Should I subscribe to a cloud LLM proxy (Claudin.io, OpenRouter) or buy a GPU and run local?

Cloud proxies and local inference solve genuinely different problems. The marketing makes them look like substitutes; they aren't.

A cloud LLM proxy (Claudin.io, OpenRouter, Glama, Together) gives you one API key and abstracts model selection across a pool of upstream providers (Anthropic, OpenAI, open-weight via aggregators). You pay either per-token (OpenRouter, Glama, Together — transparent) or flat-rate "unlimited" (Claudin.io — the controversial pricing model). The convenience is real: one endpoint, no GPU.

Local inference means the model weights load into your own GPU's VRAM and inference runs on your machine. Hardware up-front (often $600-2000), zero per-token cost after, full privacy, no rate limits, no vendor lock-in. The trade-off is real too: you maintain a runtime (Ollama/llama.cpp/vLLM), you can't run frontier 400B+ models, your throughput is bounded by your GPU.

The honest decision table:

The break-even math (the actually-honest version):

A capable local rig (RTX 3090, 24GB) costs ~$700-800 used or ~$1,500 new with a host system. At that price, your break-even vs cloud proxy is roughly 4-8 months of moderate dev use (200K tokens/day at Claude Sonnet rates). At heavy use (>1M tokens/day), break-even is under 2 months.

For the math on YOUR specific workload, plug your numbers into the cost-vs-cloud calculator. It accounts for electricity, depreciation, and the model class you actually need — not the cherry-picked best case any "save money with local AI" pitch will show you.

The "unlimited" pricing trap:

A proxy marketing $10/month for unlimited inference is selling something that doesn't make economic sense at wholesale token costs. Anthropic charges providers $3/M input + $15/M output for Sonnet 4. A heavy developer burns 50-200M tokens/month — that's $150-3000 in upstream cost. At $10 subscription revenue per user, the math only works if either (a) heavy users get rate-limited in ways that contradict "unlimited," or (b) the service is operating at a loss until it raises prices or shuts down. Similar "unlimited AI" services have historically restructured to per-use pricing or closed within 6-12 months.

If you're picking a cloud proxy, prefer transparent per-token pricing (OpenRouter) over flat-rate "unlimited" promises (Claudin.io and similar). When the proxy's economics break, the per-token one just costs you slightly more. The flat-rate one becomes either rate-limited or unavailable.

The hidden third option:

Hybrid stacks are the operator-correct answer for most builders. Run a small local model (Llama 3.2 3B for cheap structured tasks, Qwen 2.5 Coder 7B for code that doesn't need genius-level reasoning) on a 16GB GPU you already own, AND keep a cloud account for the 10% of work that genuinely needs Sonnet 4 or GPT-5. Total cost: $500 hardware + $10-30/month cloud + zero rate limits on the 90% that runs local.

The recommendation:

• First: open /will-it-run and check what your current hardware can actually run. Many developers don't realize their existing laptop GPU can handle a 7-8B model at Q4_K_M.
• Second: if your local rig isn't sufficient, browse hardware — used 3090s start at $600 and run 32B models at usable speed.
• Third: if you genuinely need cloud and decide on a proxy, use OpenRouter for per-token transparency. Skip flat-rate "unlimited" pricing models — the math doesn't support them.