Colocation vs Cloud GPU 2026: ROI Calculator & Complete Savings Guide
One 64xH100 cluster saves $1.2 million or more annually when moved from cloud rental to colocation. That is not a marketing figure — it is straightforward arithmetic: 64 H100s at $3.00/hr cloud-on-demand, running at 85% utilization for 12 months, costs roughly $12.7 million. The same cluster colocated — hardware amortized over three years, power at $0.08/kWh, data center fees included — lands around $5.5 million. The delta is your annual savings.
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 high-utilization workloads beyond six months, the economics flip decisively. The cloud premium that buys you flexibility becomes a recurring tax you pay indefinitely. Cloud wins if your commitment horizon is under 12 months and utilization stays below 60% — below those thresholds, the capital cost of colocation does not amortize fast enough to beat cloud flexibility.
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 40–60% TCO premium.
This guide gives you the full picture: an interactive ROI calculator, 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.
GPU Colocation vs Cloud ROI Calculator
Enter your cluster details to see real 2026 cost comparisons. All calculations use on-demand cloud rates vs. GYGO colocation TCO.
* Colocation TCO uses 50% of cloud on-demand cost, representing typical hardware amortization, power, cooling, and data center fees for a sustained deployment. Savings range from 40–60% depending on cluster size and location. Individual results vary. Get a custom quote via GYGO Place →
Side-by-Side TCO: Cloud vs Colocation (64xH100, 12 Months)
TL;DR — TCO Comparison
A 64xH100 cluster at 85% utilization costs ~$13M on cloud on-demand versus ~$3.2M colocated — a 75% savings over 12 months. Cloud compute alone accounts for $12.7M versus $2.1M amortized hardware cost for colocation. The TCO gap widens with egress fees and networking costs excluded from 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 | $12,700,800 | $2,133,333 (amortized 3yr) | $10,567,467 |
| Power (kWh) | Included | $430,080 | — |
| Data Center Fees | Included | $384,000 | — |
| Networking / Egress | $180,000+ | $36,000 | $144,000 |
| Management / Ops | $120,000 | $200,000 | — |
| Total 12-Month TCO | ~$13.0M | ~$3.2M | ~$9.8M (75%) |
* Colocation hardware cost uses $32,000/GPU purchase price amortized over 36 months (at $1.20/kW DC power per industry average). Power at $0.08/kWh, 700W TDP per GPU, PUE 1.2. Data center fees at $800/kW/month, ~40kW draw per 8 GPUs. Egress at $0.09/GB with 200TB/month typical training traffic. Cloud egress not included in base rental rate. Colocation vs cloud savings compress significantly if power rates exceed $0.12/kWh or utilization drops below 60%.
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 (15–30% annually), and idle billing during maintenance windows. These five categories add 20–40% 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 can add 20–40% to your real cloud bill. Understanding them is the first step toward an accurate colocation vs cloud comparison. If hidden costs push your effective cloud rate above $4.50/hr per H100, 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 exploit this: reserved instance renewal prices increase 15–30% year over year for locked-in customers. 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 3 months, utilization below 50%, or when you need burst capacity without ops overhead. Choose colocation when workload duration exceeds 6 months, utilization tops 70%, and you have a fixed GPU model commitment. Break-even for H100 clusters typically falls at 4–8 months depending on utilization rate.
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 3 months; colocation wins beyond 6 months. The 3–6 month window is genuinely ambiguous — break-even depends heavily on utilization rate and negotiated power rates.
- ✓Workload duration < 3 months. Cloud flexibility outweighs TCO premium for short experiments.
- ✓Utilization < 50%. 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 <12 months, utilization <60%, or team size <3 engineers — ops overhead exceeds savings at small scale.
- ✓Workload duration > 6 months. Break-even typically at 4–8 months. 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 <50%, commitment <6 months, or power rate >$0.15/kWh — all three conditions invert 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 4 months, the numbers almost certainly favor colocation. Use the calculator above with your actual cluster size and utilization to get your specific break-even month. Then contact GYGO Place for a facility match and real quotes. Cloud vs colocation break-even shifts by 1–2 months for every 10% change in utilization rate — not recommended to use the rule of thumb if your utilization varies 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 ($32K/H100 at 2026 market prices), amortization over 36 months (at $1.20/kW DC power per industry average), power at $0.08/kWh, cooling overhead (PUE 1.2), and data center fees at $800/kW/month market rate. Networking and egress savings are computed separately and added to the colocation advantage. 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 vetted colocation facilities supporting up to 200 kW/rack — delivering competitive quotes within 48 hours. 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 vetted colocation facilities optimized for GPU clusters. Every facility in the GYGO network is pre-qualified for high-density power (up to 200 kW/rack), direct liquid cooling, InfiniBand interconnect, and 24/7 remote hands support.
- →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 vetted colocation facilities and leased to AI companies. You earn monthly revenue share based on actual utilization, with full transparency into your hardware’s performance metrics.
- →Purchase individual GPUs or full cluster configurations starting at single-unit minimums
- →Real-time utilization dashboard with daily revenue reporting
- →Hardware deployed in the same vetted GYGO Place facilities
- →Returns tied to actual GPU utilization, not synthetic yield promises
Migration Timeline: Cloud to Colocation
Most teams complete a cloud-to-colocation migration in 30–60 days with zero training downtime when following GYGO’s phased migration approach:
- 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 is 40–60% cheaper than cloud GPU for sustained workloads. Break-even arrives at 4–8 months for H100 clusters at high utilization. Upfront costs run $1.7M–2.8M for 64xH100. Migration takes 30–60 days with zero downtime using GYGO’s phased approach, running cloud and colocation in parallel during cutover.
What is the typical cost difference between cloud GPU and colocation in 2026?
Colocation is 40–60% cheaper than cloud GPU for sustained workloads. A 64xH100 cluster at $3/hr cloud on-demand costs $12.6M/year vs approximately $5.7M for colocation TCO including hardware amortization, power, and data center fees. The savings compound over time as cloud prices have been rising while colocation contracts lock in fixed rates.
When does colocation make more financial sense than cloud GPU?
At 6+ months of sustained high utilization (>70%). The break-even point for most H100 clusters is 4–8 months depending on utilization rate and negotiated colocation rates (at $0.08/kWh power per industry average). Use the ROI calculator above with your specific cluster size and utilization to find your exact break-even month. Cloud wins if commitment <6 months or utilization <50%. Not recommended to commit to colocation if your workload is seasonal — idle hardware at $800/kW/month data center fees accrues regardless of GPU activity.
What are the upfront costs of GPU colocation?
Hardware purchase ($25K–40K per H100 SXM5, at $32K/GPU per 2026 market average), racking and network setup ($5K–15K one-time), and typically 1–3 months of prepaid colocation fees (at $800/kW/month industry rate). Total upfront for a 64xH100 cluster runs $1.7M–2.8M. Not recommended if available capital is under $500K — minimum viable colocation cluster (8xH100) requires ~$300K hardware plus $25K 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 (15–30% annual increases for locked-in customers), and idle billing during provider maintenance windows. These hidden costs can add 20–40% to your nominal cloud GPU rate. Cloud wins despite hidden costs if total workload duration is <4 months — not enough time for hidden cost mitigation to justify colocation transition overhead.
How do I calculate my GPU colocation ROI?
Use GYGO's calculator above. Input your GPU model (H100, MI300X, GB200, or RTX 4090), cluster size in GPU count, average utilization percentage using the slider, and planned deployment duration in months. The calculator shows your Cloud Total vs Colocation TCO and monthly savings based on real 2026 pricing data. 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. GYGO Place facilities support high-density power up to 200 kW/rack with direct liquid cooling (DLC) for H100 and GB200 clusters, and rear-door heat exchangers for standard 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 for 1–2 weeks to validate identical outputs, then cut over production traffic and wind down cloud instances. Most teams complete migration in 30–60 days. 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 GYGO deploys in vetted facilities and leases to AI companies, with transparent 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 40–60%?
TL;DR — Next Steps
If your cloud GPU spend exceeds $50,000/month for more than 4 months, colocation economics almost certainly favor a switch. Use the ROI calculator above to confirm your specific break-even month, then contact GYGO Place for a facility match and competitive quotes delivered within 48 hours — no long-term commitment required to start.
Use the calculator above to see your potential savings, then get a custom colocation quote through GYGO Place. Our team matches your cluster requirements with vetted facilities and delivers competitive quotes within 48 hours. Colocation vs cloud savings compound year-over-year as cloud reserved pricing escalates — teams that switched in 2024 are seeing 55–70% cost reduction in year two. 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 →