your encoder. Conversely, a larger bundle may provide more compute than a prerecorded loop needs while still leaving you with a transfer limit to monitor.
Do not read the table as a universal price ranking against Google Cloud. The comparison is meaningful only when the region, operating system, public networking, storage, encoder workload and transfer assumption are equivalent.
Estimate Compute Engine VM, disk and network costs
Compute Engine is more configurable, but the bill is assembled from more parts. Start with the machine type and region, then add the operating system choice, persistent disk, public networking and expected traffic. A VM price alone is not a like-for-like comparison with a Lightsail bundle.
Google's Compute Engine pricing documentation states that the VM pricing page does not cover disk and networking. That is the reason a small-looking VM line can produce an incomplete estimate. Include every resource required to keep the encoder operating continuously.
For a basic prerecorded stream, the estimate might include a modest machine, a boot disk and the network traffic sent to YouTube. For a live camera production, it may also need enough CPU for encoding, memory for the production software and storage for recordings or media files. The right machine is determined by the work being done, not by the fact that the stream is called 24/7.
A useful comparison sheet should have these columns:
| Item | Lightsail | Compute Engine |
|---|---|---|
| Compute | Included in the selected bundle | Selected VM type and usage |
| Storage | Included within the bundle structure | Disk selected and billed separately where applicable |
| Outbound stream | Checked against the regional allowance | Estimated as network usage for the chosen region and destination |
| Public access | Part of the selected configuration | Check the selected networking arrangement and address needs |
| Budget shape | More visible monthly bundle | Usage-based estimate with separate components |
Use the providers' calculators or pricing pages for the final figure immediately before committing. Prices, regional rules and billing treatment can change. If you are comparing India with Singapore, Europe or the United States, enter each region separately rather than assuming the difference is only network distance.
Do not treat a documented maximum egress figure as guaranteed encoder throughput. Google documents maximums that depend on machine type and destination, but a ceiling is not a service promise that your process will sustain that rate at every moment. Your test should measure the actual configuration you intend to use.
Match each option to the encoder workload
The most important question is what the VM must encode. A file loop is a different job from a camera production with scene changes, audio mixing, graphics and several inputs.
For a simple prerecorded channel, the VM may only need to read media, decode it and send a prepared output to YouTube. If the files already match the desired output and the process is not doing heavy transcoding, compute demand may be modest. The remaining concerns are stable networking, process supervision, storage access and a clean restart after failure.
A 24/7 devotional playlist, radio visualiser or lofi loop often falls into this category. It still needs testing, because a process that works for one file can stop at the end of a playlist, lose audio after a reconnect or fail when a damaged file is reached. The guide to making a YouTube live playlist repeat continuously in India covers a related playlist problem, but the cloud provider does not solve the media workflow by itself.
A live camera or studio feed has different requirements. You may need to ingest a camera, mix audio, add lower thirds, switch scenes and encode in real time. That usually makes CPU, memory, input support and operational controls more important than a bundle's headline transfer allowance.
If you are using FFmpeg, include the actual codec, resolution, frame rate and preset in your test. A command that merely copies an existing stream has a different CPU profile from one that decodes and re-encodes every frame. For troubleshooting, the FFmpeg reconnect setup for YouTube streams on an India VPS is relevant to the process and recovery side, although it does not establish that a VPS provider will have the same behaviour as your selected VM.
YouTube publishes different recommended bitrate ranges for codec, resolution and frame rate. Check the YouTube encoder settings and bitrate guidance before sizing the VM. Do not choose a bitrate first and then assume the same value applies to every output format.
Consider region and recovery requirements
Region affects more than latency. It can change a Lightsail transfer allowance, the Compute Engine network estimate, the available machine types and the practical route to YouTube. It may also affect where you prefer to keep media files and how quickly you can access the machine during an incident.
For an audience in India, an India-region VM may be a sensible starting point, but it is not automatically the right choice. The VM is sending to YouTube, not directly serving each Indian viewer. Test the route from the actual region, then compare the resulting stability, cost and available resources. A nearby region that lacks a suitable bundle or has a different transfer allowance may not be the better operational choice.
Recovery deserves its own design. At minimum, decide what should happen if the encoder process exits, the VM reboots, the stream key is rejected, a media file cannot be read or the connection to YouTube drops. A process supervisor can restart an encoder, but it cannot repair a bad command, replace missing media or prove that YouTube is receiving a healthy feed.
YouTube recommends testing before going live, monitoring stream health, leaving network headroom and testing encoder failover. Its streaming tips are useful because they frame continuity as an end-to-end responsibility rather than a property of a cloud brand.
A single VM is still a single path. If the channel is important enough that a long interruption has a real cost, consider a second encoder or another prepared path and test the switchover before relying on it. That does not guarantee uninterrupted service, but it gives you a recovery procedure instead of only a hope that an automatic restart will be enough.
For a prerecorded channel, a managed service may be a better fit if you do not want to maintain an operating system, encoder process and recovery rules. StreamNeo removes that particular maintenance burden by taking an uploaded video and running the YouTube broadcast from the cloud after you provide the stream key, with automatic monitoring and restart if the stream drops. It is YouTube-only, so it is not a substitute for a VM when you need general-purpose inputs or production software.
Hardware is another valid route for some live productions. YouTube's verified encoder directory lists devices such as AJA HELO Plus, which can stream directly to YouTube and record locally. That may suit a camera operator who wants a dedicated appliance, but it is a different trade-off from renting a cloud VM and is not required for either Lightsail or Compute Engine.
Choose using your bitrate and monthly transfer estimate
Set the output resolution, frame rate, codec and bitrate before comparing transfer. The encoder setting is the input to the calculation. A six-megabit-per-second stream and a ten-megabit-per-second stream do not have the same monthly requirement, even if they use the same VM.
For a rough decimal estimate, multiply bitrate in megabits per second by the seconds streamed, divide by eight to convert megabits to megabytes, then convert the result to decimal terabytes. At 6 Mbps for 30 continuous days, the payload is about 1.94 decimal TB before protocol overhead. At 10 Mbps for the same period, it is about 3.24 decimal TB.
Those are arithmetic estimates, not AWS or Google billing quotes. Add overhead and allow for other instance traffic. Then compare the result with the Lightsail allowance for the exact region, or enter the expected traffic into the Compute Engine estimate. Do not use the bundle's allowance as though every byte can be spent on the YouTube feed.
A practical worksheet looks like this:
| Question | What to record |
|---|---|
| Output format | Resolution, frame rate and codec |
| Encoder bitrate | The configured Mbps value |
| Continuous runtime | Hours per day and days in the billing period |
| Payload estimate | Calculated decimal TB before overhead |
| Extra traffic | Updates, uploads, controls and other transfers |
| Region | The exact Lightsail or Compute Engine location |
| Recovery | Restart method, monitoring and backup path |
If the stream sits comfortably below a regional Lightsail allowance and the bundle has enough CPU and memory, the simpler budget may be useful. If the allowance is close to the calculated transfer, the margin is too small for comfort. Move to a suitable bundle, reduce the output requirement only if YouTube's guidance and your viewers' needs allow it, or compare Compute Engine with a complete network estimate.
Compute Engine becomes attractive when you need a particular machine shape, disk arrangement, operating system or production tool. Its flexibility is valuable when a fixed bundle forces you to pay for the wrong balance of resources. It can also make the estimate harder, because the network and disk lines must be included rather than ignored.
Keep a record of your actual transfer and encoder resource use during a test. The test will not prove long-term availability, but it can expose an undersized machine, unstable route, excessive bitrate, rising disk use or a process that fails after a loop. Also check YouTube's current stream limits and archive rules before designing a schedule, since those platform details can change.
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 Lightsail always cheaper for a 24/7 YouTube stream?
No. Lightsail can be easier to budget when its bundle includes enough compute and regional transfer for your bitrate. Compute Engine may suit a different machine shape or region, but its disk and network costs need to be included before comparing totals.
Does YouTube viewer traffic increase the VM's outbound transfer?
Not in the same way as a setup where the VM distributes the video directly to viewers. In this comparison, the VM sends the encoder feed to YouTube, and YouTube handles delivery to viewers. The VM estimate should focus on that outbound feed and the instance's other traffic.
Can a VM guarantee a 24/7 broadcast?
No. A VM does not prove end-to-end availability. Use supervision, monitoring, restart rules, network headroom and, where the channel requires it, a tested backup encoder or failover path.
Should I use a cloud VM for a prerecorded loop?
A VM can work when you need control over the encoder and operating system. If you only need to upload a file and run a YouTube-only continuous broadcast, a managed workflow may remove maintenance that a general-purpose VM would leave to you.