Sie is live · free plan; usage-based paid tier · last verified 14 Nov 2026 · viability healthy
Sie
SIE runs small open models for embeddings, reranking, OCR and field extraction on your own GPUs or via its API
The cost math is the reason to look: if your agents are shipping a million documents or pages through a frontier model, SIE's benchmark saves are large enough to justify the migration work. The scheduler and GPU sharing are the real product, not the model list. Pass if embedding calls are a rounding error on your bill or nobody on the team wants to babysit GPUs.
Verdict generatedVerified 11h ago · liveness 73/100 · cite: rightaichoice.com/tools/sie
- Search and RAG teams paying per-token for embeddings and reranking at steady volume
- Agent builders running a fleet of small open models that need to share GPUs
- Document pipelines needing OCR, extraction, and PDF-to-markdown behind one API
- Teams with data-residency or air-gap rules that rule out hosted inference APIs
- Teams with no Kubernetes or GPU-operations experience and no plan to hire it
- Anyone serving a single large LLM, where vLLM or llm-d is the simpler path
- Bursty, low-volume workloads where usage-based pricing stays cheaper
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Skip SIE if nobody on your team wants to run Kubernetes or manage GPU capacity, or if your inference volume is bursty and low enough that hosted per-token pricing stays cheaper than a cluster you operate yourself.
Self-hosting shifts GPU quota, cluster capacity, and upgrade labor onto your team — the license is $0 but the L4 or H100 instances behind it are not.
Self-hosted SIE is Apache 2.0 at $0 — the real cost is the GPUs and the Kubernetes labor you already own. Cheaper than per-token hosted inference once embeddings, reranking, and OCR run at steady volume (Superlinked's benchmark claims 50x on gte-multilingual versus text-embedding-3 all-in on AWS EKS with L4s), but more expensive than Modal or hosted APIs for bursty, low-volume work. Managed SIE and the Agent plugin are listed as upcoming.
In short
Sie — SIE runs small open models for embeddings, reranking, OCR and field extraction on your own GPUs or via its API. Best for Search and RAG teams paying per-token for embeddings and reranking at steady volume, Agent builders running a fleet of small open models that need to share GPUs, Document pipelines needing OCR, extraction, and PDF-to-markdown behind one API. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is Sie?
Sie — SIE runs small open models for embeddings, reranking, OCR and field extraction on your own GPUs or via its API. Best for Search and RAG teams paying per-token for embeddings and reranking at steady volume, Agent builders running a fleet of small open models that need to share GPUs, Document pipelines needing OCR, extraction, and PDF-to-markdown behind one API. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
SIE (Superlinked Inference Engine) is an inference server for the small, task-specific open models that agents throw most of their work at: encoders, rerankers, OCR readers, field extractors, PDF-to-markdown converters, guard classifiers, compact chat LLMs and transcribers. The catalog is organized by the job you need done rather than by model family — semantic search, rerank, hybrid sparse search, ColBERT passage matching, PDF-to-Markdown, invoice and receipt extraction, structured output, request routing, entity and knowledge-graph extraction, image Q&A, object detection and PII redaction. The vendor page lists 150 models with 9 integrations, published under Apache 2.0 with SOC 2 Type 2. Three deployment shapes share the same model IDs and SDK. SIE Local is the free open-source server on NVIDIA GPU or Apple Silicon, offline by default. SIE Serverless is a usage-priced API with no GPUs to keep busy, and Native Inference (SIE Cluster) puts the deployment inside your own cloud account and region with Superlinked engineers helping evaluate and operate it. Superlinked's published numbers are the pitch: Qwen3 Reranker 4B at 55 ms median versus 179 ms for Cohere Rerank 4 Pro, Qwen3 Embedding 4B at 15.7 ms versus 238 ms for OpenAI text-embedding-3-large, and a million documents to JSON at $728 versus $7,680 on Claude Sonnet 5.5. Recent engineering posts cover serving one base model with many LoRA adapters, running topk-embed-v1 at 50k tokens/s on a single GPU, and how SIE's GPU sharing compares to HAMi, MIG and Run:ai. Under the hood it wraps SGLang, vLLM, TensorRT-LLM, TEI, llm-d, Dynamo, PyTorch and Candle, with a cluster-wide queue, worker pooling, model stacking and LRU eviction so several models share one GPU. It is a narrower bet than vLLM or llm-d when you serve one large model, and a broader one than TEI when embeddings are only part of the job.
Key features
- Encode text and images into dense, sparse, and multi-vector embeddings
- Rerank query-document pairs with cross-encoders like Qwen3 Reranker and bge-reranker
- Hybrid sparse search that combines exact terms with meaning
- ColBERT passage matching to find details in long documents
- Convert PDFs to Markdown with tables and charts intact
- Extract fields from invoices, receipts, and forms into schema-valid JSON
- OCR receipts and handwriting into text, and parse app screenshots to typed records
- Guard prompt safety and redact PII with classifier models
- Run an OpenAI-style chat API on open LLMs with structured output
- Route each request between a cheap and a strong LLM
- Extract entities and relations into a knowledge graph
- Ask questions about an image, detect objects, and classify images
- Transcribe calls and meetings to text
- Serve one base model with many LoRA adapters
- Cluster-wide queue with pool-then-batch GPU packing, worker pooling and LRU eviction
Behind the verdict
Where SIE earns its place is the boring middle of an agent pipeline. Reranking, OCR, field extraction, PDF cleanup — jobs that individually look too small to optimize and collectively show up as a four-figure line item. Superlinked's own comparison work (documents to JSON at $728 against $7,680, PDF-page search at $200 against $740) is the argument, and it holds up best at steady volume. We'd pick it when a fleet of small models is the actual workload and you want them sharing a GPU instead of each idling on its own. The pool-then-batch queue and LRU eviction matter more than any single model on the list. Serving one base model with many LoRA adapters, which the team wrote up in October, is the same story from a different angle. Same model IDs and SDK across Local, Serverless and your own cluster is the underrated part. Prototype on a laptop, move to the API when traffic shows up, then repatriate to your cloud account when compliance asks. That path is cheap to reverse. Where it bites: the serverless tier is usage-priced, so bursty, low-volume traffic still costs more than doing nothing, and latency benchmarks against Cohere and OpenAI assume the traffic is already steady. Data-residency and air-gap requirements are served by Native Inference, which means Kubernetes and GPU ops on your side — Superlinked support is optional, ownership is not. Closest alternative depends on your shape. Serving one big LLM? vLLM or llm-d is the simpler path and SIE adds nothing. Only need text embeddings? A narrower server like TEI is enough. SIE's niche is the multi-model, multi-task middle, and it competes there against stitching several tools together rather than against any single rival.
Models under the hood
as of 2026-11-08
Limitations
- SIE is positioned for small, specialized open models on everyday agent tasks rather than frontier LLMs; Superlinked's own posts concede the boundaries (use OpenAI for frontier models, Modal for bursty compute, keep FastEmbed in-process — SIE wins on sustained inference).
- Deployment is either SIE Serverless (hosted, pay per call) or your own Kubernetes cluster (GCP, AWS, Azure, or air-gapped), where capacity planning, multi-GPU serving, and upgrades are your responsibility.
- The full self-hosted engine is Apache 2.0 and free; hosted usage is billed per call.
as of 2026-10-10
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Is Sie still active?
Sie is live · free plan; usage-based paid tier · last verified 14 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is Sie? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in Sie
Checked 6 days agoAcross the latest 10 updates: 1 launch and 9 community discussions.
Should Production Agents Use Non-Transformer Models?
Superlinked examines whether production agents should run on non-transformer architectures instead of transformer-based LLMs.
Improve the Agent System Before You Fine-Tuning the Model
Superlinked argues for fixing agent system design before spending on model fine-tuning.
Serving One Base Model With Many LoRA Adapters
Superlinked walks through serving a single base model with multiple LoRA adapters on SIE.
Should You Use an LLM for Every Agent Decision?
Superlinked questions routing every agent decision through an LLM.
Can a Small Language Model Read an Entire PDF?
Superlinked tests small language models on full-PDF document processing.
QLoRA Without Guesswork: Start With Evals, Then Scale the Data
Superlinked outlines a QLoRA workflow that starts with evals before scaling training data.
Most AI tasks no longer need a frontier model
Superlinked launch post: SIE runs open models in production at near-frontier quality, as a hosted API or in the customer's own cloud.
Sharing GPUs between models: SIE vs HAMi, MIG and Run:ai
Superlinked compares SIE's GPU sharing against HAMi, MIG and Run:ai for multi-model serving.
Small Models Decide What Reaches Your Search LLM
Superlinked describes using small models to gate which content reaches a search LLM.
Run topk-embed-v1 at 50k tokens/s on a single GPU
Superlinked reports running topk-embed-v1 at 50k tokens per second on a single GPU.
What do users say about Sie?
What people actually say about Sie — is it worth it?
We scanned public community sources for Sie on Sep 30, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 1 of the posts we fetched could be positively tied to Sie. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Sie — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 14 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Sie 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed our verdict, who it suits, description, summary, features, pricing tiers, who should skip it
- — changed pricing page
- — changed our verdict, summary, features, pricing tiers, description
- — changed pricing page, limitations, pricing tiers, who it suits, summary, description, our verdict, features, who should skip it
- — changed pricing page, platforms
- — changed pricing tiers, description, summary, integrations, our verdict, features, who should skip it, who it suits
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Sie cost in 2026?
Sie: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 14 Nov 2026.
We read Sie’s pricing page every week; last read . No proven price change since we began watching it on 14 Nov 2026. How this is checked
Plans compared
Every published Sie tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| SIE Local | Free | — | — |
| SIE Serverless | Usage-based | usage | — |
| Native Inference (SIE Cluster) | Custom | quote | — |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Sie's pricing actually pencils out — and where peers do it cheaper.
Self-hosted SIE is Apache 2.0 at $0 — the real cost is the GPUs and the Kubernetes labor you already own. Cheaper than per-token hosted inference once embeddings, reranking, and OCR run at steady volume (Superlinked's benchmark claims 50x on gte-multilingual versus text-embedding-3 all-in on AWS EKS with L4s), but more expensive than Modal or hosted APIs for bursty, low-volume work. Managed SIE and the Agent plugin are listed as upcoming.
Has Sie's pricing changed?
No plan price for Sie has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Sie?
Pick Sie if you are Search and RAG teams paying per-token for embeddings and reranking at steady volume, Agent builders running a fleet of small open models that need to share GPUs, Document pipelines needing OCR, extraction, and PDF-to-markdown behind one API, Teams with data-residency or air-gap rules that rule out hosted inference APIs.
Look elsewhere if you are Teams with no Kubernetes or GPU-operations experience and no plan to hire it, Anyone serving a single large LLM, where vLLM or llm-d is the simpler path, Bursty, low-volume workloads where usage-based pricing stays cheaper, Projects that only need text embeddings, where a narrower server like TEI suffices.
Skip it if Skip SIE if nobody on your team wants to run Kubernetes or manage GPU capacity, or if your inference volume is bursty and low enough that hosted per-token pricing stays cheaper than a cluster you operate yourself.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Sie actually fits — and what changes day-one when you adopt it.
Deploy SIE with the Helm chart, point the client at the OpenAI v1-compatible endpoint by swapping the base URL, and move embeddings and reranking off per-token APIs while keeping Chroma or Qdrant as the vector store.
Outcome: Embedding and rerank inference runs on your own GPUs with no per-token cost, and the retrieval stack keeps its existing vector database and SDK code.
Define model profiles for qwen3.6-27b, bge-reranker-v2-m3, and an OCR model, let worker pooling and LRU eviction share the GPUs, and add a granite-guardian-2b input guardrail on the agent entry point.
Outcome: Several models share the same GPUs instead of each holding a dedicated deployment, and profile changes hot-reload without a cluster restart.
Install SIE air-gapped from mirrored model snapshots on EKS, GKE, or AKS via Terraform, then chain OCR to markdown, entity extraction to schema-valid JSON, and summarization on a self-hosted LLM.
Outcome: PDFs, Office files, and scans are parsed and extracted entirely inside your cloud, with KEDA scaling worker pools from zero when the nightly pipeline is idle.
Use cases
- Self-host product search with vector embeddings in about five minutes
- Build a multimodal wine recommender using OCR and embeddings
- Create a private fine-tuned compliance RAG pipeline that never leaves your cloud
- Benchmark eight retrieval strategies on your own corpus with measured NDCG@10
- Build a multimodal product classifier with embeddings
- Swap an OCR model with a one-identifier config change
- Change NER labels at request time without retraining the model
- Add a fraud-risk gate to a Stripe Link checkout using an SIE model
Setup time & first value
How long it actually takes to get something useful out of Sie — broken out by persona, not the marketing-page minute.
Self-hosted on a laptop via Docker: first vectors in about two minutes per the quickstart. On Kubernetes: Helm chart plus Terraform for EKS, GKE, or AKS — plan on a few hours for a working cluster and longer if you are going air-gapped from mirrored snapshots. The OpenAI v1-compatible endpoint makes client-side migration a one-line base-URL change, so application code is the fast part;
Switching to or from Sie
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From OpenAI embeddings/rerank: swap the base URL to the SIE endpoint — the docs ship an OpenAI → SIE migration guide.
- →From Cohere rerank-3.5: move to bge-m3 or bge-reranker-v2-m3 on SIE; a Cohere → SIE guide is published.
- →From HuggingFace TEI: TEI covers encoding only, so SIE adds Score, Extract, and Generate in the same cluster; a TEI → SIE guide exists.
- →From Infinity or FastEmbed: the docs publish Infinity → SIE and FastEmbed → SIE migration guides — Superlinked suggests keeping FastEmbed in-process and moving shared production inference to SIE.
- →From Modal: a Modal → SIE guide is published, positioned for sustained rather than bursty inference.
- ↗To a hosted embedding API (OpenAI, Cohere): revert the base URL — SIE's OpenAI v1-compatible endpoint means the client code path is already the same.
- ↗To vLLM or llm-d: if you consolidate onto a single large LLM, those are the simpler serving paths and Superlinked's own comparison concedes the point.
- ↗To HuggingFace TEI: sufficient if you only ever need text encoding, since TEI covers encoding alone.
- ↗To Modal: for bursty compute, Superlinked's own SIE vs Modal post says Modal wins there.
- ↗To FastEmbed: for in-process embedding inside a single application, Superlinked recommends keeping FastEmbed rather than standing up a cluster.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Sie”, and we withheld 6: 6 could not be judged, because “Sie” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Sie.
Official links
What are the best Sie alternatives?
The closest alternatives to Sie we list are MindsDB, Budibase and Workato. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| MindsDB | Freemium | Pricing not published | — |
| Budibase | Freemium | Pricing not published | — |
| Workato | Contact Sales | Custom pricing | — |
Alternatives to Sie
View allTools that pair well with Sie
Common stack mates teams adopt alongside Sie, with the specific reason each pairing earns its keep.
MindsDB
One API key and one prepaid bill for Claude, GPT, Gemini, Kimi, GLM and open models, plus an agent workspace with per-team spend caps.
Budibase
Open-source platform for building AI agents, apps and automations on your own data and infrastructure
Workato
Enterprise iPaaS for agent orchestration: govern MCP servers, AI models and workflows on one runtime
Sie vs its closest rival
Sie's closest head-to-head is with Spider Cloud — These aren't substitutes, so 'which one wins' is the wrong question — the honest answer is that most teams evaluating them are solving two different problems. If your bottleneck is getting live, rendered pages, SERP results, and whole-site crawls into an agent or RAG pipeline without running browsers and proxy pools yourself, Spider Cloud is the buy. If your bottleneck is paying per-token for embeddings, reranking, OCR, and extraction at steady volume — or you have data-residency rules that forbid hosted APIs — Sie is the self-hosted answer, provided you already run Kubernetes and GPUs. Teams with both problems run both; Sie handles the private small-model inference layer and Spider Cloud feeds it rendered web content. Only pick one if you genuinely have only one of those two problems. Verdict generated
Head-to-head comparisons
Sie vs Spider Cloud
These aren't substitutes, so 'which one wins' is the wrong question — the honest answer is that most teams evaluating them are solving two different problems. If your bottleneck is getting live, rendered pages, SERP results, and whole-site crawls into an agent or RAG pipeline without running browsers and proxy pools yourself, Spider Cloud is the buy. If your bottleneck is paying per-token for embeddings, reranking, OCR, and extraction at steady volume — or you have data-residency rules that forbid hosted APIs — Sie is the self-hosted answer, provided you already run Kubernetes and GPUs. Teams with both problems run both; Sie handles the private small-model inference layer and Spider Cloud feeds it rendered web content. Only pick one if you genuinely have only one of those two problems.
GeneratedSie vs Genspark
These two are not competitors and no buyer should be choosing between them: Genspark is a hosted workspace a person uses to search, write and automate, and SIE is infrastructure an engineering team runs to serve embeddings, rerankers and OCR on its own GPUs. Pick Genspark if you want cited research summaries, decks, sheets, podcasts and no-code agents in one login. Pick SIE only if you already run Kubernetes on EKS, GKE or AKS, pay per-token for embedding/reranking at steady volume, or have air-gap and data-residency rules — and note that frontier-model reasoning is explicitly not what SIE is for. If you're a solo user or an office team, SIE is the wrong shape of product entirely.
GeneratedSie vs Temporal Ai
These are not alternatives, so the only real question is whether you need one, the other, or both. If your problem is executions that must survive worker crashes, retries, and sessions abandoned mid-flight, pick Temporal — its durable state, replay, and compensating-transaction Saga pattern address exactly that failure class, and Temporal Cloud on Azure plus Serverless Workers for Lambda and Cloud Run are new delivery options. If your problem is inference cost and data residency for embeddings, rerankers, OCR, and extraction, pick Sie, provided you already run Kubernetes and GPUs. A RAG or agent team at scale will plausibly run Sie for the model tier and Temporal for the orchestration tier — they sit at different layers of the same stack, not in the same slot.
GeneratedSie vs Presto Voice
These are not competitors, and you should not shortlist them against each other. Presto Voice is an operational service sold to QSR franchise groups who want drive-thru orders taken and upsold at the speaker post without adding headset labor — no published price, a managed deployment, and Toast POS as the shortest integration path. Sie is Apache-2.0 infrastructure for engineers who already run Kubernetes and GPUs and need embeddings, rerankers, OCR, extraction, and small generation models on their own metal. One buys you a managed restaurant outcome; the other is a self-hosted inference stack. If you sell drive-thru orders, call Presto; if you serve small open models, deploy Sie.
GeneratedSie vs Dbos
These aren't competitors, so there's no head-to-head pick — but there is a sequencing answer. If your pain is agents that lose work on crashes, redeploys or week-long waits, DBOS is the cheap, fast fix and it only asks for a Postgres you already run. If your pain is per-token bills on embeddings, rerankers and OCR at steady volume, or data that legally can't leave your cloud, Sie is the right tool and you should budget Kubernetes and GPU expertise alongside it. Teams deep in agent infrastructure often end up running both: DBOS for orchestration durability, Sie for the model layer.
GeneratedSie vs Air Ai
These two products do not compete, so there is no real pick between them. Air is a defense readiness platform — you buy it if you are a military command or program office trying to compress materiel release, part identification, and vendor due diligence, and you accept a vendor-led integration cycle with contact-only pricing. SIE is infrastructure software for engineers who want embeddings, rerankers, OCR, and extraction running on their own Kubernetes GPUs instead of paying per token to a hosted API. If you have a defense readiness problem, Air is the only relevant option here; if you have an inference cost or data-residency problem, SIE is the only relevant option. A buyer choosing one is not choosing against the other.
GeneratedSie vs Cryptohopper
These aren't competitors, so there's no either/or to settle: Cryptohopper is for someone who wants a crypto bot trading on Binance or Kraken for $24.16/mo after a 3-day trial, and SIE is for a platform team with Kubernetes and GPUs who wants to stop paying per token for embeddings, rerankers, and OCR. If you're trading crypto, buy Cryptohopper. If you're running a retrieval or document pipeline at sustained volume, deploy SIE on EKS/GKE/AKS or sit on the Managed SIE waitlist. The only overlap is philosophical — both touch "AI agents" — and even that is different: Cryptohopper feeds live market data to agents via MCP, while SIE is the inference layer agents call for embeddings and extraction.
GeneratedSie vs Notable
These two products do not compete — a real buyer would never shortlist both. Notable is a healthcare-only enterprise platform: if you run a health system drowning in prior authorizations, denials, and patient call volume, it's an operational partner you evaluate against other healthcare automation vendors, not against infrastructure. Sie is for engineering teams who want embeddings, rerankers, OCR, and extraction on shared GPUs inside their own cluster instead of paying a hosted API per token — Apache 2.0, freemium, Kubernetes-native. Pick Notable if your problem is hospital administrative labor; pick Sie if your problem is inference cost and data residency for model serving. There is no overlap in buyer, budget, or skill set.
GeneratedFrequently asked questions
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