Superlinked SIE

Superlinked SIE is an open-source inference layer for agent apps, running models in your own cloud to keep prompts and documents in-house.

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What Superlinked SIE is

Superlinked’s SIE is an open-source inference layer for agent applications. It is designed to run the models your agents call from a cluster in your own cloud, instead of routing those requests to separate external APIs.

The product focuses on open models end to end, with support for embeddings, reranking, OCR, extraction, guardrails, and agent loop execution. The site positions it for teams that want to keep prompts and documents inside their infrastructure while using one deployment to serve multiple model tasks.

Core capabilities

Self-hosted agent inference

SIE serves models from a single cluster in your cloud so agent workflows can call inference, parsing, reranking, extraction, and guardrails without sending prompts or documents to a third-party host.

Two deployment modes

The deployment docs describe both a simple Docker server and a Kubernetes cluster with gateway, config service, NATS JetStream, and GPU worker pods for production scaling.

Multiple integration paths

The integration docs support framework adapters, a native SDK, and OpenAI-compatible endpoints, so teams can connect existing RAG code or migrate incrementally.

Multi-output model workflows

The docs show output types for dense, sparse, and multivector embeddings, plus reranking and extraction for entities, relations, classifications, and object detection.

Operational controls

The deployment guide lists model loading on first request, persistent caches, specific-model startup, and air-gapped installs with mirrored snapshots for offline environments.

Common use cases

RAG and retrieval stacks
Use SIE to power retrieval pipelines that need dense, sparse, or multivector embeddings, plus reranking, inside an existing LangChain, LlamaIndex, Haystack, Qdrant, Weaviate, Chroma, LanceDB, DSPy, or CrewAI stack.
Custom agent workflows
Use the native SDK when you want a custom agent pipeline with structured outputs, extraction, and fine-grained control over batching, timing, and model choice.
Document processing and enrichment
Use the OCR, document-to-markdown, extraction, and guardrail capabilities when your agent needs to process PDFs, scans, Office files, or other documents before handing structured data to downstream steps.
Local and production deployment
Use the Docker path for local development or low-traffic services, and move to Kubernetes when you need autoscaling, high availability, or scale-to-zero on cloud GPU node pools.

Pros and Cons

Pros

Keeps prompts and documents in your own cloud or an air-gapped environment.
Supports multiple model tasks from one cluster instead of separate server-per-model deployments.
Offers several integration options, including framework adapters, a native SDK, and OpenAI compatibility.
Can run as a simple Docker server for development or as a Kubernetes cluster for production.
Supports open models and a broad set of workflows including embeddings, reranking, extraction, OCR, and guardrails.

Cons

The source does not provide a published pricing page, so plan details and costs are not confirmed.
The Kubernetes setup has several prerequisites, including GPU nodes, ingress, and cluster services, which makes production deployment more involved than the Docker path.
Scale-to-zero deployments on Kubernetes can take 5 to 7 minutes to become ready again, according to the deployment guide.

FAQ

How do you deploy SIE?

SIE can run either as a single Docker server or as a Kubernetes cluster. The deployment guide says Docker is the simplest path for a single server or low-traffic service, while Kubernetes is for horizontal scaling, autoscaling to zero, and high availability.

How can SIE fit into an existing stack?

The integration guide shows three paths: framework adapters for common RAG stacks, the native SDK for full feature access, and OpenAI compatibility for existing code that already calls OpenAI embeddings endpoints.

Can SIE run without a GPU?

Yes. The deployment docs say SIE can run on CPU for development and low-traffic workloads, though GPUs are strongly recommended for production inference at scale, especially for batch encoding.

Which environments does SIE support?

The homepage and docs indicate SIE supports AWS, GCP, Azure, and air-gapped environments. The deployment guide also describes Kubernetes cluster setups on AWS EKS, GCP GKE, and Azure AKS.

Is pricing published?

The pricing page is not available at the provided URL and returns a 404, so the source does not confirm current plans or pricing structure.