Choosing between local AI and cloud AI is not a technical decision — it is a business decision. Each approach optimises for different priorities: privacy, cost structure, model quality, scalability, and maintenance burden. This guide breaks down every dimension so you can decide which fits your situation.
Overview of both approaches
| Dimension | Local AI | Cloud AI |
|---|---|---|
| Cost | GPU capex + electricity | Per-token or flat monthly |
| Privacy | Full — data never leaves | Provider-dependent |
| Latency | 10-300ms (no network) | 500-5000ms (variable) |
| Model selection | Limited by VRAM | 30+ models available |
| Scalability | Limited to 1 GPU | Auto-scaling |
| Maintenance | You own it | Provider managed |
| Internet needed | No (after download) | Yes |
| Setup time | 10-60 minutes | 30 seconds |
Cost comparison
This is the most nuanced dimension. The right answer depends entirely on your volume.
Local AI cost structure
- Upfront: $1,500-4,000 for a consumer GPU (RTX 3090/4090), $10,000+ for workstation GPUs.
- Ongoing: Electricity at ~$0.10-0.30/hour of GPU use. No per-token fees.
- Total at 10M tokens/month: ~$100-200/month (amortised hardware + power).
Cloud AI cost structure
- Upfront: Zero. Sign up with an email address.
- Ongoing: Per-token pricing or flat monthly. Plugsky plans start at $5/trial, $29/month for heavy use.
- Total at 10M tokens/month: ~$29-500/month depending on provider and model tier.
The breakeven point varies by hardware cost and usage volume. Generally, if you process more than 50M tokens per month with predictable demand, local AI is cheaper. Below that, cloud AI wins on cost. Run the numbers with our self-hosting breakeven calculator.
Privacy comparison
Local AI is the gold standard for privacy. No data leaves your machine. No third party sees your prompts, intermediate computations, or outputs. This is simple physics — the data never traverses a network.
Cloud AI varies by provider. Some (Plugsky, Groq) explicitly never train on your data. Others (OpenAI, Google) offer opt-out controls. But even with contractual guarantees, cloud AI inherently involves sending your data to someone else's server. For regulated industries, this is often a dealbreaker regardless of the provider's policy.
Latency comparison
- Local AI: 10-300ms for a 7B model on a local GPU. No network round-trip, no queueing, no rate limiting. The dominant factor is model inference speed.
- Cloud AI: 500-5000ms. Network latency (50-200ms), queueing (100-1000ms during peak), and inference on shared GPUs all add up. Streaming helps first-token latency but total time is still higher.
For real-time applications (chat, coding autocomplete, voice agents), local AI's latency advantage is significant. For batch processing or async workflows, the difference is less noticeable.
Model selection
Cloud AI wins on model variety. Plugsky offers 30+ models from Llama, Qwen, Mistral, DeepSeek, and more. OpenAI gives access to GPT-4o, o1, and o3. Anthropic provides Claude 3.5. You can switch between all of them with a single parameter change.
Local AI is limited by your VRAM. A consumer GPU (24GB) can run models up to 13B-14B parameters at 4-bit quantisation. For 70B models you need workstation GPUs. You are limited to open-weight models — you cannot run GPT-4o or Claude locally regardless of hardware.
Scalability comparison
- Local AI: One GPU serves one user at a time or a small batch. Adding capacity means buying and installing more hardware. There is no elastic scaling.
- Cloud AI: Auto-scaling to thousands of concurrent requests. Traffic spikes are absorbed automatically. You pay only for what you use.
If your workload is predictable and stable, local AI works fine. If you need to handle variable traffic — or scale from prototype to production overnight — cloud AI is essential.
Maintenance comparison
| Task | Local AI | Cloud AI |
|---|---|---|
| GPU driver updates | You manage | Provider manages |
| Model downloads | You download | Instant access |
| Model updates | You pull new versions | Provider updates |
| Monitoring | You set up | Built-in dashboards |
| Backups | You configure | Provider handles |
| Capacity planning | You forecast | Auto-scaling |
Hybrid approach: develop locally, deploy to cloud
The most pragmatic approach used by many teams: develop against a local Ollama instance for speed, privacy, and zero cost during development. Then deploy to a cloud API like Plugsky for production where you need larger models, team access, and reliability.
Because Ollama exposes an OpenAI-compatible API and Plugsky also uses the OpenAI API format, switching between them is a one-line config change: base_url from http://localhost:11434 to https://api.plugsky.com/v1. The same code, the same SDK, the same parameters — just a different backend.
Decision table
| If you value... | Choose |
|---|---|
| Data privacy above all else | Local AI |
| Lowest cost at high volume | Local AI |
| Offline operation | Local AI |
| Learning how models work | Local AI |
| Zero upfront cost | Cloud AI |
| Access to largest models | Cloud AI |
| Variable or unpredictable scale | Cloud AI |
| Team access and collaboration | Cloud AI |
| Quick prototyping | Cloud AI |
| Best of both worlds | Hybrid (local dev + cloud production) |
Get the best of both worlds
Develop locally with Ollama. Deploy to Plugsky for production. Same API, 30+ models, flat-rate pricing.
Start Free → Explore local AIFrequently asked questions
Which is cheaper: local AI or cloud AI?
For low volume (under 1M tokens/month), cloud AI is cheaper because there is no upfront hardware cost. For high volume (over 50M tokens/month), local AI is cheaper because the marginal cost per token is electricity only. Use our breakeven calculator for your specific numbers.
Is local AI more private than cloud AI?
Yes. Local AI runs entirely on your hardware — no data ever leaves your machine. Cloud AI always sends your prompts to the provider's servers. Even with privacy guarantees, cloud AI involves an inherent data transfer that local AI avoids.
Can I use both local and cloud AI together?
Yes. A hybrid approach is common: develop locally with Ollama for speed and privacy during development, then deploy to a cloud API like Plugsky for production where you need larger models and auto-scaling. The OpenAI-compatible API makes switching seamless.
What is the best cloud AI API?
Plugsky offers 30+ models on flat-rate plans with no per-token fees, OpenAI compatibility, and data privacy guarantees. OpenAI offers the largest frontier models. Both are excellent choices depending on your model needs and budget structure.
When should I use local AI instead of cloud AI?
Use local AI when: your data is too sensitive to send to a cloud provider, you have predictable high-volume workloads, you need offline operation, or you want to learn how AI models work at a low level. Use cloud AI for everything else.