Local AI or Cloud API? A Practical Systems Decision

Local AI gives you direct control over models, data paths, warm capacity, and versioning. Cloud APIs give you managed infrastructure, elastic capacity, and access to models that may be impractical to host. Neither is universally faster, cheaper, safer, or better.

We tested two local models on Sage3’s RTX 3090: Llama 3.1 8B with a 4.9 GB Ollama package and Qwen3 14B with a 9.3 GB package. Both received the same route-classification prompt with temperature 0, seed 42, thinking disabled, streaming disabled, and a 64-token cap.

ComfyUI models were unloaded first. GPU use was 1,024 MiB before the benchmark. Each model ran once cold and twice warm. Both selected the correct video route.

Warm calls were about eight to ten times faster end to end because model loading disappeared and the runtime could reuse resident state. The larger Qwen package loaded slightly faster in this run, which shows why disk size alone does not predict cold latency. Filesystem cache, quantization, model layout, runtime version, and GPU placement matter.

The outputs had different token counts, so raw total time is not a pure generation-throughput comparison. Llama produced 14 output tokens with Markdown fencing. Qwen produced 7 tokens of plain JSON. For the product, structured correctness and parsing behavior matter as much as tokens per second.

The experiment manifest records nanosecond timing fields from Ollama. Its API returns total duration, load duration, prompt token count, prompt evaluation duration, output token count, and evaluation duration, which makes this kind of workload measurement reproducible.

A local model improves control only when the complete path is local. Check prompt logs, crash reporting, analytics, model-download telemetry, reverse proxies, browser requests, and fallback behavior. An application can run inference locally while still sending metadata elsewhere.

Cloud privacy depends on provider terms, data-retention settings, region, account configuration, and the exact API. Avoid blanket claims. Document which fields leave the application boundary and why.

Local hardware becomes attractive when it stays usefully occupied. At low volume, idle hardware and maintenance can dominate. At bursty volume, a cloud API may absorb peaks without forcing permanent capacity. At steady high volume, provider margins can exceed the cost of owned hardware.

Include engineering time. Driver upgrades, model downloads, disk pressure, queue tuning, security patches, monitoring, and failed jobs are real costs even when inference has no per-call invoice.

• Local risk: GPU failure, thermal limits, disk exhaustion, model corruption, and finite queue capacity.
• Cloud risk: rate limits, regional incidents, provider changes, network dependency, and account policy.
• Local control: pin versions, inspect logs, reserve capacity, and choose fallback behavior.
• Cloud control: select regions, quotas, timeout policy, retries, and provider redundancy where supported.

Movey uses local planning for suitable routing tasks and sends media generation to the workflow or provider that fits the requested capability. A practical router can consider:

• Does policy allow this data to leave the local boundary?
• Does a local model meet the measured quality threshold?
• Is local capacity healthy and below the queue limit?
• Does the request require a capability available only from a provider?
• What are the deadline, estimated cost, and fallback policy?

if privacy_requires_local:
    route = best_healthy_local_model
elif local_quality_passes and local_queue_is_short:
    route = local_model
elif cloud_provider_is_allowed:
    route = cloud_model
else:
    route = deterministic_fallback

Official Ollama API fields and model documentation used for the controlled local benchmark.

• Ollama generate API documents streaming, keep-alive behavior, structured output, token counts, and timing fields.
• Ollama chat API documents messages, tools, structured formats, and runtime metrics for chat workloads.
• The Sage3 benchmark manifest beside the chart records all raw timings, model package sizes, GPU memory readings, prompt text, and runtime options.