Self-hosted LLM Inference Review — Open Weights on Your Own GPUs
When does running open-weight models yourself beat a managed API? A practical review of self-hosted LLM inference for privacy-first teams, with the real cost trade-offs.
Self-hosting open-weight models is the option managed-API comparisons usually ignore — and the one privacy-first teams most need to evaluate honestly. This review is about when it actually wins, not cheerleading.
What you're really buying
With a managed API you pay per token and someone else runs the GPUs. Self-hosting flips that: you pay for hardware (or rented GPU hours) and operational effort, and the marginal cost per token drops toward your electricity and amortized capital. The break-even is entirely about utilization. A GPU that's busy 80% of the day is cheap per token; one that idles is the most expensive inference you'll ever buy.
The genuine advantages
- Data residency. Inputs and outputs never leave your infrastructure. For regulated data or strict internal policy, this is often the only acceptable answer — no managed API matches it.
- No per-token meter. Predictable monthly cost regardless of volume spikes, once the hardware is provisioned.
- Control. Pin model versions, customize, fine-tune, and avoid surprise deprecations.
The honest downsides
- Ops burden. You own uptime, scaling, batching, quantization choices, and GPU memory management. This is real engineering time that the per-token price hides.
- Quality ceiling. Open-weight models have closed much of the gap, but the very top of frontier reasoning still tends to live behind managed APIs. Match the model to the task.
- Idle waste. Bursty, low-volume workloads rarely justify dedicated GPUs. Serverless GPU or batched scheduling helps, but it's more moving parts.
How to decide
Run the numbers, don't go on vibes. Estimate your monthly token volume, then:
- Put that volume through the LLM API cost calculator for the managed options.
- Estimate your self-hosted monthly cost (hardware amortization or GPU rental + power + a realistic share of ops time).
- Compare totals at your expected utilization, then again at half that. If self-hosting only wins at 90% utilization, it's fragile.
Our self-hosted LLM cost breakdown walks through this math with a worked example.
When it pays off
- Yes: steady, high-volume inference; strict data-residency needs; tasks an open-weight model handles well.
- Maybe: medium volume where a fast managed model is "good enough" — compare totals carefully.
- Probably not: low or bursty volume, or tasks that genuinely need top-tier frontier reasoning.
Bottom line
Self-hosting isn't automatically cheaper — it's cheaper at high utilization and unbeatable for data residency. Decide with the cost calculator and the cost breakdown guide, comparing total cost of ownership against managed APIs like Claude and OpenAI at your real volume.