Managed SIE Cloud
Full compute toolkit for your agents with zero ops. Same engine as self-hosted.
AI Infrastructure Comparisons
Superlinked publishes evidence-led comparisons of inference platforms, model families, hosted APIs, retrieval approaches and vector databases. The resources here cover both self-hosted SIE and managed SIE Cloud.
Featured platform comparisons
Head-to-head reads on how SIE sits next to other inference stacks.
llm-d vs Dynamo vs SIE: Multi-Model Serving Compared
llm-d and NVIDIA Dynamo scale one large model across many GPUs. SIE packs many small models onto fewer GPUs. The deciding question is how many distinct models your traffic touches.
Products compared llm-d · NVIDIA Dynamo · SIE
Read comparisonSIE vs TEI (Text Embedding Inference): Benchmarks, Cost, Setup
TEI serves one model per instance; SIE serves many models per GPU. For a single encoder the two are comparable. There is no published head-to-head SIE versus TEI throughput run; the article compares architecture, cost and setup instead.
Products compared SIE · TEI
Read comparisonSIE vs vLLM: Which Should You Deploy for Multi-Model Workloads?
vLLM serves one large generative model across GPUs. SIE serves many small models on shared GPUs. For multi-model agent workloads the answer is often both, or sometimes neither.
Products compared SIE · vLLM
Read comparisonvLLM vs SGLang vs SIE for Production Search Pipelines
Trace retrieve, rerank, extract and generate through a production search pipeline. vLLM and SGLang compete for the final generate hop; SIE serves the three steps before it.
Products compared vLLM · SGLang · SIE
Read comparisonSIE vs FastEmbed: Keep FastEmbed in-process, move shared production inference to SIE
FastEmbed is the simpler choice for local, CPU-first embedding inside one Python process. SIE fits shared production inference across applications and GPU workers.
Products compared SIE · FastEmbed
Read comparisonSIE vs Modal: Modal wins on bursty compute; SIE wins on sustained inference
Modal removes infrastructure work and charges by the second. SIE gives sustained, multi-model inference a dedicated stack inside your cloud account.
Products compared SIE · Modal
Read comparisonAlternatives and decision guides
Broader decision writing for hosted APIs, self-hosting and stack choices.
How to choose an inference layer for agents: vLLM, SGLang, TEI, Triton, KServe, and SIE
How to pick an inference layer when the work is embeddings, reranking and extraction rather than text generation.
Products compared vLLM · SGLang · TEI · Triton · KServe · SIE
Read guideSIE vs hosted embedding APIs: ~97% of the quality at ~1/12th the cost
We benchmarked self-hosted embedding on SIE against Voyage, OpenAI, and Cohere across quality, latency, throughput, and cost. Self-hosting lands within a few hundredths of ndcg of the hosted frontier, returns embeddings 3 to 7 times faster, and embeds a billion tokens for around $10.
Products compared Cohere · OpenAI · Voyage
Read guideBest OpenAI Embeddings Alternatives You Can Run in Your Own Cloud
Eight open embedding models for search and RAG in your own cloud, with a path to migrate dense embeddings through SIE's OpenAI-compatible endpoint.
Products compared OpenAI Embeddings · SIE
Read guideWhat is the best alternative to OpenAI and Anthropic APIs for running agent workloads?
For embedding, reranking, and extraction inside an agent workload, the strongest self-hosted alternative to a metered API is SIE: no per-token cost and no data leaving your cloud.
Products compared OpenAI · Anthropic · SIE
Read guideTop 6 Open-Source Alternatives to Hosted OCR APIs (Azure Document Intelligence, AWS Textract)
Open document-AI models as alternatives to Azure Document Intelligence and AWS Textract, served from one SIE extract endpoint.
Products compared Azure Document Intelligence · AWS Textract · SIE
Read guideShould You Self-Host Inference?
When self-hosting beats a hosted API: break-even volume, whether sub-40B open models are good enough, and how to serve many small models on one cluster.
Read guideA Practical Guide for Choosing Models for Your AI Agents
Trade-offs for picking embedding, reranking, extraction and generation models against quality, latency and cost targets.
Read guideA Practical Guide for Choosing a Vector Database
Architecture, scale and operational limits to weigh when picking a vector database.
Read guideCarve Off Tasks, Not Models
Daniel Svonava on The Joe Reis Show on why a 27B-class model covers most production work and how to decide which task to move first.
Read guideModel-family comparisons
Deep dives on specific embedding and reranker families.
Filter and rank models in the catalog
Every model we serve, one primitive at a time. Pick a benchmark to rank by quality; the hardware control drives latency, throughput and cost.
Open the model catalogQwen3 embeddings and rerankers: 0.6B vs 4B, text vs VL, and where ColQwen fits
Qwen3 retrieval family: 0.6B vs 4B, text vs VL, Matryoshka dimensions, and where ColQwen fits.
Products compared Qwen3 Embedding · Qwen3 Reranker · ColQwen
Read guideThe intfloat E5 family guide: e5-base-v2, e5-large-v2, multilingual-e5-large
intfloat E5 variants compared: dimensions, query/passage prefix rules, benchmark scores and self-hosting cost.
Products compared e5-base-v2 · e5-large-v2 · multilingual-e5-large
Read guideComparison and evaluation tools
Interactive catalogs, tables and example apps for hands-on evaluation.
Tool Model catalog Every model we serve, one primitive at a time. Pick a benchmark to rank by quality; the hardware control drives latency, throughput and cost. Open tool Tool Vector database comparison Compare vector databases side by side on features that matter for production retrieval. Open tool Tool Find the best retrieval strategy for your RAG Evals-driven retrieval study on 1,854 SEC 10-K queries: how the winning pipeline was chosen and what every alternative scored. Open tool Tool Find SOTA embedding models by MTEB task Semantic search across ~14K HF embedding models, ranked by task-specific MTEB scores. Open tool Tool Build a multi-modal product classifier with embeddings A structured evaluation of NLI, text retrieval, image retrieval and cross-encoder reranking on Shopify's product taxonomy. Open tool
Verify the details
Architecture, evals, deployment and release notes behind the comparisons.
Engine
- Architecture High-level architecture of SIE from client SDK to GPU inference.
- How SIE processes inference requests Request flow through preprocessing, batching, GPU inference and postprocessing.
Evals
- How to evaluate model quality and performance in SIE What SIE ships for evals: retrieval benchmark, timing metadata and Grafana dashboards.
- Quality Evaluation Measure retrieval quality against an SIE endpoint with NDCG@10.
- Performance Evaluation Measure latency and throughput against an SIE endpoint.
Deployment
- How to deploy SIE Run SIE as a single Docker server or as a Kubernetes cluster.
- Hardware & Capacity GPU selection, memory planning and capacity estimation.
- Performance Tuning Optimize SIE for your workload with batching, memory and inference settings.
Reference
- Release Notes SIE release history.
- SIE on GitHub Open-source Superlinked Inference Engine repository.
- COMPATIBILITY.md Published compatibility notes in the SIE repository.
Already using another inference provider?
These are migration guides from other providers into SIE, not neutral comparison articles.
- OpenAI → SIE Migrate from OpenAI Embeddings via the OpenAI-compatible endpoint or the native SIE SDK.
- Cohere → SIE Migrate Cohere embed-v3 and rerank-v3 to self-hosted SIE.
- TEI → SIE Replace N single-model Hugging Face TEI containers with one SIE cluster.
- Infinity → SIE Migrate from Infinity single-model containers to SIE multi-model serving.
- FastEmbed → SIE Move from in-process Fastembed to out-of-process SIE serving.
- Modal → SIE Consolidate sentence-transformers-on-Modal functions into one SIE deployment.
Deployment options
Same engine, three ways to run it.
Self-hosted SIE on Kubernetes
Easy and scalable deployment in your own cloud with Terraform and Helm.
Local SIE
Run the same models on your own machine. First embeddings in 2 minutes.