Self-hosting an open-weight model is not finished when the weights download. Something has to sit between your application and the GPU, batching requests, managing memory and routing traffic. That something is the inference server, and in October 2026 most Australian teams end up choosing between three: vLLM, SGLang and Hugging Face's TGI.
The choice looks like a technical detail. It is not. The three differ enough on throughput, latency and the engineering effort they demand that picking the wrong one shows up on the monthly GPU bill. This post compares them the way a delivery team would, by asking what each one costs you to run well.
Which inference server should an Australian team choose?
For most Australian teams starting out, vLLM is the right default. It has the largest community, the broadest model support and strong throughput on standard GPU setups without heavy tuning. Choose SGLang only when a latency-sensitive agentic workload justifies extra configuration effort, and choose TGI mainly if you are already committed to the Hugging Face stack.
That answer holds for the common case: a team without a dedicated inference engineer, running one or two models for internal workloads. It changes if your traffic is dominated by long tool-calling chains, or if your platform team already lives inside Hugging Face tooling. The rest of this post works through those exceptions.
| Server | Best fit | Setup effort | Main caution |
|---|---|---|---|
| vLLM | Most teams, general workloads | Low | Default settings are not always optimal for your model |
| SGLang | Latency-sensitive agentic chains | High | Needs an experienced engineer to configure well |
| TGI | Teams already on Hugging Face | Medium | Has historically lagged on high-concurrency throughput |
Where each server earns its place
vLLM: the safe default
vLLM's PagedAttention memory management is the reason it performs well out of the box. It packs the model's working memory efficiently, so a single GPU can serve more concurrent requests before it runs out of room. For a team that wants a working, reasonably fast deployment without weeks of tuning, that matters more than any benchmark headline.
The broad model support is the other half of the case. When you swap models, and most teams do within a year, vLLM is the server most likely to support the new one on day one. If you are still deciding whether to run vLLM at all versus a lighter local runtime, our earlier comparison of Ollama, vLLM and LocalAI covers that step.
SGLang: faster on complex prompts, if you can tune it
SGLang tends to pull ahead on latency when prompts have complex structure, such as agentic workflows that call tools, read the result and call again. Teams running structured multi-step workflows report meaningful latency gains. The cost is engineering time: getting those gains requires configuration expertise that many mid-market teams do not have in house.
TGI: a fit for Hugging Face shops
TGI integrates tightly with the Hugging Face ecosystem. If your team already manages models, datasets and deployments there, staying on TGI reduces the number of moving parts. Outside that situation there is no strong reason to choose it over vLLM, given its weaker record on raw throughput under heavy concurrency.
What the choice is worth in dollars
A Sydney fintech running a 20B parameter model for internal document processing tested all three servers on identical hardware. The gap between the best and worst configuration was 35% in throughput. At their volume that was worth roughly $1,100 a month in GPU capacity they did not need to buy.
That saving was real, but it was not free. Capturing it took a senior engineer about two weeks of benchmarking and tuning. Whether that trade is worth making depends on how long you will run the workload and how much traffic it carries.
| Item | Illustrative value |
|---|---|
| Monthly GPU saving from better configuration | $1,100 |
| Senior engineer time spent benchmarking | 2 weeks |
| Illustrative cost of that time | $8,000 |
| Months to pay back | About 7 |
| Net saving over two years | About $18,400 |
Put plainly: on a workload you expect to run for two years or more, benchmarking pays for itself. On a six-month pilot it probably does not, and vLLM's defaults are the sensible call. Our ROI calculator is a quick way to test that trade with your own volumes.
A short checklist before you commit
Budget engineering time for benchmarking on your real workload before you commit. The wrong server for your traffic can cost thousands of dollars a month in wasted GPU capacity.
Start with vLLM unless you have a specific latency-sensitive use case that justifies SGLang's added complexity.
Re-benchmark whenever you change the underlying model. Server performance does not transfer cleanly between model architectures.
Make GPU infrastructure decisions separately. All three servers still need you to choose hardware, region and capacity.
Plan for monitoring, patching and failover regardless of server. None of these tools removes that work.
What not to conclude from this
It is tempting to read a 35% throughput gap as a reason to spend months chasing the perfect configuration. It is not. The gap was specific to one model, one workload and one hardware setup. A different model on the same servers could rank them differently, which is exactly why re-benchmarking after a model change matters.
It is also tempting to treat the server choice as the hard part of self-hosting. It is one decision among several. GPU pricing, Australian data residency, on-call coverage and patching all carry their own cost. Our post on deploying GPT-OSS-20B on a single GPU walks through the full stack for one model, and the GPU spot pricing break-even analysis shows where self-hosting starts to make financial sense.
For many Australian organisations the honest comparison is not vLLM against SGLang. It is a self-hosted stack, with its inference server, GPUs and engineering time, against a managed model like Claude where none of that is your problem. Self-hosting can win on cost at high volume or under strict residency rules, but it is an engineering project, not a weekend task.
Getting the decision right
If you are weighing a self-hosted open-weight model against Claude, or you have already chosen self-hosting and want the infrastructure sized properly, we can help you work through the numbers. See our services or book a time to talk it through.
