A 24/7 YouTube stream’s estimated outgoing data depends chiefly on its bitrate and how long it runs: each steady 1 Mbps works out to about 10.8 decimal GB per day. Google Cloud lists transfer from a Compute Engine VM to YouTube as “No charge”, but that applies to that transfer category, not to the VM or every other service in the setup.
You can reproduce the estimate with a short formula, then compare it with the bitrate you intend to use. The examples below use YouTube’s published H.264 recommendations for common resolutions and frame rates; they are arithmetic estimates, not measured usage or a monthly cloud bill.
Estimate stream data from bitrate and time
Resolution alone does not tell you how much data a stream sends. A 1080p picture can be encoded at different bitrates, and a higher frame rate may call for a different setting. For a first-order estimate, use the encoder’s steady video bitrate and the stream’s runtime. If audio is configured separately or the overall bitrate differs from the video setting, account for that in the bitrate you use.
The key distinction is between data volume and cost. Data volume answers how many bytes are estimated to leave the VM while it sends the stream. Cost depends on Google Cloud’s applicable pricing categories and the actual services you run. A transfer rule that says no charge does not erase VM compute, storage, or charges for another managed service.
A useful planning sequence is:
- Choose the codec, resolution, frame rate and bitrate you plan to send to YouTube.
- Convert that steady bitrate to an hourly data estimate.
- Multiply by the number of hours in your planned day or billing period.
- Separately check which cloud resources are running and how they are billed.
If you are deciding how to configure a YouTube broadcast rather than estimating an existing one, first review the basics of going live on YouTube, including encoder setup. The data estimate is only as useful as the bitrate you actually choose; YouTube’s recommendations are tied to codec and output profile, not a universal rule for every stream.
Use the decimal GB-per-hour formula
Start with bitrate in megabits per second (Mbps). Multiply by 1,000,000 to get bits per second, multiply by the number of seconds, divide by eight to convert bits to bytes, and divide by 1,000,000,000 to express the result in decimal gigabytes (GB).
For one hour, that is:
Mbps × 1,000,000 bits/second × 3,600 seconds ÷ 8 ÷ 1,000,000,000 = Mbps × 0.45 GB
So the compact estimate is bitrate in Mbps × 0.45 = decimal GB per hour. For a full day, multiply the hourly figure by 24. For a 30-day period, multiply it by 720 hours. Another quick check is that each 1 Mbps sustained continuously estimates to 10.8 decimal GB per day, or 324 decimal GB over 30 days.
These units matter. This article uses decimal GB, where one GB is 1,000,000,000 bytes. It does not use GiB, which is 1,073,741,824 bytes. If a dashboard displays GiB or reports actual bytes, its number will not match a decimal-GB estimate exactly even before stream behaviour is considered.
| Steady bitrate | Approx. per hour | Approx. per 24 hours | Approx. per 30 days |
|---|---|---|---|
| 4 Mbps | 1.8 GB | 43.2 GB | 1,296 GB |
| 6 Mbps | 2.7 GB | 64.8 GB | 1,944 GB |
| 8 Mbps | 3.6 GB | 86.4 GB | 2,592 GB |
| 10 Mbps | 4.5 GB | 108 GB | 3,240 GB |
| 12 Mbps | 5.4 GB | 129.6 GB | 3,888 GB |
Every figure in the table comes from the same multiplication, rather than an observation of a particular channel. It gives you a consistent way to compare settings and runtimes. If your channel runs for less than a full day, substitute its actual run time; if it has pauses or interruptions, its total sent data will differ from a continuously running estimate.
Worked example: 8 Mbps
At a steady 8 Mbps, the hourly estimate is 8 × 0.45, or 3.6 decimal GB per hour. A continuously running day is 3.6 × 24, or 86.4 GB. Over 30 days, the estimate is 3.6 × 720, or 2,592 GB.
That last figure is about 2.592 decimal terabytes if you use 1,000 GB per TB. Keep the unit convention explicit when comparing a provider’s display or a spreadsheet. Do not treat the result as a meter reading: it is a planning estimate derived from the assumed bitrate and duration.
This example can also help you check a change in settings. Moving from 8 Mbps to 10 Mbps increases the estimated volume in proportion to the bitrate: the 10 Mbps estimate is one quarter higher. That is not a quality guarantee. Whether the difference is worthwhile depends on the picture, motion, source material and the needs of the audience. A devotional still image, a local news loop and fast sports footage do not place the same demands on an encoder.
YouTube’s live encoder settings guidance lists bitrate recommendations by codec, resolution and frame rate. It also notes that YouTube detects the encoder settings chosen and transcodes a live stream into formats for viewers on different devices and networks. Your outgoing bitrate is therefore not the same as the bitrate each viewer receives.
Worked example: 1080p at 30 fps
YouTube’s published H.264 recommendation for 1080p at 30 frames per second is 10 Mbps. At that setting, the formula gives 10 × 0.45 = 4.5 GB per hour. For a 24-hour day, 4.5 × 24 = 108 GB; for 30 days, 4.5 × 720 = 3,240 GB, or 3.24 decimal TB.
Treat the recommendation as a codec-specific reference, not a requirement that every 1080p/30 stream must use exactly 10 Mbps. If you use another codec, a different encoder configuration, or a variable bitrate, your actual traffic can differ. YouTube recommends constant bitrate encoding for live streams, which makes this kind of estimate easier to apply, but a configured target is not a promise that every second sends precisely that amount.
For an always-on channel, write down the bitrate alongside the profile: “H.264, 1080p, 30 fps, 10 Mbps” is far more useful than “1080p”. If you later lower the bitrate to reduce the estimated volume, check the resulting picture on the content your audience actually watches. A static temple image may tolerate a different setting from a busy street scene or a lesson with frequent movement.
If your stream consists of a repeating recording, the media workflow also matters. For example, a playlist that repeats continuously on YouTube Live is a separate reliability concern from the bitrate calculation: a correct data estimate cannot prevent a playlist from stopping at the end of its file.
Worked example: 1080p at 60 fps
For H.264 at 1080p and 60 frames per second, YouTube’s published recommendation is 12 Mbps. The estimate is 12 × 0.45 = 5.4 GB per hour. That becomes 5.4 × 24 = 129.6 GB in a day and 5.4 × 720 = 3,888 GB in 30 days, or 3.888 decimal TB.
Compared with the 10 Mbps 1080p/30 example, the 12 Mbps profile produces a proportionally larger estimate because the bitrate is higher. The arithmetic does not mean that 60 fps is automatically better for your channel. It is most useful when the source has motion that benefits from the higher frame rate; for a mostly still or slow-moving visual, you may have little reason to choose it.
The same principle applies to YouTube’s lower-resolution recommendations. The H.264 page lists 4 Mbps for 720p/30 and 6 Mbps for 720p/60. Applying the formula gives 1.8 and 2.7 decimal GB per hour, respectively. Label each estimate with the settings it assumes. A bitrate number without codec, resolution and frame rate can easily be mistaken for a general platform limit.
The figures above are derived calculations, not published Google or YouTube data-usage measurements. Actual transferred bytes can vary with bitrate behaviour, protocol overhead, interruptions and restarts. The cited guidance does not provide one overhead allowance that applies to every channel, so adding an invented universal percentage would give a false sense of precision.
Understand the VM-to-YouTube no-charge category
Google Cloud’s VPC network pricing page specifically lists transfer from a Compute Engine VM to products including YouTube as “No charge”. The listed category applies whether the VM has an external IP address or an internal IP address. For the stated case—outgoing transfer from a VM to YouTube—that is the relevant rule to check.
The fact that a stream sends several terabytes by the estimate above does not, by itself, mean you should apply Google Cloud’s general internet egress rates to that VM-to-YouTube traffic. The pricing page separates the specifically listed YouTube category from other network transfer cases. Apply the general rates only if your traffic falls into a different category; do not assume that the YouTube entry covers traffic to every destination in your design.
The “in India” part of the question does not change the arithmetic: a steady bitrate and runtime give the same decimal data estimate wherever the VM is located. For the specified VM-to-YouTube category, Google lists YouTube by name and does not make the no-charge statement depend on the VM’s IP type. Still, check the current pricing page for the resources and destinations in your own configuration before budgeting.
If your channel sends a separate copy to another destination, serves files to viewers from a cloud VM, or uses a different product to process or distribute the stream, that is not automatically the same transfer category. Draw the data path first: identify what sends data, where it goes, and which Google Cloud product handles it. That makes it easier to apply the right pricing entry rather than treating “egress” as one universal charge.
Separate transfer from total setup costs
A no-charge transfer line is not an all-in cost estimate. A Compute Engine VM that runs continuously still has compute costs based on its configuration and run time. Disk and any stored recordings can add separate charges. If you enable other cloud products, check those products’ pricing independently rather than assuming the VM-to-YouTube rule includes them.
For example, Google Cloud’s Live Stream API pricing page describes resource-based charges and says active channel time is billed; the API has no free usage tier. That matters only if your architecture uses the API. It is a separate managed service, not a synonym for sending a stream from a VM to YouTube, and its cost depends on the resources and configuration used.
Google’s Live Stream API best-practices guidance gives its own recommended input and output profiles. For example, it recommends 6 Mbps for 1080p25/30 H.264 input and 9 Mbps for 1080p50/60 input, while output ladder rates differ. Do not substitute those API figures for YouTube’s encoder recommendations in the earlier examples. The product, stream path and purpose of a bitrate setting are different.
A practical cost worksheet should have separate lines for:
| Cost or usage item | What to identify |
|---|---|
| VM-to-YouTube transfer | Whether the traffic matches Google Cloud’s listed YouTube category |
| VM compute | The VM configuration and how long it runs |
| Disk and recordings | Whether media is stored, how much is retained, and for how long |
| Other cloud services | Any managed streaming, processing or distribution product actually enabled |
Do not turn the data estimate into a monthly bill without those details. A continuously running VM and an architecture using the Live Stream API are not equivalent configurations. Nor does a “no charge” transfer entry mean that all Google Cloud streaming costs are free. If you are choosing between running a computer yourself and moving the workload to a cloud machine, the practical trade-offs in moving a 24/7 stream from a PC to a cloud server can help frame the operational decision; compare costs only after matching the runtime and services.
For an always-on channel, reliability is also part of the setup decision. A local computer may suit you if you already have dependable power, connectivity and someone to respond when it stops. A cloud VM shifts the running workload away from your premises, but you still need to configure, monitor and maintain the resources involved. A hosted option can remove the need to keep your own computer switched on; StreamNeo turns an uploaded video into a 24/7 YouTube live stream, so it addresses the specific burden of keeping a local machine running for a continuous file-based channel.
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
How much data does a 24/7 YouTube stream use?
Use the stream’s bitrate and runtime, not resolution alone. At 8 Mbps, the decimal estimate is 3.6 GB per hour, 86.4 GB per day and 2,592 GB over 30 days; a different bitrate scales the result proportionally.
Does Google Cloud charge egress to YouTube?
Google Cloud lists transfer from a Compute Engine VM to YouTube as “No charge”, for VMs with either an external or internal IP address. That statement applies to the listed transfer category, not to VM compute, storage or every cloud service.
Are the figures in this article measured usage?
No. They are arithmetic estimates using decimal GB and an assumed steady bitrate. Protocol overhead, bitrate variation, interruptions and restarts can make actual transferred bytes different.
Does the estimate include Google Cloud Live Stream API charges?
No. The formula estimates data sent at a stated bitrate; it does not estimate a managed service bill. If your design uses the Live Stream API, check its separate resource-based pricing and the active channel time and features you use.