The AI landscape offers two fundamentally different deployment models: running models locally on your own hardware, or using cloud-hosted APIs. Each approach has clear trade-offs in cost, performance, privacy, and flexibility. This article breaks down when each option makes sense.
What Is Local AI?
Local AI means running language models directly on your machine using tools like LM Studio, Ollama, or vLLM. The model weights are downloaded to your computer, and inference happens entirely on your hardware — no internet connection required after setup.
Popular local models include Llama, Mistral, Qwen, and Phi. These are open-weight models that anyone can download and run.
What Is Cloud AI?
Cloud AI means sending requests to hosted API endpoints. Services like Anthropic (Claude), OpenAI (GPT), Google (Gemini), and Amazon Bedrock handle the infrastructure. You pay per token and get access to the most capable models available.
Cost Comparison
Local AI costs are primarily hardware. A capable GPU (like an NVIDIA RTX 4090) costs around 1500-2000 EUR upfront. After that, running costs are essentially electricity. If you process a high volume of requests daily, local inference can be dramatically cheaper over time.
Cloud AI costs are pay-per-use. At low volumes, this is economical. At high volumes, costs can escalate quickly. A single Claude Sonnet API call costs fractions of a cent, but thousands of calls per day add up.
The break-even point depends on your volume. For occasional use — a few dozen requests per day — cloud APIs are more cost-effective. For continuous, high-volume processing, local inference often wins.
Performance and Quality
This is where cloud AI has a clear advantage. The most capable models — Claude Opus, GPT-4o, Gemini Ultra — are only available through cloud APIs. These models have hundreds of billions of parameters and require infrastructure that is impractical to run locally.
Local models are smaller by necessity. A 7B or 13B parameter model that runs on consumer hardware is significantly less capable than a 400B+ cloud model. The gap is narrowing with each generation of open models, but it remains substantial for complex reasoning tasks.
For simple tasks — text classification, extraction, summarization of short documents — local models perform surprisingly well. For complex tasks — multi-step reasoning, large codebase understanding, nuanced writing — cloud models are still superior.
Privacy and Data Control
This is where local AI excels. When you run a model locally, your data never leaves your machine. No API calls, no third-party servers, no data retention policies to worry about.
This matters enormously for:
- Healthcare and legal data subject to strict regulations
- Proprietary code that cannot be sent to external services
- Personal data covered by GDPR or similar privacy laws
- Air-gapped environments with no internet access
Cloud providers offer data processing agreements and promise not to use your data for training, but running locally eliminates the concern entirely.
Latency
Local inference latency depends on your hardware. With a modern GPU, you can get response times comparable to cloud APIs for small models. For larger models or CPU-only inference, latency can be significantly higher.
Cloud APIs offer consistent, optimized latency regardless of model size. Anthropic and OpenAI invest heavily in inference optimization, and their response times are hard to match with consumer hardware.
For real-time applications — chatbots, code completion, interactive tools — cloud APIs generally provide a better experience. For batch processing where latency is not critical, local inference works well.
Flexibility and Control
Local AI gives you complete control over the model, including:
- Fine-tuning on your own data
- Customizing inference parameters without restrictions
- Running specialized or domain-specific models
- No rate limits or usage quotas
- No dependency on external service availability
Cloud AI gives you access to the best models without managing infrastructure, automatic updates, and built-in features like tool use, vision, and streaming.
The Hybrid Approach
Many teams use both. A practical setup:
- Cloud AI for complex tasks: Use Claude or GPT for difficult reasoning, code generation, and tasks where model quality matters most
- Local AI for simple, high-volume tasks: Use a local model for text classification, data extraction, or preprocessing where privacy matters and the task does not require frontier capabilities
- Local AI for development and testing: Run a local model during development to avoid API costs, then switch to cloud for production
This hybrid approach optimizes for both cost and quality. Tools like Bob (our open-source AI platform) support this pattern natively with provider abstraction — you can switch between Amazon Bedrock and OpenAI-compatible local servers without changing your application code.
Practical Recommendations
Start with cloud AI if you are building a new product or exploring AI capabilities. The ease of use and model quality let you validate your idea before investing in infrastructure.
Add local AI when you have validated your use case and need to optimize costs, ensure privacy, or reduce external dependencies.
Use local AI exclusively when data cannot leave your network, when you need to operate offline, or when your volume makes cloud costs prohibitive.
Conclusion
The local vs cloud decision is not binary. Both approaches have clear advantages, and the right choice depends on your specific requirements for cost, quality, privacy, and latency.
The AI infrastructure landscape is evolving quickly. Local models are getting more capable, cloud APIs are getting cheaper, and hybrid approaches are becoming easier to implement. The key is to match your deployment model to your actual needs rather than defaulting to one approach for everything.