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Superlinked builds SIE, an open-source Apache 2.0 Kubernetes inference engine that runs 100+ models behind one API on shared GPUs. It offers dense, sparse, and multi-vector embeddings, reranking, extraction, and generation, with LangChain, LlamaIndex, and vector database integrations.
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Short answers to the questions buyers and builders commonly ask about Superlinked. Each answer cites the shared ledger below, where every source is listed once.
SIE (Superlinked Inference Engine) is an open-source inference server for AI models that e · Self-hosted inference engine for search and document processing that runs 100+ models behi · Dense, sparse, and multi-vector embeddings, reranking, and extraction (entities, relations · Text generation served from a separate GPU generation image (or Apple Silicon MLX backend)
SIE exposes four primitives: - Encode converts text or images to vectors for semantic search and RAG - Score reranks query-document pairs for higher-precision retrieval - Extract pulls entities and structured data from unstructured text - Generate runs text generation on open LLMs you host yourself
Agent plugin that drops into an agent stack to route document work (parsing, extraction, s · Convert PDFs, Office files, and scans to clean markdown; produce schema-valid JSON that is · Search and document processing workloads powered by specialized models (embeddings, rerank · End-to-end applications including product search, fraud-risk scoring, contract review, vis
Agent plugin Drops into your agent stack and routes document work (parsing, extraction, summarization, question answering, image description) off your frontier-model bill. It can also redact sensitive data before it reaches a third-party model.
Sparse, multi-vector (ColBERT), reranking, and extraction features are not exposed via the
Sparse, multi-vector, reranking, and extraction are not part of the OpenAI API shape; they require the native SDK or a framework adapter.
OpenAI-compatible endpoints /v1/embeddings, /v1/chat/completions, /v1/completions, and /v1 · Native Python (sie-sdk) and TypeScript (@superlinked/sie-sdk) client SDKs · SIE exposes an OpenAI-compatible API endpoint at http://localhost:8080/v1 for dense embedd
SIE speaks the OpenAI API for both embeddings and generation. /v1/embeddings and /v1/chat/completions are drop-in
SIE has official integrations with LangChain and LlamaIndex. · Supports PyTorch, SGLang, and Flash Attention compute engines selected automatically per m · Framework adapters for LangChain, LlamaIndex, Haystack, Qdrant, Weaviate, Chroma, LanceDB, · DSPy
Connect to LangChain or LlamaIndex | Integrations
Self-host options: Docker on a laptop, Helm on a Kubernetes cluster, Terraform on AWS, GCP · Managed cloud deployment with zero data retention; free hosted capacity is offered for sel · Runs as a self-hosted inference engine on shared GPUs in the customer's own cloud · Runs in Kubernetes (with SIE server sidecar per worker pod and sie-server adapter) or stan
Self-host Docker on a laptop, Helm on your cluster, Terraform on AWS, GCP, and Azure. Air-gapped installs run from mirrored model snapshots.
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Open Source Inference for Agents | Superlinked
SIE is open source under Apache 2.0 and available on GitHub · SIE exposes four API primitives: Encode, Score, Extract, and Generate · SIE supports 100+ models out of the box
Superlinked, Inc., 28 Geary Street, Suite 650, San Francisco, CA 94108, USA
SIE (Superlinked Inference Engine) is an open-source inference server for AI models that exposes four API primitives: Encode, Score, Extract, and Generate.
SIE supports 100+ models out of the box (encoders, rerankers, extractors, and generation models).
SIE is open source and available on GitHub.
Self-host options: Docker on a laptop, Helm on a Kubernetes cluster, Terraform on AWS, GCP, and Azure; air-gapped installs run from mirrored model snapshots.
Managed cloud deployment with zero data retention; free hosted capacity is offered for selected projects.
SIE wraps SGLang, vLLM, TensorRT-LLM, and TEI as runtimes/backends, plus native PyTorch (Python) and Candle (Rust); multiple models share one GPU via LRU eviction.
Agent plugin that drops into an agent stack to route document work (parsing, extraction, summarization, question answering, image description) off a frontier-model bill, with optional sensitive-data redaction before any third-party model ca
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
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These tools share a workflow, capability or audience with Superlinked. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Convert PDFs, Office files, and scans to clean markdown; produce schema-valid JSON that is extracted or generated.
SIE has official integrations with LangChain and LlamaIndex.
SIE is a Kubernetes inference cluster: a stateless gateway publishes work to one queue and worker pods pull jobs, form full batches, and share GPUs across many models.
Self-hosted inference engine for search and document processing that runs 100+ models behind a single API on shared GPUs
Runs as a self-hosted inference engine on shared GPUs in the customer's own cloud
SIE is released under Apache 2.0
Raised $12M+ from Index Ventures, Theory Ventures, Samsung Next, and others
Search and document processing workloads powered by specialized models (embeddings, rerankers, vision, OCR, classification)
Embeddings, rerankers, vision models, OCR, and classification models
Supports PyTorch, SGLang, and Flash Attention compute engines selected automatically per model
Runs in Kubernetes (with SIE server sidecar per worker pod and sie-server adapter) or standalone Docker
Supports LoRA adapters for fine-tuned model variants
Operates across San Francisco, London, Budapest, and Tel Aviv
Dense, sparse, and multi-vector embeddings, reranking, and extraction (entities, relations, classifications, object detection)
Text generation served from a separate GPU generation image (or Apple Silicon MLX backend)
End-to-end applications including product search, fraud-risk scoring, contract review, vision-first document RAG, and multi-model agent workflows
Framework adapters for LangChain, LlamaIndex, Haystack, Qdrant, Weaviate, Chroma, LanceDB, DSPy, and CrewAI (Python) plus LangChain.js, LlamaIndex.ts, Chroma, and LanceDB (TypeScript)
OpenAI-compatible endpoints /v1/embeddings, /v1/chat/completions, /v1/completions, and /v1/responses served by the SIE gateway
Native Python (sie-sdk) and TypeScript (@superlinked/sie-sdk) client SDKs
Self-hosted Docker (single sie-server) or Kubernetes (sie-gateway, sie-config, NATS JetStream, and GPU worker pods)
Managed SIE deployments offered directly by Superlinked
Production Kubernetes installs on GCP, AWS, and Azure with external Terraform modules (terraform-google-sie, terraform-aws-sie, terraform-azure-sie)
Workload Identity (GCP/Azure) or IRSA (AWS) bound to a sie-server service account for credential-less access to model cache buckets
Sparse, multi-vector (ColBERT), reranking, and extraction features are not exposed via the OpenAI-compatible API and require the native SDK or a framework adapter
Public 'sie' GitHub repository hosting example applications and accepting community pull requests
DSPy
ChromaDB
Qdrant
1024 dimensions for BAAI/bge-m3 dense vectors
