OBS versus FFmpeg: which costs less to run for a 24/7 YouTube stream? Neither is inherently cheaper. Your costs depend on what each setup must do, the power draw of the whole machine, your electricity rate, and any added hosting or network charges. FFmpeg may use less power for a simple, compatible stream-copy loop; OBS may be the more economical choice if you need its scenes, captures, and production controls and already have suitable hardware. Those are workload-based possibilities, not results from a controlled power test.
Why the software names do not determine the cost
OBS is a production application: it can capture sources, compose scenes, render them, and encode the output. Its CPU needs vary with the encoder, resolution, frame rate, and scene complexity. Scene rendering also uses GPU resources, and complex scenes or GPU contention can affect performance. OBS documents its system requirements and encoding performance considerations.
FFmpeg is a flexible media tool. Depending on the input and output requirements, it may copy already encoded streams or decode, filter, and encode media again. Stream copy can avoid decoding and encoding, but cannot perform operations that need decoded frames, such as filtering. FFmpeg describes transcoding as computationally expensive and recommends stream-copying when it meets the need. See the FFmpeg documentation.
So a simple FFmpeg loop that copies compatible files is not a fair cost comparison with OBS compositing multiple animated scenes. First match the work: source, output resolution and frame rate, codec, bitrate, audio, and the scene or filter functions you actually require.
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How to calculate the electricity cost
Measure the average power draw of the complete streaming system under representative load, then apply your local electricity rate:
Average watts ÷ 1,000 × runtime in hours = kilowatt-hours (kWh)
kWh × your electricity rate per kWh = electricity cost
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For a continuous stream, measure over a representative interval and scale to the actual runtime. A brief idle reading may not represent a machine encoding and rendering around the clock. A wall meter measures the whole setup; it does not isolate the power used by OBS or FFmpeg. The U.S. Department of Energy explains the kWh calculation as “kWh = kW x hrs” and describes a watt meter as an optional measurement tool in its energy-use guidance.
CPU utilization alone is not a cost measurement. Measure comparable whole-system power, ideally repeating or averaging readings, and use the same machine and matched output workload for both applications. DOE’s method is an appliance-cost calculation, not a published OBS-versus-FFmpeg test.
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Which setup may cost less in practice?
FFmpeg for a simple stream-copy loop
FFmpeg may cost less to run on an existing low-power machine when you are looping compatible, already encoded content and stream copy meets your needs. That is an inference from the lower-work stream-copy path, not a measured power comparison. If you need filters or must change the media’s encoding, FFmpeg may need to decode and re-encode, increasing the workload.
OBS when its production features matter
OBS may be the cheaper overall choice when you need its scene composition, capture, or other production functions and already own a suitable computer. Replacing that workflow with scripts or a paid hosted production service could add labor or service costs; whether it does depends on your setup. OBS’s documented workload components do not establish a comparative price.
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Hardware encoding and cloud hosting
Hardware encoding can shift encoding work from the CPU to a specialized GPU component. OBS generally recommends hardware encoders for performance, but warns that older encoder generations can produce lower quality at the same bitrate. Check the encoder available on your system, its supported settings, the quality you need, and actual power draw; the label “hardware encoding” alone does not establish lower total cost. See OBS’s hardware encoding guidance.
Running a stream in the cloud is a separate deployment choice, not an inherent feature of FFmpeg or OBS. YouTube lists cloud-based live processing among encoder options, but that does not show that a generic VPS will work for your workload or cost less. Compare current regional compute, GPU, storage, and network charges for the specific provider and plan. YouTube’s encoder guidance lists service and hardware categories; it is not a price comparison or endorsement.
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Keep the YouTube output consistent
Use a YouTube-accepted configuration when comparing the applications. YouTube’s live encoder guidance covers RTMP/RTMPS transport, H.264, H.265/HEVC, and AV1 video, frame rates up to 60 fps, constant bitrate, and a recommended two-second keyframe frequency (not over four seconds). The suitable bitrate depends on resolution and frame rate; use the current settings table on YouTube’s live encoder settings page rather than treating one bitrate as universal. YouTube recommends RTMPS for encrypted transport and automatically transcodes live streams into other formats for viewers.
For a useful comparison, keep the target resolution, frame rate, codec, bitrate, keyframe interval, and audio consistent where possible. If one application is doing additional composition or filtering, include that work in the comparison rather than attributing its cost to the application name alone.
Include costs beyond electricity
Count only costs that are incremental to your setup. Depending on how you stream, these may include a computer or GPU bought specifically for the job, cloud compute or storage, metered upload data, restart or uptime measures, and setup and maintenance time. There is no universal ISP fee, VPS rate, or support cost: those depend on provider, location, and plan. The sources cited here do not establish a comparative software subscription charge.
For a 24/7 stream, reliability and operator effort matter alongside the power bill. Compare how each setup recovers after a network or application failure, and include any restart tools, monitoring, or time you need to keep it running. Do not assume a cloud deployment is cheaper until you add its regional compute, GPU, storage, and network costs.
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