A listed IndiaAI Compute NVIDIA L4.1x GPU instance costs ₹44.86 per hour on demand, or ₹24.00 per hour under a 12-month reservation. If billed continuously for an illustrative 730-hour month, those rates work out to about ₹32,748 and ₹17,520, respectively. These figures price the GPU instance—not a complete, verified 24/7 streaming setup. Storage and other possible costs are separate, and the available published figures do not establish an all-in monthly total.
What the IndiaAI L4.1x rates mean
IndiaAI Compute’s price list, whose displayed build was last updated on 2026-08-25, lists an NVIDIA L4.1x at ₹44.86 per GPU-hour on demand and ₹24.00 per GPU-hour at the 12-month reserved rate. The reservation figure comes with a year-long commitment; it is not a month-to-month rate.
| Billing option | Listed GPU rate | Illustrative cost for 730 billed hours | Qualification |
|---|---|---|---|
| On demand | ₹44.86 per GPU-hour | About ₹32,748 (₹44.86 × 730 = ₹32,747.80) | Arithmetic using the listed hourly rate; the 730-hour monthly estimate was calculated on 2026-10-03, not published as a separate monthly price. IndiaAI Compute |
| 12-month reserved | ₹24.00 per GPU-hour | About ₹17,520 | Arithmetic using the listed rate over 730 hours; requires a 12-month reservation and is not a standalone monthly plan. IndiaAI Compute |
For on-demand GPU charges, the IndiaAI calculator describes the calculation as hourly rate × number of instances × required hours. Its 12-month reservation formula uses hourly rate × instances × 365 days × 24 hours. It treats storage separately, with distinct rates for block, file, and object storage. Check the calculator and current price list for the specific instance and storage choices before committing.
Why this is not an all-in 24/7 streaming price
A GPU hourly rate does not establish the cost of a complete machine or deployment. The published figures cited here do not settle provider-specific CPU and RAM, storage quantity, network throughput, egress charges, software licensing, taxes, instance availability, or the quality of the network route to YouTube. Without a chosen provider, region, transfer volume, and applicable tariffs, an exact bandwidth or egress bill cannot be responsibly calculated.
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The 730-hour examples assume that one instance remains billed for all those hours. If a stream runs only part of the month, calculate GPU charges using the actual billed instance-hours and the applicable rate, then add the selected services. A 24/7 workload may make reserved or sustained-use pricing worth investigating, but the listed reserved price requires a 12-month term.
Check the workload before choosing a GPU
There are two materially different cloud workloads: rendering a game or scene remotely and encoding its output, or sending an already-produced video feed to a cloud encoder. The listed rate and hardware specifications do not establish a tested configuration for either one. In particular, there is no basis here to claim that a single L4.1x instance has been tested for, or guarantees, a stable 4K60 stream.
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NVIDIA lists the L4 with 24 GB of GPU memory and two NVENC encoders. Those are product specifications that make the card relevant to video-encoding research, not a live-stream performance benchmark or proof that a particular cloud instance exposes the encoders as needed. Confirm the actual instance configuration and test the full path before relying on it for a continuous broadcast. NVIDIA L4 specifications
YouTube’s 4K60 ingest settings affect the setup
YouTube’s recommended upload bitrate for 4K/2160p at 60 fps depends on codec: 35 Mbps for AV1 or H.265, and 50 Mbps for H.264. The corresponding minimums are 10 Mbps and 14 Mbps. YouTube recommends RTMPS, a secure extension to RTMP, for live ingest. YouTube Help: Choose live encoder settings, bitrates, and resolutions
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- Use constant bitrate (CBR), a keyframe interval of two seconds, and no more than 60 fps, in line with YouTube’s encoder guidance.
- For 4K streams, YouTube uses normal latency rather than the low-latency option.
- Ensure the cloud instance’s usable outbound path can sustain the selected bitrate. The bitrate sent to YouTube alone does not determine the provider’s network bill; billing depends on that provider’s rules and any additional traffic or services.
These are YouTube ingest recommendations, not evidence that a particular GPU, region, or network path will meet them reliably. Validate the selected instance and delivery route with the actual encoder and stream settings.
How to estimate your own monthly GPU charge
- Choose the workload: remote rendering plus encoding, or encoding an incoming video feed. The required compute and configuration may differ.
- Ask the provider for the exact instance type, GPU access, CPU and RAM, region, and whether the video encoders are available to your software.
- Estimate billed GPU hours: use actual planned hours for on-demand use, or confirm the full commitment and billing formula for a reserved rate.
- Multiply the listed hourly rate by instance count and billed hours. Treat the result as GPU-instance charges only.
- Add separately quoted storage, network or egress, licensing, taxes, and any other services. Confirm availability and test the complete YouTube ingest path before treating the estimate as a dependable operating budget.
A cloud alternative for prerecorded YouTube broadcasts
If your goal is to keep uploaded documentaries or other prerecorded videos playing as a 24/7 YouTube stream—not to render a scene or stream from a camera—StreamNeo is a different option from renting and configuring a GPU instance. It uploads and loops your videos from the cloud, so your computer and home connection do not need to stay on. Each slot streams the files as uploaded, up to 4K 60fps, at one flat price per slot, and automatically recovers if YouTube drops the stream. The first day is free with no card, one free day per account.
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StreamNeo’s monthly option is $9.99 per month. For an upload-and-loop workflow, compare that with the GPU-instance figures above while accounting for what each approach does: StreamNeo plays uploaded videos on YouTube; it is not a remote rendering GPU or a camera-based live-stream solution.
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