What is Self-Hosted Inference?
Self-hosted inference is the practice of running AI model inference on your own infrastructure, whether 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
# Deploy to AWSterraform applyhelm install sie oci://ghcr.io/superlinked/charts/sie-cluster
# Use from Pythonpip install sie-sdkfrom sie_sdk import SIEClientfrom 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 is that you own the ops, but you’re not subject to third-party outages or rate limits.