GoModel vs vLLM - AI Gateway vs Inference Engine

Comparison

GoModel vs vLLM Comparison

vLLM (Apache 2.0, a PyTorch Foundation project, 90k+ GitHub stars) is the de facto standard engine for serving open-weight models on your own hardware: PagedAttention, continuous batching, and support for NVIDIA, AMD, TPUs and other accelerators. It executes models; it does not proxy to providers. That is why it is not in the gateway benchmark - the benchmark measures gateway overhead in front of a mock backend, and vLLM is the backend layer.

Category	GoModel	vLLM
What each one is
Category	AI gateway in front of model providers	Inference engine that serves models on your GPUs
What it does with a request	Authenticates, routes, caches, meters, logs, forwards to a provider	Batches it onto the GPU and generates the tokens (PagedAttention, continuous batching)
Hardware	Any CPU box - 42.7 MB RAM under load in the benchmark	GPUs and accelerators: NVIDIA, AMD, TPUs and more
Runtime	One Go binary, 14.4 MB image	Python and CUDA stack, multi-gigabyte images
Models	31 hosted providers behind one endpoint	Open-weight models you download, usually from Hugging Face
License	MIT	Apache 2.0 (PyTorch Foundation project)
Where they overlap: the OpenAI-compatible server
OpenAI-compatible endpoint	Yes, plus a native Anthropic Messages endpoint	Yes (vllm serve)
Authentication	Virtual keys, scoped workflows, budgets, key rotation	One static token via --api-key, or nothing
Many models behind one endpoint	Aliases and virtual models across every provider	One model per server process
Failover	Retries, circuit breaker, fallback across providers	None - if the server is down, the model is down
Caching	Exact and semantic response caching - repeats never hit a GPU	Prefix (KV) caching of attention states - saves compute, not requests
Observability	Cost, usage, audit logs, dashboard, Prometheus	Engine metrics via Prometheus: throughput, KV cache, queues
Raw token throughput	Not its job - it proxies and gets out of the way	State of the art - this is the whole point of vLLM

Rows marked as an advantage are about depth of that specific concern, not about which project is better - a gateway winning a serving-throughput row would mean something had gone very wrong. vLLM facts are from its documentation and repository as of August 2026 (latest release v0.28.0, 26 August 2026).

Better together: GoModel in front of vLLM

Set one environment variable and your vLLM server becomes a GoModel provider, next to OpenAI, Anthropic and 28others - with the gateway's controls in front of it.

What GoModel adds to a vLLM deployment

Real authentication. vLLM offers a single static token at best; GoModel puts virtual keys, scoped workflows and key rotation in front of it, so a shared GPU box stops being a shared secret.
Budgets and quotas per team on hardware everyone wants. GPU time is the scarcest resource in the building; GoModel meters who is using it.
Failover from self-hosted to cloud. An alias can resolve to your vLLM server first and fall back to a hosted provider when the box is saturated, restarting or down.
One endpoint for both worlds. Apps call one API and switch between your Llama on vLLM and a frontier model by changing the model name, not the client.
Exact and semantic caching, so repeated prompts return from the gateway without spending GPU seconds at all.

When you do not need a gateway at all

One app, one vLLM server, same rack. Call it directly; add GoModel when a second team, a second model, or an access question shows up.
You are chasing maximum tokens per second on a single box and nothing else. Any proxy hop is overhead - GoModel keeps it to a couple of milliseconds, but the honest minimum is zero.

Set VLLM_BASE_URL=http://your-vllm:8000/v1 and vLLM registers as a keyless provider (add VLLM_API_KEY only if you started it with --api-key). Call models as vllm/meta-llama/Llama-3.1-8B-Instruct - slash-shaped Hugging Face IDs work as-is. See the GoModel documentation for details.

Try it against your own workload

Benchmarks are an argument; your traffic is the proof. GoModel speaks the OpenAI and Anthropic APIs natively, so pointing an existing app at it is a base-URL change.

1 · Run GoModel

docker run --rm -p 8080:8080 \
  -e LOG_FORMAT=text \
  -e OPENAI_API_KEY="your-openai-key" \
  enterpilot/gomodel

2 · Point your SDK at it

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="your-gomodel-key",
)

Full setup, providers, and configuration live in the documentation. Questions? Ask on Discord or book a 30-minute call.