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API vs Self-hosted LLMs

Rent the best model, or run your own

The verdict

API providers give you the best models with zero ops. Self-hosting gives you data control, custom fine-tunes, and predictable cost past a break-even volume, usually 10 to 50 million tokens per day.

API LLM

Glossary →

Call a hosted model through a provider API (OpenAI, Anthropic, Google, Bedrock). Pay per token, get the frontier model, hand over control of ops. Data leaves your premises unless you use a private-cloud tier.

Best for: Prototyping, low volume, need the best model.

Self-hosted LLM

Glossary →

Run an open-weights model (Llama, Mistral, Qwen, DeepSeek) on your own GPUs, whether bare-metal, cloud, or a serving framework like vLLM or TGI. You own the model, the ops, the data path, and the cost curve.

Best for: High volume, data residency, custom fine-tunes.

At a glance

API vs Self-hosted LLMs: dimension-by-dimension comparison
Dimension	API LLM	Self-hosted LLM
Model ceiling	Frontier (GPT-5, Claude 4, Gemini 2)	Best open weights
Cost profile	Per token, elastic	Per GPU-hour, fixed
Data path	Provider (or private tier)	Your VPC or your metal
Fine-tuning control	Provider-limited	Unrestricted
Ops burden	Minimal	Autoscale, on-call, eval infra
Break-even hint	Below ~10M tokens/day	Above 10-50M tokens/day (workload-dependent)

Key differences

1APIs give you the frontier model instantly; self-hosting caps at whatever open weights are strongest
2APIs bill per token; self-hosting bills per GPU-hour whether you are busy or idle
3APIs send data to the provider; self-hosted stays in your VPC (or on your metal)
4Fine-tuning on APIs is provider-mediated; on self-hosted it is unrestricted
5APIs push ops onto the provider; self-hosting means autoscaling, on-call, and eval infrastructure are yours

In the interview

What they're really testing

Whether you can reason about break-even volume, data residency, and where open-source has closed the gap.

Say this

For prototyping and low volume I use an API because the model is better and there are no ops. Self-hosting earns its keep past a break-even volume, usually ten to fifty million tokens per day depending on the model, or immediately when data residency is a hard constraint or the fine-tune has to be unrestricted. Open weights (Llama-4, Qwen-3, DeepSeek-V3) have closed most of the gap for reasoning-lite workloads.

Traps to sidestep

Claiming self-hosting is always cheaper
Ignoring the ops cost of running your own inference (autoscale, on-call, eval)
Missing that some providers offer private-cloud tiers for data residency
Forgetting that fine-tuning on APIs is provider-mediated

How to choose

If prototyping or low volumeAPI LLM

If strict data residency or regulated industrySelf-hosted LLM

If very high volume where per-token cost dominatesSelf-hosted LLM

If you need the strongest available model this weekAPI LLM

API by default. Self-host at high volume, strict data residency, or when custom fine-tunes matter.

Common misconceptions

Myth: Self-hosting is always cheaper.

Reality: Below break-even it is much more expensive once you count GPUs, ops, on-call, and eval infrastructure. Break-even shifts with the model and workload.

Myth: APIs can't meet compliance needs.

Reality: Many providers offer private-cloud, no training on your data, and regional tiers. Compliance may be a paperwork exercise, not a self-hosting mandate.

Memory aid

API is renting a plane ticket; self-hosting is buying and operating your own plane.

Can you combine them?

The common pattern is hybrid: use an API for the strongest reasoning-heavy calls, self-host smaller open models for high-volume classification, embeddings, and reranking. Route by cost per successful outcome.

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