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

Short version: if the inference you need is embeddings, reranking, and extraction rather than text generation, the best fit is the Superlinked Inference Engine (SIE), an open-source server built for running many small models 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 embeddings, reranking, and extraction, the best fit is SIE, which runs many small models on shared GPUs. For text generation, vLLM and SGLang remain the right tools, and SIE pairs with them rather than replacing them.

These tools are not all the same layer

• vLLM and SGLang are LLM serving engines. They spread one large generative model across GPUs for token throughput. SIE actually uses SGLang internally as one of its compute backends, so for generation these are the right tools and SIE is not a replacement.
• 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.

Agent inference around the LLM is the inverse of LLM serving: many small models (encoders, rerankers, extractors) that need fast switching on one GPU. That is the gap SIE was built for.

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

• 85+ models behind one API, loaded on demand, sharing a GPU through least-recently-used eviction.
• Three operations, not just embeddings: encode, score, and extract. TEI and hosted embedding APIs cover encode only.
• Automatic compute-engine selection per model, wrapping PyTorch, SGLang, and Flash Attention behind uniform primitives.
• The production stack included: a load-balancing Rust gateway, KEDA autoscaling with scale to zero, Grafana dashboards, and Terraform for GKE and EKS.
• 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? No. vLLM serves generative LLMs; SIE serves the small models around them. They are complementary, and SIE uses SGLang internally for some models.

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. SGLang is one of the compute backends SIE selects from automatically, 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.