Cloud vs Local: Complete Cost & Provider Comparison
The landscape has shifted. It's no longer "cloud vs local" -- it's "cloud AND local, each for what they do best."
Table of Contents
Cloud GPU Providers
Budget Tier (Cheapest)
| Provider |
GPU |
$/hour |
Notes |
| Vast.ai |
RTX 3090 |
$0.11 |
Peer-to-peer marketplace. Cheapest but variable quality. |
| Vast.ai |
A100 80GB |
~$0.50 |
Can be unreliable. |
| Vast.ai |
H100 80GB |
$1.49 |
Lowest H100 price anywhere. |
| TensorDock |
RTX 4090 |
$0.37 |
Good for inference on smaller models. |
| TensorDock |
A100 80GB |
$1.42 |
Solid budget option. |
| Salad |
RTX 3090 |
$0.11 |
Distributed consumer GPUs. |
| Fluence |
A100 80GB |
$0.80 |
Decentralized. Zero egress fees. |
Mid-Range AI Providers
| Provider |
GPU |
$/hour |
Notes |
| RunPod |
A100 80GB |
$1.74 |
Docker-first. Great templates. |
| RunPod |
H100 80GB |
$3.19 |
Secure cloud option. |
| Lambda Labs |
A100 80GB |
$1.10 |
No egress fees. Often sold out. |
| Thunder Compute |
A100 |
$0.78 |
Virtualized GPUs. |
Enterprise Cloud
| Provider |
GPU |
$/hour |
Notes |
| AWS EC2 P4d |
8xA100 |
$21.95-40.97 |
Recently 33% price cut. |
| AWS EC2 P5 |
8xH100 |
~$55.04 |
45% price reduction Jun 2025. |
| Azure |
H100 |
$6.98 |
Most expensive H100. |
| GCP |
Various |
Premium |
Good ML ecosystem. |
Dedicated Monthly Servers
| Provider |
GPU |
$/month |
Notes |
| Hetzner GEX44 |
RTX 4000 Ada |
~$184 |
Cheapest dedicated GPU server. EU-based. |
| Hetzner GEX131 |
RTX PRO 6000 |
~$940 |
Training-grade. Flexible hourly billing. |
| Contabo |
L40S |
$790 |
90% cheaper than AWS equivalent. |
GPU Pricing Summary
| GPU |
VRAM |
Cheapest |
Mid-Range |
Enterprise |
| RTX 3090 |
24GB |
$0.11/hr |
$0.20 |
N/A |
| RTX 4090 |
24GB |
$0.18/hr |
$0.37-0.44 |
N/A |
| A100 80GB |
80GB |
$0.44/hr |
$1.10-1.74 |
$2.74-5.04 |
| H100 80GB |
80GB |
$1.49/hr |
$2.25-3.19 |
$5.95-6.98 |
Inference API Providers
No GPU management required. Call open-source models via API.
| Provider |
Speed |
Cost/1M tokens |
Free Tier |
Best For |
| Groq |
300+ tok/s |
$0.59 |
Yes (generous) |
Real-time, coding |
| Cerebras |
969 tok/s (record) |
$0.10 (8B) |
Yes |
Bulk processing |
| DeepInfra |
Fast |
$0.08 |
Yes |
Budget inference |
| Together.ai |
86 tok/s |
$0.88 (70B) |
$25 credit |
Fine-tuning + inference |
| Fireworks AI |
68 tok/s |
$0.18-7.00 |
Yes |
Multimodal |
| SambaNova |
Fast |
Competitive |
Yes |
Enterprise |
| Replicate |
Varies |
Pay-per-run |
Yes |
Serverless |
| Novita AI |
Good |
$0.10 |
Yes |
200+ models |
| OpenRouter |
Varies |
Pass-through |
No |
Unified access to all |
Context: GPT-4o equivalent performance now costs ~$0.40/million tokens. Down from $20 in late 2022. A 50x price drop in 3 years.
When to Use APIs vs Self-Hosted
- < 8,000 conversations/day: API is almost always cheaper
- > 30M tokens/day: Self-hosted reaches cost parity within 1-4 months
- For coding agents: API calls to frontier models (Claude, GPT-4o) are generally better quality than any self-hosted model
Mac-as-a-Service
You can rent Mac hardware in the cloud.
| Provider |
Hardware |
Price |
Best For |
| MacStadium |
Mac Mini M4 |
$109-199/mo |
CI/CD, enterprise, long-term |
| MacStadium |
Mac Studio (Ultra) |
~$599/mo |
Heavy workloads |
| MacinCloud |
Mac servers |
$1/hr or $25/mo |
Occasional use |
| JUUZ |
Mac Mini M2, Studio |
$49/mo (EU) |
Budget Mac cloud |
| AWS EC2 Mac |
Mac instances |
~$25/day |
AWS ecosystem |
| Rent-a-Mac.io |
Mac Minis |
Varies |
Dedicated |
Buy vs Rent Math
Mac Studio Ultra at $599/mo via MacStadium = $7,188/year. Buying one costs $5,600 one-time. Buying is cheaper after ~9 months.
Cost Comparisons
Scenario A: Running a 70B Model Daily (8 hrs/day)
| Option |
Monthly Cost |
Annual Cost |
| Mac Studio M4 Max 128GB |
$0 (after $3,500 purchase) |
~$292 amortized/yr |
| RunPod A100 80GB |
$417 |
$5,004 |
| Vast.ai A100 |
$120 |
$1,440 |
| TensorDock A100 |
$340 |
$4,080 |
| Lambda Labs A100 |
$264 |
$3,168 |
| Hetzner GEX44 dedicated |
$184 |
$2,208 |
Scenario B: Light Use (2 hrs/day)
| Option |
Monthly Cost |
Annual Cost |
| Mac Studio M4 Max 128GB |
$0 (after $3,500) |
$292 amortized |
| RunPod A100 |
$104 |
$1,248 |
| Vast.ai A100 |
$30 |
$360 |
Break-Even Points
- Mac Mini M4 Pro 64GB ($2,000): Breaks even in 6-12 months vs $100+/mo cloud APIs
- Mac Studio M4 Max 128GB ($3,500): Breaks even in 8-13 months vs cloud A100 at 8 hrs/day
- Mac Studio M3 Ultra 512GB ($9,500): Breaks even in ~12-18 months vs cloud GPU at $8/hr
Enterprise Scale (Lenovo H100 Study)
| Metric |
On-Premises |
AWS On-Demand |
AWS 3-Year Reserved |
| 5-Year TCO (24/7) |
$871,912 |
$4,306,416 |
$2,362,812 |
| Savings vs cloud |
-- |
$3.4M saved |
$1.5M saved |
On-prem breaks even at just 5 hours/day usage vs on-demand cloud.
When to Use Cloud vs Local
Cloud Makes Sense When
- You need frontier models (Claude Opus, GPT-4o, Gemini Ultra) -- they're cloud-only
- Training large models (NVIDIA H100/B200 clusters)
- Burst workloads -- need 100 GPUs for 3 days, then nothing
- You lack IT staff to manage hardware
- Rapid prototyping -- try before committing
- Global serving -- need low latency worldwide
Local Makes Sense When
- Running inference on known models 24/7
- Data privacy is critical (healthcare, finance, legal, government)
- Predictable, steady workloads (math favors local)
- Low latency required (no network round-trip)
- Want to avoid vendor lock-in and API deprecations
- Total cost of ownership over 2+ years matters
The Pragmatic Hybrid (What Most Teams Do)
- Local model (Ollama + Qwen3-Coder or GLM-4.7) for autocomplete, quick questions, private data, iteration
- Cloud model (Claude Opus/Sonnet, GPT-4o) for complex multi-file coding, architecture, heavy reasoning
- Inference API (Groq free tier, DeepInfra) for cheap bulk queries
The Repatriation Trend
Major shift happening in 2025-2026:
- 86% of CIOs planned to repatriate some cloud workloads (Barclays CIO Survey)
- 83% of enterprise CIOs plan to bring at least some workloads on-premise
- Only 8-9% plan full repatriation -- most go hybrid
Top reasons:
1. Cost -- cloud GPU costs are staggering at scale
2. Security/Privacy -- sensitive data leaving the organization
3. Latency -- network round-trip adds 50-200ms
4. Control -- vendor lock-in, unpredictable pricing
5. Regulatory compliance -- data residency requirements
The Mac Mini buying frenzy (triggered partly by OpenClaw/Clawdbot going viral) is the consumer/SMB version of this enterprise trend.
Recommendations by Use Case
| Need |
Best Path |
Monthly Cost |
| Best coding quality |
Claude Code (Anthropic API) |
$20-200 |
| Cheapest queries |
Inference APIs (Groq free tier) |
$0-20 |
| Run your own models |
Mac Mini M4 Pro 64GB + Ollama |
$0 after $2K purchase |
| Cloud GPU flexibility |
RunPod or TensorDock A100 |
$85-417 |
| Privacy/compliance |
Self-hosted on Hetzner |
$184 |
| Enterprise at scale |
On-prem H100 cluster |
High upfront, saves millions over 5yr |