Ollama (Local LLMs) vs Cloud LLMs
Running LLMs locally with Ollama versus using cloud APIs represents a fundamental trade-off in agent architecture. Local models give you privacy, zero per-token cost, and no rate limits. Cloud models give you frontier capabilities, no hardware requirements, and instant access to the latest models.
The best production systems use both. Local models handle high-volume, simpler tasks where latency and cost matter. Cloud models handle complex reasoning and tool use where capability matters most.
Ollama (Local)
- •Complete data privacy — nothing leaves your machine
- •Zero per-token cost after hardware investment
- •No rate limits or API quotas
- •Works offline — no internet dependency
- •Full control over model selection and quantization
Cloud LLMs
- •Frontier capabilities (GPT-4, Claude, Gemini)
- •No hardware requirements — scales instantly
- •Always up to date with latest model versions
- •Reliable tool use and function calling
- •Enterprise features (fine-tuning, batch API, evaluation)
Verdict
Use both. Local Ollama models for high-volume simple tasks and privacy-sensitive workloads. Cloud models for complex reasoning and tool use. Model fallback chains that start local and escalate to cloud give you the best of both.
Frequently Asked Questions
What hardware do I need for Ollama?
It depends on the model. 7B models run on 8GB RAM. 13B models need 16GB. 70B models need 48GB+ VRAM. GPU acceleration (NVIDIA, Apple Silicon) dramatically improves speed.
Can local models do tool use?
Some can, but reliability varies. Models like Qwen and Llama support function calling, but frontier cloud models are still more reliable for complex tool use scenarios.
How do I combine local and cloud models?
Use a fallback chain: try the local Ollama model first, fall back to a cloud model via OpenRouter if the local model fails or returns inadequate results.