Cost Modeling for Hosting AI Workloads: From Spot GPUs to Dedicated Nebius-Style Offers

Cost Modeling for Hosting AI Workloads: From Spot GPUs to Dedicated Nebius-Style Offers

Hook: If you manage ML infra or run heavy model training, you know the bills can be unpredictable: hidden egress charges, ballooning SSD costs, and spot interruptions that turn a cheap run into an expensive retry. This guide gives you a spreadsheet-ready cost model that compares spot, on-demand, and specialized AI clouds (Nebius-style) across per-hour and per-job metrics — including realistic line items for storage (PLC SSDs), data transfer, and interruption risk.

Executive summary — what you'll get

• A reusable, copy-paste spreadsheet model (formulas) for per-hour and per-job cost estimation.
• Practical sample inputs for spot, on-demand, and dedicated AI cloud offers with risk adjustments for spot eviction and restart cost.
• Storage and data-transfer considerations, including PLC SSD pricing trends (SK Hynix developments in 2025–2026).
• Working examples and three scenario outputs so you can plug in your own rates and get TCO in minutes.

Why this matters now (2026 context)

Late 2025 and early 2026 saw two converging trends that change how firms should model AI infra costs:

• Specialized AI clouds and ‘neoclouds’ — companies like Nebius have pushed bundled offers that include high-density GPU nodes, optimized NVMe tiers, and managed networking. Those offers often trade lower volatility and better networking for longer-term price commitments.
• PLC SSD progress — SK Hynix and others accelerated PLC (5-bit cell) viability, pressuring SSD $/GB down and enabling cheaper high-capacity NVMe tiers in 2026. That lowers dataset storage costs but changes endurance and performance trade-offs.

Practical takeaway: the cheapest per-hour GPU price isn't always the lowest per-job TCO once storage, transfer, eviction risk, and checkpointing overhead are included.

Model structure — what to include in a per-job cost

Every job’s total cost should sum these line items:

1. GPU compute cost — per-GPU-hour * hours consumed (account for spot discount or dedicated flat)
2. CPU/auxiliary VM cost — per-hour cost for CPU, memory, and software
3. Storage cost — persistent dataset and checkpoint storage (PLC NVMe or standard SSD) pro-rated by job duration
4. Data transfer (egress/ingest) — GB moved * $/GB (remember intra-region may be free but egress often expensive)
5. Operational & management fees — monitoring, orchestration, or provider managed-service fees
6. Risk and retry overhead — expected cost from spot eviction or preemption

Key risk factors to model explicitly

• Spot eviction probability (p) — multiply by expected wasted work and restart cost.
• Checkpoint frequency & overhead — how much work you can discard on eviction.
• Data egress pattern — how often you export large model checkpoints or datasets off-cloud.
• Storage durability and I/O limits — PLC SSDs are cheaper but may have lower write endurance; model re-writes for frequent checkpoints.

Spreadsheet-ready model (copy into Google Sheets / Excel)

Below is a compact model you can paste into a sheet. The left column lists input variables (editable). The right column gives sample values (replace with your prices). Formulas reference the input cells using standard Excel/Sheets syntax (assume inputs are in column B starting at B2). After the inputs, computed outputs show per-hour and per-job costs.

Inputs (place in A2:A20) | Value (place in B2:B20)
-----------------------------------------------------------------
GPU type                     | H100-80GB (label)
On-demand GPU $/GPU-hour     | 25.00     (B3)
Spot discount (fraction)     | 0.60      (B4)  -> implies spot price = B3 * (1 - B4)
Spot eviction probability    | 0.10      (B5)
Avg progress lost on eviction (fraction) | 0.5  (B6)
Dedicated (Nebius-style) $/GPU-hour (flat) | 10.00 (B7)
CPU/VM $/hour                | 1.50      (B8)
Storage size (GB)            | 1000      (B9)
PLC SSD $/GB-month (forecast) | 0.020     (B10)    -> $20/TB-month
Storage months used (pro-rata) | 0.1     (B11)     -> 3 days ~= 0.1 month
Ingress GB                   | 200       (B12)
Egress GB                    | 50        (B13)
Egress $/GB                  | 0.05      (B14)
Mgmt/service fee per-job $   | 5.00      (B15)
Job GPU-hours required       | 100       (B16)
Checkpoint write per hour (GB) | 2       (B17)
Write endurance cost multiplier (PLC extra writes $/GB) | 0.0001 (B18)

Computed
-----------------------------------------------------------------
On-demand GPU price $/hr    | =B3
Spot GPU price $/hr        | =B3*(1-B4)
Expected extra GPU-hours from eviction | = B16 * B5 * B6
Expected GPU-hours (spot, accounting for retries) | = B16 + (B16 * B5 * B6)
GPU compute cost (on-demand) | = B3 * B16
GPU compute cost (spot, expected) | = (B3*(1-B4)) * (B16 + (B16 * B5 * B6))
GPU compute cost (dedicated) | = B7 * B16
CPU cost (per job)          | = B8 * (B16 / 1)   # assume 1 CPU-hour per GPU-hour
Storage cost (per job)      | = B9 * B10 * B11
Checkpoint storage write cost | = B17 * B16 * B18
Data transfer cost (per job) | = B13 * B14 + B12 * 0  # assume ingress free
Total cost (on-demand)      | = GPU compute cost (on-demand) + CPU cost + Storage cost + Checkpoint storage write cost + Data transfer cost + B15
Total cost (spot)           | = GPU compute cost (spot, expected) + CPU cost + Storage cost + Checkpoint storage write cost + Data transfer cost + B15
Total cost (dedicated)      | = GPU compute cost (dedicated) + CPU cost + Storage cost + Checkpoint storage write cost + Data transfer cost + B15

Sample outputs (with provided sample inputs)
On-demand GPU price $/hr = 25.00
Spot GPU price $/hr = 10.00
Expected extra GPU-hours from eviction = 5.00
Expected GPU-hours (spot) = 105.00
GPU compute cost (on-demand) = 2,500.00
GPU compute cost (spot, expected) = 1,050.00
GPU compute cost (dedicated) = 1,000.00
CPU cost (per job) = 150.00
Storage cost (per job) = 20.00
Checkpoint storage write cost = 20.00
Data transfer cost (per job) = 2.50
Mgmt fee = 5.00
Total on-demand = 2,697.50
Total spot = 1,247.50
Total dedicated = 2,197.50
  

How to use the sheet

• Replace the sample input values (column B) with actual vendor prices.
• For spot models, set Spot eviction probability to your observed/SLI value (0.05–0.3 typical depending on provider and GPU family).
• Adjust Avg progress lost on eviction based on checkpointing cadence (e.g., hourly checkpoints -> loss ~ fraction between checkpoints).
• For Nebius-style offers, replace the dedicated $/GPU-hour with the provider's flat hourly or monthly node price pro-rated to per-GPU-hour.

Detailed considerations and formulas

Spot instances — expected-cost formula

Spot (preemptible) instances are attractive but require modeling of retries. Use this expected-cost approach:

Expected GPU-hours (spot) = job_gpu_hours * (1 + p * loss_fraction)

Where p is eviction probability and loss_fraction is the average fraction of job work lost on eviction. Then:

Expected spot compute cost = spot_price_per_hour * Expected GPU-hours (spot)

Note: if your job is restartable and you checkpoint frequently, loss_fraction can approach 0.1–0.2. For long-running large-batch distributed training, loss_fraction may be 0.5 or higher.

On-demand and reserved pricing

On-demand is straightforward: price * hours. For longer-term projects, include reserved/committed discounts. To model reserved instances:

1. Pro-rate reserved instance upfront cost across expected usage hours in the commitment period.
2. Use the lower hourly committed rate for compute per-hour during the commitment.

Dedicated (Nebius-style) offers

Nebiuses and similar providers often price by node (multi-GPU) with included network/storage quality. Two modeling approaches:

• Per-GPU-hour equivalent: divide the node price by number of GPUs and hours to get $/GPU-hour.
• Per-node allocation: if your jobs require whole nodes, model the full node price per-job (less convenient for multi-tenant workloads).

Include the provider’s guaranteed networking (RDMA, NVLink), which often reduces job time and can materially lower per-job cost even if per-hour is similar.

Storage and PLC SSDs

PLC (Page-Level Cell / 5-bit cell) flash reduces $/GB but brings endurance and potential I/O performance trade-offs. In 2025–2026 SK Hynix announced process improvements that make PLC SSDs feasible for large-capacity dataset hosting; this trend is expected to put downward pressure on NVMe pricing across clouds in 2026.

When modeling storage:

• Use per-GB-month for persistent dataset storage: storage_cost = size_GB * $/GB-month * months_used
• Model checkpoint I/O costs separately if you write many small checkpoints — PLC endurance may increase replacement costs or force the use of higher-tier SSDs.
• Consider storage IOPS and throughput if training on large datasets: lower-cost PLC tiers may throttle streaming performance and increase job time (hidden CPU/GPU costs).

Practical PLC input recommendations (2026)

• PLC $/GB-month: use $0.010–$0.030 (i.e., $10–$30/TB-month) as an optimistic 2026 range — verify with your provider.
• If heavy write workloads (frequent checkpointing), add a write-endurance reserve cost: estimated writes_GB * replacement_cost_per_GB.

Data transfer: the silent bill driver

Data transfer (especially egress) is often overlooked. Typical patterns that spike costs:

• Frequent exports of large model checkpoints (multi-GB to TB).
• Cross-region dataset copies or serving model outputs to users outside the provider region.

Model it explicitly:

Data transfer cost = egress_GB * $/GB (ingress is often free).

Reduce egress costs

• Cache models in the same region as consumers.
• Compress checkpoints (use quantization where acceptable).
• Use provider peering or direct connect to avoid public egress.

Real-world example (anonymized)

We ran the model against a 100-GPU-hour fine-tune job for a medium LLM in late 2025. Inputs:

• On-demand H100-equivalent: $28/hr
• Spot discount: 65% → spot = $9.8/hr
• Spot eviction p=0.12; avg loss_fraction=0.4 (checkpoint every 2 hours)
• Dataset: 1.5 TB stored on PLC tier at $18/TB-month → ~$27/month => pro-rated to $0.09 for the job
• Egress: 100 GB at $0.06/GB = $6

Result (approx):

• Total on-demand cost: ~$2,900
• Total spot expected cost: ~$1,350
• Total dedicated (Nebius-style negotiated node) cost: ~$2,200 — lower than on-demand because node networking cut training time by ~15%

Lesson: the fastest-per-hour option (on-demand) can be more expensive per-job once you model time-to-results and network speed. The dedicated node’s lower wall-clock time changed the calculus.

Advanced strategies to minimize TCO

• Hybrid strategy: run large, resilient workloads on dedicated nodes and ephemeral hyperparameter searches on spot pools.
• Smart checkpointing: checkpoint incrementally and push older checkpoints to cheaper cold PLC/archival tiers — model the retrieval cost.
• Spot bidding and buffer pools: keep a small committed pool of on-demand GPUs to act as failover and reduce wasted work;
• Network-optimized clusters: where cross-GPU gradient exchange matters, pay a premium for RDMA/NVLink-enabled clusters to reduce per-job wall-clock time.
• Negotiate dedicated node SLAs: if you can predict demand, ask Nebius-style vendors for committed monthly node blocks with built-in egress discounts and NVMe tiers.

Future predictions (2026 and beyond)

• PLC adoption will continue — as SK Hynix and others refine PLC yields, expect $/GB to fall further. This will change dataset placement strategies; cold datasets will move to PLC-heavy tiers.
• Bundled AI offerings rise — specialized clouds will offer node+storage+network bundles with predictable billing and developer-friendly SLAs (beneficial for predictable TCO).
• FinOps for ML will mature — expect more vendor tooling offering per-job cost backfills and auto-tuning to select spot vs dedicated automatically. See examples of cost-risk frameworks that teams are adopting.

Checklist for an accurate TCO

• Record historical spot eviction rates per instance family and region.
• Measure wall-clock speedups from network-optimized nodes.
• Track checkpoint size and write frequency to estimate PLC endurance costs.
• Audit data egress patterns and negotiate peering where egress is material.
• Include all provider fees and managed-service surcharges (not just compute). Use a tool-sprawl audit to make sure you’re not paying for redundant services.

Actionable next steps — get this model into your pipeline

1. Copy the spreadsheet block above into a Google Sheet or Excel file.
2. Replace the sample input values with your vendor prices and observed eviction/throughput numbers.
3. Run three scenarios: conservative (all on-demand), aggressive (spot-first), and hybrid (dedicated + spot mix). Compare per-job and per-month TCO.
4. Use the results to inform procurement: ask Nebius-style providers for node pricing that beats your on-demand+network+egress combined cost.

Closing recommendations

Short-term projects and hyperparameter sweeps: favor spot with aggressive checkpointing and a small on-demand fallback pool. Large-scale training or productionized retraining loops: model the real wall-clock time advantages of dedicated Nebius-style nodes, and include network/throughput and storage tiering in the model. In 2026, lower PLC SSD prices will make dataset hosting cheaper — but verify endurance and expected throughput for your workload before migrating cold checkpoints to PLC tiers.

Final note: this guide gives you the scaffolding; the outputs will only be as good as the inputs. Replace the sample numbers with your observed prices and run sensitivity analysis on eviction probability and checkpoint loss fraction to see where your risk threshold lies.

Call to action

Download our ready-to-use spreadsheet template and per-job cost calculator at webhosts.top (or paste the model above into a sheet now). If you'd like a tailored TCO assessment for your workloads — share a short summary of your GPU hours, dataset sizes, and preferred providers, and our team will produce a one-page cost-optimization plan with recommended spot/dedicated mixes and projected 12-month TCO.

Related Reading

• ByteCache Edge Cache Appliance — field review (2026)
• Carbon-Aware Caching: Reducing Emissions Without Sacrificing Speed
• Tool Sprawl Audit: Practical Checklist for Engineering Teams
• News Brief: EU Data Residency Rules and What Cloud Teams Must Change in 2026
• Makeup for Mental Health Days: Self-Care Routines Creators Can Share That Are Monetizable and Supportive
• EdTech Stack Checklist: Which Tools to Keep, Replace, or Ditch
• QA Checklist to Kill AI Slop in Your Email Copy
• 5 Starter Projects for Raspberry Pi 5 + AI HAT+ 2 (Code, Models, and Templates)
• How Casting Changes Could Create Deals on Streaming Hardware