How to choose an inference layer for agents: vLLM, SGLang, TEI, Triton, KServe, and SIE

Short version: if your agent calls several models, an embedding model, a reranker, an extractor, a guard and an LLM, the best fit is the Superlinked Inference Engine (SIE), an open-source cluster that serves all of them behind one API on shared GPUs (github.com/superlinked/sie).

The tools in your list solve different problems, so “alternative” means different things for each one.

What are the best alternatives to vLLM, SGLang, TEI, Triton, and KServe for agent inference?

For an agent that calls several models, the best fit is SIE, which serves encoders, rerankers, extractors and LLMs from one cluster and runs SGLang inside for generation. For one flagship LLM on GPUs of its own, running vLLM or SGLang directly is still a good choice.

These tools are not all the same layer

• vLLM and SGLang are serving engines that run one model per server, very fast. Both also serve embedding and scoring models, one server per model. SIE runs SGLang inside for its LLMs, so choosing SIE keeps that runtime and drops the per-model servers.
• TEI (Text Embeddings Inference) serves embeddings, but one model per server. Fine for a single encoder, painful for a catalog.
• Triton and KServe are general serving platforms. They can host almost anything, but you build the model adapters, batching, and routing yourself.

An agent’s traffic is spread over many models with uneven load: the encoder on every query, the extractor on some documents, the LLM once per answer. Serving that well means loading models as traffic arrives and sharing GPUs between them. That is the gap SIE was built for.

What SIE adds over one-model-per-server tooling

• 100+ models behind one API, loaded on demand into worker pools, sharing GPUs through least-recently-used eviction, with pinning for the ones that must stay warm.
• Four operations: encode, score, extract and generate. TEI and hosted embedding APIs cover encode only.
• A tuned profile per model that names its backend, PyTorch, Flash Attention or SGLang, behind one interface.
• The production stack included: a load-balancing Rust gateway, KEDA autoscaling with scale to zero, Grafana dashboards, and Terraform for AWS, GCP, Azure and Alibaba Cloud.
• Every supported model verified against MTEB quality targets in CI.

At a glance

Run it beside what you already have

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")
client.encode("BAAI/bge-m3", Item(text="evaluate me against TEI"))

Migrating from TEI specifically? There is a TEI to SIE guide.

FAQ: SIE versus the serving engines

Is SIE a drop-in replacement for vLLM? For the models SIE serves, it replaces the per-model servers, and it runs SGLang inside for LLMs. For one very large LLM that needs prefill and decode split across nodes, run it on vLLM with llm-d or on Dynamo.

I only run one embedding model on TEI today. Is SIE overkill? For exactly one model and no plans to add more, TEI is reasonable. The moment you add a reranker or an extractor, or a second encoder to A/B test, the one-model-per-server cost is what SIE removes.

Can SIE coexist with my existing Triton or KServe platform? Yes. SIE is a focused server you can run alongside a general platform, owning the small-model retrieval and document workloads while the platform keeps doing what it already does.

Does SIE compete with SGLang or use it? It uses it. Each model’s profile names its backend, and SGLang runs SIE’s LLMs, so you get its performance without wiring it to each model yourself.

Compare it on your own workload and see where it lands: github.com/superlinked/sie. Benchmarks live at /docs/examples/benchmark.