Local LLMs vs Cloud APIs: Which Makes Sense for Your Workload?

Published 2026-07-06 · 29 open-weight models · 2076 cloud API routes

Running an LLM on your own hardware is no longer a fringe experiment. Tools like Ollama and llama.cpp make local inference as easy as a single command, and consumer GPUs can run models up to 70B parameters at usable speeds. But cloud APIs keep getting cheaper too — and they offer frontier models no local setup can match.

This guide compares the two approaches across cost, capability, privacy, and practical trade-offs, using the site's local model database and our full cloud API catalog.

• When local deployment makes sense
• When cloud APIs make sense
• Cost comparison framework
• Model capability comparison
• Hardware guide for local models
• Summary: how to choose

When Local Deployment Makes Sense

Running models on your own hardware is worth considering when any of these matter to you:

Privacy and data sovereignty

Your prompts, context, and model outputs stay on your hardware. No data is sent to a third-party API endpoint. For applications handling customer PII, internal documents, or proprietary code, this alone can justify local deployment.

Predictable costs at high volume

Cloud APIs charge per token. If your application processes millions of tokens daily, the per-token cost adds up fast. Local inference has a high upfront cost (GPU hardware) but near-zero marginal cost per request — just electricity. At high enough volume, local becomes cheaper than even the most affordable cloud API.

No rate limits or vendor lock-in

Cloud APIs enforce rate limits, may deprecate models without warning, and change pricing on their schedule. A local model runs exactly how you configure it, as long as your hardware lasts.

Lower latency

Local inference eliminates network round trips. Typical latency is 100-500ms versus 500-2000ms for cloud APIs, which matters for real-time applications like chat, coding assistants, and voice agents.

Browse 29 open-weight local models →

When Cloud APIs Make More Sense

Cloud APIs remain the right choice in many scenarios:

Frontier models are not available open-weight

Today's most capable models — Claude Opus 4, GPT-5.4, Gemini 3.1 Pro — are proprietary and only accessible via API. If your task needs top-tier reasoning, coding, or multimodal performance, a cloud API is your only option.

Variable or elastic workloads

If your request volume spikes and dips, a cloud API scales with you. You pay for what you use and nothing when your application is idle. Local hardware is a fixed cost whether or not you run inference.

Zero setup time

A cloud API key takes five minutes to generate. Running a local model requires GPU hardware, driver setup, model download (4-40 GB each), and ongoing maintenance. For teams evaluating LLMs or building prototypes, cloud APIs are the faster path.

Multi-model routing without multi-GB downloads

Cloud APIs let you switch between models for different tasks — use a cheap model for classification, a mid-tier model for chat, and a reasoning model for complex analysis. Locally, each model requires separate download and GPU memory. Our routing cascade guide covers this strategy in depth.

Browse 2076 cloud API models →

Cost Comparison Framework

Comparing local and cloud costs requires looking at total cost of ownership, not just per-token prices.

Cloud API costs

Cloud pricing is straightforward: you pay per million input and output tokens. Use our cost calculator with workload presets to estimate your monthly bill. For high-volume workloads, prompt caching and batch processing can cut costs by 50% or more (see our cache and batch pricing guide).

Local inference costs

Local costs divide into hardware (one-time), electricity (ongoing), and your time (setup and maintenance).

• Consumer GPU (e.g., RTX 3090/4090, 24 GB VRAM): $800-1,600 one-time, can run models up to ~13B at FP16 or ~70B with quantization
• Used workstation GPU (e.g., RTX 3080 12 GB): $300-500 one-time, good for 7B models at FP16
• Electricity: approximately $10-15/month for 24/7 operation based on community reports
• Cloud GPU rental: $0.50-2.00/hour for A10G/A100 instances as an alternative to buying hardware

Break-even estimate

At roughly $0.10-0.50 per million input tokens on the cloud side (for budget models) and a one-time hardware cost of $800-1,600, a local setup breaks even when you process approximately 1.6-16 billion input tokens — roughly 150-1,500 monthly requests at 10K tokens each over a year. The exact point depends heavily on which cloud model you compare against and whether you need frontier capability.

Estimate your cloud API cost with the calculator →

Model Capability Comparison

Here is what the current open-weight landscape looks like versus comparable cloud offerings. The largest open-weight models in our catalog:

For comparison, cloud APIs offer equivalent open-weight models (Llama 4, DeepSeek V4, Qwen 3.7) via inference providers like Together, Fireworks, DeepInfra, and Groq at a fraction of frontier pricing. The key difference: a local setup gives you unlimited inference at marginal electricity cost, while the cloud gives you instant access without hardware investment.

For proprietary frontier models — Claude, GPT-5, Gemini Pro — cloud is the only option regardless of your budget. Compare capabilities side by side with our model comparison tool.

Hardware Guide for Local Models

Choosing hardware for local inference starts with VRAM. Here is a rough guide by model size:

Quantization (INT4 or INT8) dramatically reduces VRAM requirements at a modest quality cost. Most local models support multiple quantization levels — check individual model pages in the local model explorer for specific quantization support.

If you do not have a compatible GPU, cloud GPU rental (A10G, A100, H100 instances) can serve as an intermediate step between cloud APIs and full local ownership. At $0.50-2.00/hour, a long-running inference job may still beat per-token API costs for high-volume workloads.

Filter local models by VRAM requirement →

Summary: How to Choose

Choose local when: privacy matters, you have predictable high-volume usage, you need low latency, and the available open-weight models meet your quality bar.

Choose cloud when: you need frontier models, your workload is variable, you are prototyping or iterating quickly, or you want multi-model routing without managing hardware.

Many teams use both: cloud APIs for complex reasoning or multimodal tasks, and local models for high-volume classification, extraction, and chat where cost and latency matter most. This hybrid approach balances capability with cost.

Start by exploring what is available: browse 29 local models, check cloud model comparisons, or run your workload through the cost calculator.

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