---
title: What is Self-Hosted Inference?
description: Self-hosted inference is the practice of running AI model inference on your own infrastructure — your own cloud account (AWS, GCP, Azure) or on-premises hardware — rather than sending requests to a third-party managed API. You control the hardware, the models, the configuration, and crucially, where your data goes.
canonical_url: https://superlinked.com/glossary/what-is-self-hosted-inference
last_updated: 2026-06-02
---

# What is Self-Hosted Inference?

Self-hosted inference is the practice of running AI model inference on your own infrastructure — your own cloud account (AWS, GCP, Azure) or on-premises hardware — rather than sending requests to a third-party managed API. You control the hardware, the models, the configuration, and crucially, where your data goes.

---

## Why does self-hosted inference matter?

Managed model APIs (OpenAI, Cohere, Voyage AI, etc.) are convenient for prototyping, but they introduce three problems at production scale:

### 1. Cost
Managed APIs charge per token. For embedding workloads — where you may encode millions of documents regularly — per-token pricing becomes the dominant infrastructure cost. Self-hosting on your own GPUs can reduce this by up to 50x.

### 2. Data privacy
Every request to a managed API sends your data to a third party's servers. For regulated industries (legal, healthcare, finance, government), this is often a compliance blocker. Self-hosted inference keeps data entirely within your own cloud account.

### 3. Model control
Managed APIs offer a fixed menu of models. Self-hosted inference lets you run any open-source model — including fine-tuned or LoRA-adapted models — and swap them without changing your integration.

---

## Self-hosted inference vs managed APIs

| | Managed API | Self-hosted inference |
|---|---|---|
| Pricing | Per token | Pay for your own GPUs |
| Cost at scale | High | Up to 50x lower |
| Data location | Third-party servers | Your own cloud |
| Model selection | Fixed menu | Any open-source model |
| Setup complexity | None | Requires deployment |
| SOC2 / compliance | Depends on vendor | You control it |

---

## What does self-hosted inference involve?

At minimum, self-hosting an embedding or reranking model requires:

- **GPU provisioning** — selecting and provisioning appropriate GPU instances (e.g. A100, L4)
- **Model serving** — a server that loads the model and exposes an API endpoint
- **Batching and concurrency** — handling multiple requests efficiently to maximise GPU utilisation
- **Monitoring** — tracking latency, throughput, and GPU utilisation
- **Model management** — loading, swapping, and updating models without downtime

This is non-trivial to build well. Tools like SIE handle all of this out of the box.

---

## How does SIE simplify self-hosted inference?

SIE (Superlinked Inference Engine) is an open-source inference server designed specifically for search and document processing workloads. It deploys into your own AWS or GCP account and handles:

- GPU cluster management via Terraform + Helm
- Support for 85+ SOTA embedding, reranking, and extraction models
- LoRA hot-loading (swap adapters without restarting the server)
- Automatic batching for GPU efficiency
- A simple SDK for encoding and reranking

```bash
# Deploy to AWS
terraform apply
helm install sie oci://ghcr.io/superlinked/charts/sie-cluster

# Use from Python
pip install sie-sdk
```

```python
from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("https://your-sie-endpoint")
vectors = [r["dense"] for r in client.encode("BAAI/bge-m3", [Item(text=d) for d in documents])]
```

Your data stays in your AWS or GCP account. SIE is Apache 2.0 licensed and SOC2 Type 2 certified.

---

## What workloads benefit most from self-hosted inference?

Self-hosted inference is particularly valuable for:

- **High-volume embedding pipelines** — re-indexing large document corpora frequently
- **Real-time semantic search** — low-latency encoding at query time
- **RAG applications** — both indexing and retrieval steps at scale
- **Regulated data** — legal, medical, financial documents that can't leave your environment
- **Custom fine-tuned models** — running LoRA adapters trained on your domain

---

## Frequently asked questions

**Do I need a dedicated ML team to run self-hosted inference?**
Not with SIE. Deployment is handled via standard DevOps tooling (Terraform, Helm). If you can deploy a Kubernetes application, you can deploy SIE.

**What GPUs does SIE support?**
SIE supports A100-40GB, A100-80GB, L4, and L4-spot instances on AWS and GCP. Spot instances further reduce cost.

**Is self-hosted inference more reliable than managed APIs?**
You control availability, so reliability depends on your infrastructure. SIE's cluster mode supports horizontal scaling and failover. The trade-off: you own the ops, but you're not subject to third-party outages or rate limits.

---

## Related resources

- [SIE deployment documentation](/docs/deployment)
- [SIE vs TEI vs OpenAI benchmark](/docs/examples/benchmark)
- [Browse supported models](/models)
- [What is a LoRA adapter?](/glossary/what-is-a-lora-adapter)
- [What is semantic search?](/glossary/what-is-semantic-search)