Local inference vs cloud APIs
Local AI is not strictly better than cloud APIs. It wins on privacy, predictable cost, offline capability, and lock-in. It loses on raw quality at the frontier, on operator complexity, and on time-to-deploy. The honest decision is workload-by- workload.
| Dimension | Local inference Owned hardware | OpenRouter Multi-vendor router | Together / Fireworks Open-source cloud | Frontier APIs OpenAI / Anthropic / Google |
|---|---|---|---|---|
Raw decode speed Tokens per second at the model the operator actually uses. | Acceptable Bound by your hardware. 30-100 tok/s consumer; 200-400 with vLLM on pro. | Strong Routes to fastest provider; usually 100-300 tok/s. | Strong Optimized inference; competitive with frontier on open-source models. | Excellent Fastest tokens-per-second on their proprietary models. |
Privacy Where your prompts and outputs live. | Excellent On your machine. Logs are yours. | Limited Routed across multiple providers; each has its own retention policy. | Acceptable Stated retention controls; trust the policy or run on dedicated. | Limited Vendor-controlled; enterprise tiers offer DPA but the data still leaves your boundary. |
Offline capability Works with the network unplugged. | Excellent The whole point. | Poor Internet-required. | Poor Internet-required. | Poor Internet-required. |
Lock-in risk What you lose if your vendor changes. | Excellent Open weights + open runtime; portable. | Strong Multi-provider abstraction reduces single-vendor lock-in. | Strong Open-weights focus; switching providers is real. | Limited Closed models; switching means re-prompting and quality regression. |
Predictable cost Can you forecast next month's bill within ±10%? | Excellent Capex + electricity. Predictable to the cent. | Acceptable Per-token; predictable if usage is stable. | Acceptable Per-token + dedicated tiers. | Limited Per-token; one viral product moment can 10x the bill. |
Latency floor Time to first token under good conditions. | Excellent Sub-100 ms TTFT typical; no internet round trip. | Strong Provider-dependent; 200-500 ms typical. | Strong Optimized network; 200-400 ms. | Strong Optimized infrastructure; 200-500 ms typical. |
Model breadth How many models you can choose from. | Strong Anything with public weights; HuggingFace + GGUF library. | Excellent Hundreds of routes; aggregator-grade breadth. | Strong Most popular open-source + custom fine-tunes. | Limited Only the vendor's own models. |
Quality on hardest tasks Frontier-tier reasoning + tool use + long context. | Acceptable Best open-source models close the gap, but a quality gap to GPT/Claude/Gemini remains. | Strong You can route to frontier when you need it. | Strong Open-source models, served well; same quality ceiling as local. | Excellent Top-tier on hardest reasoning + tool use. |
Compliance / DPA What enterprise procurement asks about. | Excellent Data never leaves your infrastructure; airgap real. | Limited Vendor DPAs vary by route; complex to audit. | Strong SOC 2; explicit DPAs; dedicated tier for stricter requirements. | Strong Robust enterprise programs; data-zero retention options. |
Operator complexity Hours per month keeping the system working. | Limited 5-15 hours/month: drivers, runtime updates, model management. | Excellent Effectively zero. | Excellent Effectively zero. | Excellent Effectively zero. |
Decision rule of thumb
Local wins when: you handle sensitive data, you have predictable inference volume, you want to control the entire stack, you need offline capability, or your workload is a fit for an open-source model.
Cloud wins when: you need frontier-grade reasoning, your usage is bursty or unpredictable, you don't have an operator on staff, or you're prototyping and time-to-first-result matters more than per-token cost.
Hybrid wins more often than people admit: local for the 80% of workloads where it's fine, cloud frontier API for the 20% where quality matters.