Transcoding Farm Cost 2026: Cloud vs On-Prem vs GPU

Updated November 2026: live-channel prices re-checked against AWS's current MediaLive examples, MediaConvert multipliers corrected (AV1 lists at 7×), and new notes on GPU Spot discounts and media ASICs.

Transcoding farm cost: the short answer

Managed cloud transcoding bills per minute of output. Google Cloud Transcoder API lists $0.015 for SD, $0.030 for HD and $0.060 for UHD per output minute (November 2026), so a six-rung ladder of two SD, three HD and one UHD rung costs about $10.80 per source hour. A self-hosted ffmpeg fleet on AWS Spot capacity costs roughly $0.10 to $0.30 of raw compute per source hour in our planning model, plus the engineers who run it. Below a few hundred catalog-hours a month the managed service is cheaper; above that, self-hosting pulls ahead.

Live transcoding is billed separately, per channel-hour. AWS's own MediaLive example, a five-rung channel, comes to $2.37 per hour on a single pipeline and $3.94 per hour with two pipelines (November 2026).

Why this matters

Of the two largest recurring compute lines in a streaming platform, egress decides most of the bill, but transcoding decides the rest — and unlike egress, you pay it again every time you add a codec, a resolution, or a new device target. A founder, product manager, or streaming CTO sizing a platform has to choose how the farm runs before a single title is encoded, and the wrong default quietly over-pays on every hour of content for the life of the catalog. This article is written for that decision-maker, not for the engineer tuning encoder flags: the codec mechanics live in the Video Encoding section, and we link out to them rather than re-derive. What stays here is the product-and-cost question — cloud versus on-prem, CPU versus GPU, real-time versus batch — and the arithmetic that tells you which one your catalog and audience actually need.

What the transcoding farm actually does

Start with the word. To transcode is to take one already-encoded master file — the high-quality intermediate the industry calls the mezzanine — decode it back to raw pictures, and re-encode it several times at the different resolutions and bitrates your players will request. Each of those re-encodes produces one rendition, one rung on the encoding ladder. A six-rung ladder means six encodes of every title. The transcoding farm is simply the fleet of machines that runs all those encodes in parallel, because doing them one at a time would take far too long for a real catalog.

Picture a print shop working from one master photograph. The photographer hands over a single high-resolution file, and the shop runs off a wallet print, a postcard, an A4, and a billboard — same image, many sizes, each cut for a different use. The transcoding farm is that shop for video: one mezzanine in, a ladder of sized-and-priced copies out. The shop's cost is the press time, not the original photo; the farm's cost is the compute, not the master.

Two properties of this cost matter before we price it. First, transcoding is a one-time cost per title in the sense that you encode a movie once and stream it a million times — but it is a recurring cost across the catalog and across time, because every new title pays it again, and so does every existing title the day you add a new codec (say AV1) or a new device tier. A catalog is never "done" being encoded. Second, the work is compute-bound, not storage-bound: the expensive part is the processor cycles spent compressing pixels, not the disk the result sits on. That is why the farm is its own line item, separate from the storage and the CDN.

Figure 1. The transcoding farm turns one mezzanine into the whole ladder. Each rung is a separate encode; the farm runs them in parallel. The cost is processor time, paid again for every title and every new codec.

VOD and live are two different bills

Before choosing how to run the farm, separate the two jobs it can do, because they price completely differently.

Video-on-demand (VOD) transcoding is batch work. The file already exists; you can encode it faster than real time, in chunks, on whatever machines are cheapest, and if a machine dies mid-job you just retry the chunk. Nobody is waiting in real time for a specific second of output. This tolerance for interruption is what makes VOD transcoding cheap to run, and it is the reason the spot market (discussed below) fits it so well.

Live transcoding is real-time work. A camera feed arrives at one second per second and must leave at one second per second; the encoder can never fall behind, and there is no second chance at a missed moment. You pay for a machine to sit running for the whole broadcast whether the bitrate is busy or idle. That makes live a per-hour-of-channel cost rather than a per-hour-of-content cost, and it is structurally more expensive per delivered hour. The split between these two pipelines is the subject of live vs VOD: two pipelines, one platform; here we price each in turn, VOD first because it is where most catalogs spend most of their money.

The three ways to run the farm

There are three architectures, and most platforms end up using more than one. The cost model of each is different, so compare the models, not the logos.

1. Managed cloud services — you pay per minute of output

The simplest option is to hand the file to a cloud service and let it return the renditions. Two reference services, with their public 2026 list prices:

• AWS Elemental MediaConvert bills per minute of output, scaled by multipliers. Its Basic tier lists at $0.0075 per minute for SD H.264, single-pass; HD counts as 2× ($0.0150) and 4K as 4×, and the rate drops to $0.0053 after the first 100,000 normalized minutes a month (US East, November 2026). The Professional tier starts at $0.0120 per normalized minute, and the HD multipliers are where its bill grows: 2× for single-pass H.264, 3.5× for multi-pass H.264, 4× for single-pass HEVC, and 7× for multi-pass HEVC or AV1. An HD minute of AV1 therefore lists at about $0.084.
• Google Cloud Transcoder API bills per minute of output by resolution class: $0.015 per minute for SD (below 720p), $0.030 for HD (720p–1080p), and $0.060 for UHD/4K, with audio-only output at $0.005 per minute. Opt-in 1- and 3-year savings plans bring HD down to $0.027 and $0.024 per minute (November 2026).

The crucial detail people miss is that the meter runs on output, not input. A one-hour master encoded into a six-rung ladder produces six hours of output, and you are billed for all six. Walk the arithmetic on the Google rates for a typical ladder of two SD rungs, three HD rungs, and one UHD rung:

one source hour → 6 output hours = 360 output minutes, split by rung:
  2 SD  rungs × 60 min × $0.015 = 120 min × $0.015 = $1.80
  3 HD  rungs × 60 min × $0.030 = 180 min × $0.030 = $5.40
  1 UHD rung  × 60 min × $0.060 =  60 min × $0.060 = $3.60
  ────────────────────────────────────────────────────────
  per source hour                                  = $10.80

The appeal is real: zero machines to run, instant elasticity, and you pay exactly nothing when no encoding is happening. The drawback is just as real: at a few cents per output minute, a large or fast-growing catalog watches the meter add up, and there is no idle discount because there is no idle.

2. Self-hosted ffmpeg — you pay per machine-hour

The second option is to rent ordinary cloud machines and run an open-source encoder — almost always ffmpeg — on them yourself, queuing jobs across the fleet. Now you are not paying per output minute; you are paying for machine time, and that changes the math because cloud machines have a deep discount most managed services cannot pass through: the spot market, where you rent a provider's spare capacity for a fraction of the standard on-demand price, with the catch that the provider can reclaim the machine on about two minutes' notice.

The discount is large. AWS advertises Spot at up to 90% off On-Demand and sends a two-minute interruption notice before it reclaims an instance (AWS documentation, November 2026). In one published walkthrough, a compute instance that lists near $0.40 per hour on demand rents for roughly $0.10 per hour on the spot market — about a 75% cut. Because VOD transcoding is interruptible batch work, the two-minute reclaim notice is survivable: you split each title into chunks, and a reclaimed chunk simply re-runs elsewhere. One published cluster example put a 1,024-core fleet at about $51 per hour on demand versus roughly $15 per hour on spot — the difference between about $36,700 and $9,300 a month for the same throughput.

GPU capacity discounts far less. The g6.xlarge instance (one NVIDIA L4) lists at $0.8048 per hour On-Demand in US East, while its Spot price across that region's availability zones ran from about $0.43 to $0.77 per hour in November 2026. That is a cut of 4% to 46%, not 75%. Budget a GPU fleet at On-Demand or reserved prices and count any Spot saving as a bonus.

The appeal is the lowest unit cost at volume and full control over encoder settings. The cost you take on in exchange is operations: you run the job queue, handle spot interruptions, monitor failures, and keep the fleet patched. That engineering time is a real line item, which is why self-hosting rarely pays below a few hundred catalog-hours a month.

3. On-premises hardware — you pay up front

The third option is to buy the machines and run them in your own racks. This is a capital expense — pay once, then only power and maintenance — and it only beats rented cloud capacity at very large, steady volume, where the machines run hot enough, long enough, to amortize the purchase. For a streaming catalog with bursty encoding (a content drop, a back-catalog re-encode), the cloud's pay-for-what-you-use elasticity usually wins. On-prem earns its place for broadcasters and platforms with a constant, predictable encoding load that never goes idle.

Figure 2. The same job, three cost shapes: managed cloud bills per output minute (no idle cost), self-hosted bills per machine-hour (cheap at volume, you run the ops), on-prem is capital up front (only at steady scale).

GPU versus CPU: speed and density against compression

Cutting across all three architectures is a second choice: encode on the machine's general-purpose processor (CPU) or on a graphics chip (GPU) built with a dedicated video encoder such as NVIDIA's NVENC. This is the trade-off that confuses the most teams, so state it plainly.

A GPU encoder is dramatically faster and denser. On NVIDIA's Ada generation, NVENC encodes 1080p at up to roughly 800 frames per second and 4K at up to roughly 200 frames per second (NVIDIA benchmark, July 2023), where a high-quality CPU encode (the x264 software encoder at its "slow" preset) on a 4K clip can run three to five times slower than the video's own runtime. The GPU also packs more simultaneous jobs onto one machine. For sheer throughput — a huge back-catalog to clear, or a long-tail library where speed matters more than the last byte — the GPU wins decisively.

Those throughput figures are for Ada. NVIDIA's Video Codec SDK 13.0 for Blackwell GPUs (February 2025) added 4:2:2 chroma for H.264 and HEVC, 10-bit H.264, and an AV1 ultra-high-quality mode that uses seven B-frames instead of five. NVIDIA describes the quality gain over Ada as significant but gives no single percentage, so test on your own content before you plan around it.

The CPU's advantage is compression efficiency: at the same target quality, a slow software encode produces a smaller file than the GPU does. On most live-action content the gap is modest and modern NVENC lands close to the x264 "medium" preset in measured quality. But the GPU falls visibly behind on hard material — film grain, animation, fine high-detail textures — and for a preservation master or a broadcast-grade deliverable, a slow CPU encode with x264 or x265 is still the right tool. Which codec to reach for — H.264 for reach, HEVC or AV1 for efficiency — is the product decision in codec strategy for OTT, while the codec-by-codec mechanics live in Video Encoding; what matters for the farm is the cost consequence.

That consequence is the hinge of the whole article: a smaller file is not just a quality nicety — it is permanently lower egress. The CPU's extra compression saves bytes on every single view, forever. The GPU's speed saves processor time once, at encode. So the GPU-versus-CPU choice is really a question of which cost dominates for this title — the one-time encode or the lifetime delivery.

A third class of encoder hardware sits beside CPUs and GPUs: dedicated media ASICs, which some vendors call VPUs. AMD rates its Alveo MA35D card, announced in April 2023, at up to 32 simultaneous 1080p60 streams per card with AV1 support, at about 1 watt per stream. AWS's VT1 instances use the older Xilinx U30 card for the same job. For a vendor-by-vendor view of these options, see cloud transcoding services compared, from AWS to NETINT.

How much does it cost to transcode a catalog?

Put the two main options side by side on a real catalog: 10,000 hours of VOD, a six-rung ladder (two SD, three HD, one UHD). That is 60,000 output-hours, or 3.6 million output-minutes.

Managed cloud, at the per-rung Google rates we used above ($10.80 per source hour):

10,000 source hours × $10.80 per source hour = $108,000 to encode the catalog once

Self-hosted on spot, encoding the same ladder. Suppose one spot machine at ~$0.10/hour encodes a six-rung ladder for one source hour in about one hour of wall-clock time (a reasonable mid-range figure for CPU encoding several rungs in parallel per machine):

10,000 source hours × 1 machine-hour × $0.10 = $1,000 in raw compute
+ realistic overhead (failures, retries, orchestration, ~2×) ≈ $2,000
+ engineering time to build and run the queue (amortized) = the real cost

The raw-compute gap — roughly $108,000 against a few thousand dollars — looks decisive, and at this scale it is: a 10,000-hour catalog is comfortably past the break-even, and self-hosting saves real money. But notice what closes the gap at small scale. The managed bill is purely proportional — encode 100 hours and you pay about $1,080, with zero fixed cost. The self-hosted bill carries a fixed cost that has nothing to do with output: the engineering to build and operate the fleet. Below a few hundred hours a month, that fixed cost is larger than the entire managed bill, and the managed service is cheaper and simpler.

Figure 3. The break-even. Managed cloud is pure per-minute (no fixed cost, steep slope). Self-hosted carries a fixed ops cost but a far lower per-hour rate. Below the crossover, managed is cheaper; above it, self-hosting pulls away.

The number that actually decides: encode cost against lifetime egress

Here is the mistake that costs the most, and it is not picking the wrong service — it is optimizing the wrong cost. The encode is paid once; the egress is paid on every view. So the right encoding strategy for a title depends on how many times it will be watched.

Remember that egress is what the CDN charges to send bytes out to viewers, and it scales with file size times views — the single biggest recurring cost in streaming, unpacked in CDN cost engineering: egress, commits, and the 95th percentile. Take a tentpole title that will be streamed a million times. Spending more compute up front — a slow CPU encode, the efficient HEVC or AV1 codec, even per-title tuning — to shave 20% off the file size pays back a million times over in egress. Netflix reported in December 2025 that AV1 carries about 30% of its on-demand viewing and that AV1 sessions use about a third less bandwidth than its H.264 and HEVC sessions. For that title, you optimize bytes, and the encode cost barely registers against the delivery savings.

Now take a long-tail title that may be watched a hundred times, or never. Spending the same expensive compute to shrink it is money you will likely never recover, because there is almost no egress to save. For that title, you optimize the encode — fast GPU, a lighter preset — and accept a slightly larger file, because the bytes will rarely move. This is the same logic that drives per-title and context-aware encoding, and it is why a single farm strategy for the whole catalog leaves money on the table. The popular head wants the CPU; the long tail wants the GPU; the platform wants both, routed by predicted demand. All of it feeds the same bottom line in the OTT cost model.

Figure 4. Let view count route the encode. A title bound for a million views earns expensive, byte-efficient CPU compression; a long-tail title that may never be watched gets fast, cheap GPU throughput instead.

Live transcoding is a separate, per-hour bill

Everything above prices VOD. Live is the other shape, and it is worth a number so the contrast is concrete. A managed live encoder such as AWS Elemental MediaLive bills per hour the channel runs. In AWS's own US East example (November 2026), each 1080p or 720p H.264 output costs $0.702 per hour and each SD output $0.354. A channel with two HD HEVC inputs, a five-rung ladder and advanced audio totals $3.942 per hour as a standard two-pipeline channel, or $2.37 per hour on a single pipeline. Run around the clock, that is roughly $1,700 a month single-pipeline or $2,840 a month with redundancy, for the encode alone, before storage or CDN.

The point is not the exact figure — it is the shape. VOD compute scales with how much content you have; live compute scales with how many channel-hours you run, busy or idle. A platform that runs both needs to budget them separately, because a quiet live channel still costs a running machine while a quiet VOD library costs nothing to encode.

Choosing the farm: a comparison

Table 1. The four farm approaches by cost shape and fit. The "best when" column is the decision: match the cost model to your volume pattern and your audience's view distribution, not to a vendor's brand. CPU saves egress; GPU saves time; managed saves ops; on-prem saves money only when it never sits idle.

A common mistake: pricing the farm on encode cost alone

The most expensive transcoding decisions come from comparing the wrong numbers. Three versions of the same error:

The first is choosing a service by its per-minute headline and forgetting the meter runs on output. A "cheap" $0.0150-per-minute HD rate becomes ten dollars a source hour once a six-rung ladder multiplies it — the ladder, not the rate, sets the bill.

The second is self-hosting too early. A team encoding 50 hours a month builds a spot-instance fleet to "save money," then spends more engineer-time babysitting spot interruptions than the entire managed bill would have cost. Below the break-even, the managed service is both cheaper and simpler; self-host when volume crosses the line, not before.

The third, and costliest, is optimizing encode when you should optimize egress, or the reverse. Running every title through a slow, multi-pass, top-codec encode "for quality" burns compute on long-tail content nobody will stream enough to justify it; running the whole catalog through the fastest GPU preset "for speed" inflates the file size of your most-watched titles and pays for it in egress a million times over. The fix is to let predicted demand route the title: bytes-first for the head, encode-first for the tail.

Where Fora Soft fits in

Sizing and building a transcoding farm that holds its cost as a catalog grows — picking managed cloud versus a self-hosted spot fleet at the right break-even, routing popular titles to byte-efficient CPU encodes and the long tail to fast GPU throughput, and keeping the live and VOD pipelines budgeted apart — is the streaming engineering Fora Soft has done since 2005, across 250+ shipped projects in video streaming, OTT/Internet TV, e-learning, telemedicine, and video surveillance. The work is exactly this: building the encode pipeline so its compute bill scales with the audience instead of outrunning it, and so every title is encoded the way its viewership justifies. When a media company needs a streaming platform whose transcoding cost stays sane from the first thousand hours to the first million, that pipeline engineering is the capability we bring. It sits at the core of our video and audio processing development work and of the OTT and IPTV platforms we build around it, and of the wider video streaming software development projects where live and VOD share one farm.

The handbook

Fora Soft Video Engineering Handbook

We wrote a seven-volume handbook, 9,000+ pages, that takes the video stack from a single pixel to production, and someone who works through it can size and build the transcoding farm this article prices. Volume 1 sits closest to this page: what the x264, x265 and hardware encoders actually trade, why a slow preset buys a smaller file, and how the ladder multiplies every encode. Volume 3 follows those bytes to the CDN and the egress bill that decides which titles earn the slow encode, and volume 2 covers the ingest and HLS/DASH packaging the farm hands its renditions to. Written by Nikolay Sapunov, CEO at Fora Soft.

Browse the handbook on Amazon →7 volumes · Kindle & paperback

See our case studies

References

1. AWS Elemental MediaConvert pricing — Amazon Web Services (2026). Per-normalized-minute output billing. Basic tier $0.0075/min (first 100,000 min, then $0.0053), AVC single-pass multipliers SD 1×, HD 2×, 4K 4×. Professional tier $0.0120/min (first 50,000 min); HD multipliers AVC single-pass 2×, AVC multi-pass 3.5×, HEVC single-pass 4×, HEVC multi-pass 7×, AV1 7×. US East. Dated vendor pricing, re-verify. Tier 3 (first-party vendor). https://aws.amazon.com/mediaconvert/pricing/ (accessed 2026-11-06)
2. Google Cloud Transcoder API pricing — Google Cloud (2026). Per-minute-of-output by resolution class: SD (<720p) $0.015/min, HD (720p–1080p) $0.030/min, UHD/4K $0.060/min; audio-only $0.005/min; auto-subtitles $0.50/subtitle/min; opt-in 1-year and 3-year savings plans (HD $0.027 and $0.024/min). Billed per output stream, so renditions multiply. Dated vendor pricing, re-verify. Tier 3 (first-party vendor). https://cloud.google.com/transcoder/pricing (accessed 2026-11-06)
3. AWS Elemental MediaLive pricing — Amazon Web Services (2026). Live encode billed per channel-hour. Example one (US East, on-demand, standard channel): two 1080p HEVC inputs $0.588/hr each, 1080p and 720p AVC outputs $0.702/hr each, three SD AVC outputs $0.354/hr each, advanced audio $0.300/hr, total $3.942/hr; same configuration on a single-pipeline channel $2.3652/hr. Monthly figures in the article are 720-hour multiples of these. Tier 3 (first-party vendor). https://aws.amazon.com/medialive/pricing/ (accessed 2026-11-06)
4. Run open-source FFmpeg at lower cost on VT1 instances for VOD encoding — AWS Open Source Blog (2024). Purpose-built media-accelerator instances (Xilinx U30) deliver higher encode density and lower cost per stream than general CPU for VOD H.264/HEVC; reference architecture for a self-hosted farm. Tier 3 (first-party engineering). https://aws.amazon.com/blogs/opensource/run-open-source-ffmpeg-at-lower-cost-and-better-performance-on-a-vt1-instance-for-vod-encoding-workloads/ (accessed 2026-06-16)
5. How to run FFmpeg on AWS Spot Instances for scalable, low-cost video processing — IMG.LY (2026). Spot capacity at a deep discount to on-demand (≈ $0.10 vs ≈ $0.40/hr on a representative instance); two-minute interruption notice; chunk jobs for resilience. The economics of a self-hosted spot fleet. Tier 5 (industry engineering blog). https://img.ly/blog/how-to-run-ffmpeg-on-aws-spot-instances-for-scalable-low-cost-video-processing/ (accessed 2026-06-16)
6. Use Spot Instance pricing for video encoding workflows (Bitmovin containerized encoding) — AWS Startups Blog. Worked example: a 1,024-core encoding cluster at ~$51/hr on-demand versus ~$15/hr on spot (~$36.7k vs ~$9.3k/month) for equal throughput; spot fits interruptible VOD encoding. Tier 3 (first-party engineering). https://aws.amazon.com/blogs/startups/use-spot-instance-pricing-for-your-video-encoding-workflows-with-bitmovin-containerized-encoding/ (accessed 2026-06-16)
7. NVIDIA Video Codec SDK — encoding benchmarks (Ada, July 2023) — NVIDIA (2023). NVENC throughput (1080p up to ~800 fps; 4K up to ~200 fps) and multi-session density per chip; quality near x264 "medium" on live-action with measured VMAF. The GPU speed/density case. Tier 3 (first-party engineering). https://developer.download.nvidia.com/designworks/video-codec-sdk/Video-Benchmark-Ada-July-2023.pdf (accessed 2026-06-16) Ada-generation figures; see ref 12 for Blackwell.
8. NVENC vs CPU encoding — VMAF analysis — Igor's Lab (2023). Independent VMAF comparison: modern NVENC ≈ x264 medium on most content but falls behind slow CPU presets on fine detail, grain, and animation; CPU keeps the compression-efficiency edge that lowers egress. Tier 5 (independent benchmark). https://www.igorslab.de/en/nvidias-nvenc-vs-cpu-encoding-the-turing-video-encoder-for-twitch-streaming-co-comparison-analysis-with-netflix-vmaf/ (accessed 2026-06-16)
9. Per-Title Encode Optimization — Netflix Technology Blog. The reference case for tuning encode effort to content (and, by extension, to demand): spend compute where the bytes will be delivered enough to repay it. Anchors the encode-cost-vs-lifetime-egress argument. Tier 4 (first-party engineering). https://netflixtechblog.com/per-title-encode-optimization-7e99442b62a2 (accessed 2026-06-16)
10. Spot Instance interruption notices — Amazon EC2 User Guide (2026). "A Spot Instance interruption notice is a warning that is issued two minutes before Amazon EC2 stops or terminates your Spot Instance." Tier 3 (first-party documentation). https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-instance-termination-notices.html (accessed 2026-11-06)
11. Amazon EC2 Spot Instances — Amazon Web Services (2026). Spot capacity "available at up to a 90% discount compared to On-Demand prices." Tier 3 (first-party vendor). https://aws.amazon.com/ec2/spot/ (accessed 2026-11-06)
12. NVIDIA Video Codec SDK 13.0 powered by NVIDIA Blackwell — NVIDIA Technical Blog (February 2025). Blackwell NVENC adds 4:2:2 H.264/HEVC encoding, 10-bit H.264, interlaced H.264, AV1 UHQ tuning with seven B-frames instead of five, and MV-HEVC; quality gain over Ada described as significant, charted but not given as a single figure. Tier 3 (first-party engineering). https://developer.nvidia.com/blog/nvidia-video-codec-sdk-13-0-powered-by-nvidia-blackwell/ (accessed 2026-11-06)
13. AMD Launches First 5nm ASIC-based Media Accelerator Card — AMD press release (April 2023). Alveo MA35D: up to 32 1080p60 streams per card, about 1 W per stream (35 W typical), 50 W TDP; AV1, HEVC and H.264. Tier 3 (first-party vendor). https://ir.amd.com/news-events/press-releases/detail/1122/amd-launches-first-5nm-asic-based-media-accelerator-card-to-power-new-era-of-interactive-media-services-at-scale (accessed 2026-11-06)
14. Netflix Says AV1 Codec Now Powers 30% of Netflix VOD Streaming — StreamingMediaBlog, summarising the Netflix Technology Blog (December 2025). AV1 carries about 30% of Netflix on-demand viewing; AV1 sessions use about one-third less bandwidth than AVC and HEVC and see 45% fewer rebuffering interruptions. Tier 4 (first-party data via trade press). https://www.streamingmediablog.com/2025/12/netflix-av1.html (accessed 2026-11-06)
15. AWS EC2 g6.xlarge: Spot, On-Demand and Savings Plan pricing in us-east-1 — DoiT Spot pricing tracker (2026). g6.xlarge (1× NVIDIA L4) On-Demand $0.8048/hr; Spot by availability zone $0.4347–$0.7697/hr when checked. Spot prices move daily. Tier 5 (third-party price tracker). https://www.doit.com/compute/spot/us-east-1/g6.xlarge (accessed 2026-11-06)

Source note (per §4.3.2): this is a cost/architecture article, so the controlling sources are first-party vendor pricing pages and documentation (refs 1–4, 6, 10, 11, 13) and first-party encoder benchmarks and release notes (refs 7, 12), dated because vendor prices and chip capabilities change. Codec mechanics are deliberately linked out to Video Encoding rather than re-derived. Independent benchmarks (ref 8) and industry cost breakdowns and price trackers (refs 5, 15) are used for orientation and cross-check, and ref 14 reports Netflix's own published figures; the break-even figures are presented as order-of-magnitude planning ranges, not guarantees. No standards claim is made that would require a tier-1 spec; where one is touched (codec behavior) the article points to the section that owns it.