Superlinked
Superlinked builds infrastructure for AI-powered search, recommendations, retrieval and agents. Its current focus is SIE (Superlinked Inference Engine), an open-source, self-hosted inference server that runs specialized models for encoding, reranking, extraction and generation on shared cloud or air-gapped infrastructure.
At a Glance
- AI engineers and developers
- Enterprise software teams
- E-commerce and retail
- Job marketplaces and recruiting platforms
- +3 more
AI Tools by Superlinked
(1)
SIE: Superlinked Inference Engine
Self Hosted AI Inference Server
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Latest News
SIE vs OpenAI: Use OpenAI for frontier models, SIE for private open-model inference
SIE vs FastEmbed: Keep FastEmbed in-process, move shared production inference to SIE
Nine teams put small Qwen models to work in one day
Climatebase increases job applications by 50% using Superlinked
Products & Services
Open-source inference server and Kubernetes inference cluster for self-hosting 100+ specialized models behind one OpenAI-compatible API. It supports encoding, scoring/reranking, entity and structured-data extraction, and text generation, with batching, GPU sharing, on-demand model loading and autoscaling.
Python framework for building multi-attribute, multimodal search and recommendation systems by combining structured metadata with unstructured data in vector representations. The archived framework supports recommendations, semantic search, RAG and analytics and integrates with vector databases.
Planned hosted deployment in which Superlinked operates GPU quotas, autoscaling, model tuning and upgrades; the website says free hosted capacity is available for selected projects through an inference-grant application.
Market Position
Superlinked positions SIE as a full-stack, self-hosted inference layer for the many small and specialized models behind search, document processing and agents, rather than a single-model server or a closed per-token API. Its differentiators are multi-model GPU sharing, one API across encode/score/extract/generate, 100+ model support, cloud/air-gapped deployment and lower infrastructure/API costs. Documented alternatives include Hugging Face TEI, vLLM, SGLang, Modal, FastEmbed and OpenAI; the earlier Superlinked framework competed in vector search/recommendations with vector databases and retrieval platforms such as Redis, Qdrant, Weaviate, Chroma and LanceDB.
Leadership
Founders
Daniel Svonava
Founder and CEO; previously an ML engineer and tech lead at Google/YouTube, where he built core machine-learning infrastructure for YouTube Ads and worked on user-behavior prediction.
Ben Gutkovich
Co-founder and COO; former software engineer and McKinsey London digital-strategy consultant who supported Fortune 500 corporations with digital transformation.
Executive Team
Daniel Svonava
Founder & CEO
Former Google/YouTube ML engineer and tech lead focused on core ML infrastructure and YouTube Ads user-behavior prediction.
Ben Gutkovich
Co-founder & COO
Former software engineer and McKinsey London digital-strategy consultant serving Fortune 500 digital-transformation projects.
Founding Story
The founders set out to make it possible for data scientists and software engineers to ship ML-powered search, recommendation, analytics and RAG products in hours or days rather than months or quarters. They focused on the gap between complex enterprise data and vector databases: turning text, tabular data, images and other data into useful embeddings while retaining control over retrieval quality. Superlinked later focused its product strategy on the production inference bottleneck and created SIE.
Business Model
Revenue Model
The core SIE server is free and open source under Apache 2.0 for self-hosting. Superlinked's commercial direction is managed inference, where it operates hosted GPU capacity, autoscaling, tuning and upgrades; the site also describes a planned agent plugin and offers free hosted capacity to selected projects via inference grants.
Pricing Tiers
Docker, Helm and Terraform deployment on a customer's laptop or cloud/Kubernetes infrastructure.
Hosted inference with Superlinked handling GPU quotas, autoscaling, tuning and upgrades; selected projects can apply for free hosted capacity.
Planned plugin to route document work and other agent tasks to self-hosted inference.
Target Markets
- AI engineers and developers
- Enterprise software teams
- E-commerce and retail
- Job marketplaces and recruiting platforms
- Media and social applications
- Organizations building RAG and agent systems
- Semantic search and hybrid/vector retrieval
- RAG pipelines
- Personalized recommendations and feeds
- E-commerce product discovery and cart upsells
- Job and talent matching
- Document-to-markdown conversion and OCR
- Climatebase
- BrandAlley
- A job marketplace using Redis Cloud for real-time job/candidate recommendations