Colocation vs Cloud GPU 2026: TCO Comparison and Break-Even Guide
One 64xH100 cluster saves $2.2 million annually when moved from cloud rental to colocation, and every input is checkable. 64 H100s at the $6.52 median published on-demand list rate, running at 85% utilization for 12 months, cost $3,107,067. The same cluster colocated costs $951,819: hardware at $36,125 a GPU amortized over three years is $770,667 a year, and 81.6 kW of rack at $185/kW/month all-in adds $181,152. The delta is $2,155,248, a 69% reduction.
Cloud GPUs solve a real problem: instant capacity, zero upfront capital, and no operational burden. For prototyping, burst workloads, and early-stage experimentation, cloud rental is the right tool. But for any team running sustained workloads past 11 months, the economics flip. The cloud premium that buys you flexibility becomes a recurring tax you pay indefinitely. Cloud wins if your commitment horizon is under 11 months or utilization stays below 26%. Inside 11 months the $2,312,000 of hardware has not paid itself back. Below 26% utilization it never does inside the 36-month amortization, because the colocated cluster bills the same whether the GPUs run flat out or sit idle.
Not recommended if: your team lacks a dedicated ops engineer, your workload peaks are unpredictable, or your GPU model requirements change quarterly. In those cases, cloud flexibility outweighs the 69% saving the table below shows.
This guide gives you the full picture: side-by-side TCO tables, the five hidden cloud costs most teams miss, a decision tree for choosing the right deployment model, and details on how GYGO Place and GYGO Invest make colocation accessible. For GPU-by-GPU benchmarks and pricing, see the 2026 GPU Showdown guide.
Side-by-Side TCO: Cloud vs Colocation (64xH100, 12 Months)
TL;DR — TCO Comparison
A 64xH100 cluster at 85% utilization costs $3.1M a year on cloud on-demand at the $6.52 median list rate, versus $951,819 colocated, a 69% saving over 12 months. Amortized hardware is $770,667 of that colocation figure and rack space the other $181,152. The gap widens once you add egress fees and networking costs that sit outside cloud base rates.
The table below models a 64xH100 SXM5 cluster running at 85% utilization for 12 months — the most common production training configuration in 2026. All figures use real market prices.
| Cost Component | Cloud (On-Demand) | Colocation | Savings |
|---|---|---|---|
| Compute / Hardware | $3,107,067 | $770,667 (amortized 3yr) | $2,336,400 |
| Rack, power & cooling | Included | $181,152 | — |
| Total 12-Month TCO | $3,107,067 | $951,819 | $2,155,248 (69%) |
* 64x H100 SXM5 at 85% utilization over 12 months. Cloud uses the $6.52 median of six published on-demand list rates read 13 October 2026. Colocation hardware uses $36,125/GPU, a reseller-listed complete 8-GPU HGX H100 server at $289,000 divided by eight on 13 October 2026, amortized over 36 months. Rack is 81.6 kW at $185/kW/month all-in, a modelling assumption inside the published Ashburn range of $180–$300/kW that we take to cover power, cooling, cross-connect and remote hands. Networking egress and in-house operations staff sit outside both columns because neither has a published rate we can hold you to — both run against cloud, so the 69% gap is the conservative reading.
5 Hidden Costs of Cloud GPU Most Teams Miss
TL;DR — Hidden Cloud Costs
Cloud GPU bills hide egress fees ($0.08–0.12/GB), spot interruption penalties, inter-region latency costs, vendor lock-in premium renewals, and idle billing during maintenance windows. These five categories add to your nominal hourly rate — costs that colocation eliminates through flat-rate connectivity and dedicated hardware ownership.
Your cloud invoice shows a line item for GPU-hours. It rarely shows these five cost categories that add to your real cloud bill. Understanding them is the first step toward an accurate colocation vs cloud comparison. Published H100 list rates already run $3.99 to $12.29 per GPU-hour, a median of $6.52, so hidden costs stack on top of a number that is higher than most teams assume, and colocation break-even arrives faster than standard models predict.
Egress Fees ($0.08–0.12/GB)
Cloud providers charge for data leaving their networks. A 405B parameter model checkpoint can be 800 GB. Moving training data in and checkpoints out across a multi-month run can generate $100,000–$300,000 in egress fees that never appear in pre-sales estimates (at $0.09/GB outbound, per AWS/GCP standard rates). Colocation facilities typically offer 10–100 Gbps connectivity with flat monthly port fees — egress is not metered. Cloud vs colocation egress delta alone can justify migration for datasets exceeding 500 TB/year.Not recommended for colocation if your data transfer volume is under 50 TB/year — egress savings won’t materially affect break-even timing.
Spot Interruption Penalties
Spot GPU instances can be 60–80% cheaper than on-demand — but they are reclaimed with 2-minute notice. A distributed training job interrupted at hour 47 of a 60-hour run loses the entire compute cost of that iteration if checkpointing was not perfectly configured. The real cost includes developer time to rebuild state, failed experiment budgets, and schedule slippage. Colocated hardware is exclusively yours — no interruptions, ever. Cloud spot wins if your jobs are under 4 hours with checkpointing every 30 minutes; colocation wins for multi-day runs where interruption risk is unacceptable.
Multi-Region Latency & Bandwidth Costs
Large training clusters often span multiple availability zones or even regions for redundancy. Inter-zone traffic is billed at $0.01–0.02/GB and introduces 100–200ms latency penalties that reduce effective GPU utilization on AllReduce operations. A 512-GPU training run with poor locality can lose 15–25% of raw FLOPS to communication overhead. Colocation with InfiniBand NDR delivers 400 Gbps at sub-microsecond latency within a single rack. Cloud vs colocation effective throughput gap widens as cluster size grows beyond 64 GPUs — avoid colocation if your cluster stays below 8 GPUs, where interconnect density advantages are not yet cost-justified.
Vendor Lock-In Premium Pricing
Once your training pipeline, tooling, and data pipelines are optimized for a single cloud provider, switching costs become enormous. Providers price that in: reserved instance renewal quotes rise for locked-in customers, and the lock-in premium compounds quietly. Colocation with bare-metal hardware and standard networking gives you full portability — take your GPUs to a different facility at any time, or sell them on the secondary market. Not recommended if you rely heavily on proprietary cloud ML services (managed training, AutoML, feature stores) that have no colocation equivalent — migration cost to self-managed equivalents can negate first-year colocation savings.
Idle Billing During Maintenance Windows
Cloud providers bill for instances even during provider-side maintenance, hypervisor updates, and live migrations that pause your workload. At scale, scheduled and unscheduled downtime can add 2–5% to your effective hourly rate while delivering zero useful compute. Reserved instances offer no refunds for provider-caused interruptions. With colocated hardware, you own the maintenance schedule — plan downtime when it fits your training runs. Cloud wins if your SLA requirements are below 99.5% uptime, since cloud managed infrastructure handles patching at no ops cost; colocation wins if you need >99.9% uptime with full control over maintenance windows.
Cloud vs Colocation: Decision Framework
TL;DR — Decision Framework
Choose cloud for workloads under 11 months, utilization below 26%, or when you need burst capacity without ops overhead. Choose colocation when workload duration exceeds 11 months, utilization tops 70%, and you have a fixed GPU model commitment. Break-even for a 64xH100 cluster lands at month 11 at 80% utilization, month 9 at 90%, month 12 at 70%, and month 17 at 50%.
The right infrastructure choice depends on your workload profile, capital flexibility, and time horizon. Use this framework to identify where you sit on the colocation vs cloud spectrum. Cloud wins under 11 months; colocation wins past month 11 at 80% utilization and above. Between 50% and 70% your utilization moves the answer: payback slides to month 12 at 70% and month 17 at 50%, both still inside the 36-month amortization.
- ✓Workload duration < 11 months. The hardware has not paid itself back yet, so cloud flexibility costs you nothing.
- ✓Utilization < 26%. Idle colocated hardware still costs money. Cloud scales to zero when idle.
- ✓GPU model uncertainty. You might need H100 today and MI300X in 3 months. Cloud lets you switch.
- ✓No ops team. Cloud managed services eliminate hardware management overhead.
- ✓Burst capacity. You need 512 GPUs for one week and can't justify ownership. Compare GPU rental options →
Cloud wins if commitment <11 months, utilization <26%, or team size <3 engineers, where ops overhead exceeds savings at small scale.
- ✓Workload duration > 11 months. Break-even lands at month 11 at 80% utilization and month 9 at 90%. Every month after is pure savings.
- ✓Utilization > 70%. High utilization maximizes return on hardware investment.
- ✓Fixed GPU model commitment. You know you need H100s or MI300Xs for the foreseeable future. Buy hardware via GYGO →
- ✓Egress-heavy workloads. Flat-rate connectivity saves $100K+/year on large datasets.
- ✓Compliance or data sovereignty. Dedicated hardware in a specific jurisdiction meets regulatory requirements cloud multi-tenancy cannot.
Avoid colocation if utilization <26%, commitment <11 months, or power rate >$0.15/kWh. Any one of the three inverts the TCO math against you.
The Break-Even Rule of Thumb
For H100 clusters: if your cloud GPU spend exceeds $50,000/month for more than 11 months, the numbers almost certainly favor colocation. Run the same arithmetic against your actual cluster size and utilization to get your specific break-even month. Then contact GYGO Place for a facility match and real quotes. Break-even moves from month 12 at 70% utilization to month 11 at 80% and month 9 at 90%, and a good deal faster below 70%, so skip the rule of thumb if your utilization swings by more than ±20%.
The break-even formula: Months to break-even = Hardware purchase cost ÷ (Monthly cloud spend × utilization rate − Monthly colocation TCO). Inputs: hardware purchase at $36,125 per H100, a reseller-listed complete 8-GPU HGX H100 server at $289,000 divided by eight on 13 October 2026, amortized over 36 months, and rack at $185/kW/month all-in, a modelling assumption inside the published Ashburn range that we take to cover power, cooling, cross-connect and remote hands. Networking egress and in-house operations staff are excluded from both sides, because neither has a published rate we can hold you to. Avoid colocation if break-even > 24 months — hardware obsolescence risk outweighs savings beyond that horizon.
How GYGO Makes Colocation Accessible
TL;DR — GYGO Services
GYGO Place connects teams with partner colocation facilities built for high-density GPU racks, delivering comparable quotes within one business day. GYGO Invest lets you purchase GPU hardware deployed in those same facilities and earn monthly revenue share. Both services eliminate the data center relationships, upfront capital barriers, and operational complexity that historically made colocation inaccessible.
Moving from cloud to colocation has historically required deep data center relationships, operational expertise, and large upfront capital commitments. GYGO removes each of these barriers through two complementary services.
GYGO Place
GYGO Place connects you with partner colocation facilities built for GPU clusters. High-density colocation is on the market: Equinix announced plans in December 2023 to support direct-to-chip liquid cooling in more than 100 of its data centers across more than 45 metros, CyrusOne publishes cabinet configurations up to 300 kW, and Colovore advertises 5 to 600+ kW racks. GYGO sources facilities with direct liquid cooling, InfiniBand interconnect, and remote hands.
- →Submit your cluster requirements once — receive competitive quotes from multiple facilities
- →Filter by location, power density, cooling type, interconnect, and contract flexibility
- →Phased migration support: run cloud and colocation in parallel during cutover
- →Facilities support H100, H200, MI300X, GB200, and RTX 4090 cluster configurations
GYGO Invest
Not ready to purchase an entire GPU cluster? GYGO Invest lets you purchase GPU hardware that is deployed in partner colocation facilities and leased to AI companies. You earn monthly revenue share based on actual utilization, with the operator reporting your hardware’s utilization.
- →Purchase individual GPUs or full cluster configurations starting at single-unit minimums
- →Utilization and revenue reporting from the operator
- →Hardware deployed in the same partner facilities
- →Returns tied to actual GPU utilization, not synthetic yield promises
Migration Timeline: Cloud to Colocation
A cloud-to-colocation migration under GYGO’s phased approach keeps training running from checkpoints through cutover. Brokers put the whole arc at 4–6 months for mid-market deployments (Metro Colo Advisory, June 2026), most of it hardware lead time:
- 1Week 1–2: GYGO Place facility selection and contract signing
- 2Week 2–3: Hardware procurement via GYGO Buy partners (or ship your own)
- 3Week 3–4: Hardware racking, networking, and software environment setup
- 4Week 4–6: Parallel operation — run cloud and colocation simultaneously
- 5Week 6–8: Traffic cutover and cloud instance wind-down
- 6Month 2+: Full cost savings realized, cloud spend eliminated
Frequently Asked Questions: Colocation vs Cloud GPU ROI
TL;DR — FAQ Summary
Colocation runs about 69% below cloud GPU for sustained workloads. Break-even arrives at month 11 for H100 clusters at 80% utilization and month 9 at 90%. Upfront costs run $2.33M–$2.37M for 64xH100, almost all of it complete 8-GPU servers. GYGO’s phased approach runs cloud and colocation in parallel during cutover, so training jobs resume from checkpoints rather than restarting.
What is the typical cost difference between cloud GPU and colocation in 2026?
Colocation runs about 69% below cloud GPU for sustained workloads. A 64xH100 cluster at the $6.52 median published on-demand list rate costs $3,107,067 a year at 85% utilization, against $951,819 colocated: $770,667 of amortized hardware at $36,125 a GPU over three years, plus $181,152 for 81.6 kW of rack at $185/kW/month all-in. That is a $2.2M annual saving, and it compounds as colocation contracts lock in fixed rates. The $36,125 per-GPU price is a reseller-listed complete 8-GPU HGX H100 server at $289,000 divided by eight, read 13 October 2026; the $185/kW/month rack rate is a modelling assumption inside the published Ashburn range of $180–$300/kW; the list rates were read 13 October 2026.
When does colocation make more financial sense than cloud GPU?
At 11+ months of sustained utilization above 70%. Break-even for a 64xH100 cluster against the $6.52 median published on-demand list rate lands at month 11 at 80% utilization, month 9 at 90%, month 12 at 70%, and month 17 at 50%. Work the same arithmetic against your own cluster size and utilization to find your exact break-even month. Cloud wins if your commitment runs under 11 months, or if utilization stays below 26%, the point where the colocated cluster costs the same as renting it for 36 months. Not recommended to commit to colocation if your workload is seasonal, because rack at $185/kW/month all-in accrues on idle hardware regardless of GPU activity.
What are the upfront costs of GPU colocation?
Hardware purchase ($36,125 per H100 SXM5 deployed, a reseller-listed complete 8-GPU HGX H100 server at $289,000 divided by eight on 13 October 2026), racking and network setup ($5K–15K one-time), and typically 1–3 months of prepaid colocation fees at $185/kW/month all-in. Total upfront for a 64xH100 cluster runs $2.33M–$2.37M, almost all of it the $2,312,000 of servers. Not recommended if available capital is under $500K — a minimum viable colocation cluster (8xH100) needs $289,000 of hardware plus setup. GYGO Place connects you with facilities that offer phased payment structures and shorter initial contract terms to reduce barrier to entry.
What hidden costs do cloud GPU bills include that colocation avoids?
Egress fees ($0.08–0.12/GB for data leaving the cloud, at standard AWS/GCP rates), spot interruption penalties when preemptible instances are reclaimed, multi-region latency costs for distributed training ($0.01–0.02/GB inter-zone), vendor lock-in premium pricing on contract renewals, and idle billing during provider maintenance windows. These hidden costs add to your nominal cloud GPU rate. Cloud wins despite hidden costs if total workload duration runs under 11 months, which is not enough time for hidden cost mitigation to justify colocation transition overhead.
How do I calculate my GPU colocation ROI?
Multiply your cloud hourly rate by GPU count, average utilization, and hours in the period to get cloud spend. Against that, total the colocation side: amortized hardware, power, and facility fees over the same months. The gap between the two is your saving, and the month where cumulative savings cover your upfront hardware outlay is your break-even. The TCO table above runs a 64xH100 cluster through that math line by line. For a custom quote accounting for your specific power density, networking, and contract terms, contact GYGO Place.
What power and cooling do I need for GPU colocation?
Modern GPU clusters require 20–100+ kW per rack depending on configuration. A single DGX H100 system draws approximately 10 kW; a full 8-GPU H100 rack draws 40–50 kW with networking. Colocation at that density is on the market: CyrusOne publishes cabinet configurations up to 300 kW per rack for its Intelliscale design, and Colovore advertises 5 to 600+ kW per rack. GYGO sources partner facilities built for those densities, with direct liquid cooling for H100 and GB200 clusters and rear-door heat exchangers for air-cooled configurations. GB200 NVL72 systems require specialized direct liquid cooling and 120 kW+ per rack.
Can I move from cloud GPU to colocation without downtime?
Yes, with proper planning. GYGO coordinates phased migration: provision colocation hardware first, replicate your training environment (Docker/Kubernetes configs, datasets, model weights), run cloud and colocation in parallel to validate identical outputs, then cut over production traffic and wind down cloud instances. Budget months, not weeks: Metro Colo Advisory (15 June 2026) puts a mid-market AI workload migration at 4–6 months from evaluation to production cutover, with facility selection and contract negotiation at 6–8 weeks and GPU hardware procurement at 12–16 weeks. Long-running training jobs are checkpointed and resumed on the new hardware.
Does GYGO offer both colocation placement and GPU hardware investment?
Yes. GYGO Place helps you find optimal data center facilities for your own GPU hardware — you own the hardware, GYGO connects you with the facility and manages the placement process. GYGO Invest is a separate service where you purchase GPU hardware that is deployed in partner facilities and leased to AI companies, with utilization reporting and monthly revenue share. See our full FAQ for more details.
More questions about GPU infrastructure? See our full FAQ →
Ready to Cut Your GPU Costs by 69%?
TL;DR — Next Steps
If your cloud GPU spend exceeds $50,000/month for more than 11 months, colocation economics almost certainly favor a switch. Run the TCO math above against your own cluster to confirm your break-even month, then contact GYGO Place for a facility match and comparable quotes within one business day — no commitment required to ask.
Work the TCO comparison above against your own numbers to see your potential savings, then get a custom colocation quote through GYGO Place. Our team matches your cluster requirements with partner facilities and delivers comparable quotes within one business day. Colocation savings compound as the hardware amortises: the facility bill stays flat while the rental bill repeats every month. Not recommended if your decision timeline is under 30 days — proper facility evaluation and contract negotiation requires at least 3–4 weeks to complete.
Not ready to commit? Compare cloud GPU rental options →