Upstage

Developer Tools , Data Extraction , AI Assistant

Upstage provides a specialized stack for Document AI and RAG, combining high-performance small language models with superior layout analysis for complex enterprise data.

Excellent for developers building RAG systems from messy PDFs and tables, weaker for general-purpose creative writing or massive ecosystem support.

Llm Ocr Rag Enterprise Api Document Analysis Machine Learning Nlp

Analysis based on product data, pricing structure, traffic signals, and public user sentiment.

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Who Should Use Upstage?

Typical users

ML engineers and enterprise developers building internal knowledge bases or automated document processing workflows.

Maturity fit

scaling to advanced

Choose this if…

You need to extract structured data from complex PDFs and tables for RAG
Your priority is a high-performance model with low latency and a small footprint
You require private cloud or on-premise deployment for data sovereignty

Skip this if…

You need a consumer-ready chatbot interface rather than an API
Your workflow relies on a massive library of third-party community plugins
You only process simple, plain-text documents where basic OCR suffices

About Upstage

Upstage is a South Korean AI company that develops the Solar LLM family and specialized Document AI tools. It focuses on the 'Document-to-LLM' pipeline, providing the infrastructure to turn unstructured enterprise files into machine-readable context for RAG applications.

Official profiles

X (Twitter) LinkedIn

What it actually does

It provides a suite of APIs for LLM inference, advanced OCR, and document layout analysis. The platform identifies headers, tables, and figures in documents to ensure that when data is fed into an LLM, the logical structure and context remain intact.

What makes it different

Unlike general LLM providers, Upstage focuses heavily on the 'pre-processing' stage of AI. Their Layout Analysis API is specifically tuned to recognize table structures and reading orders that standard OCR often breaks, making their RAG results more accurate for financial or technical documents.

Solar LLM inference (Mini and Pro versions) Document Layout Analysis for RAG High-accuracy OCR for multiple languages Groundedness Check for hallucination reduction Private LLM deployment (On-prem/Private Cloud) Fine-tuning for domain-specific data

Key Features

Solar-mini

A 10.7B parameter model that outperforms much larger models on benchmarks while maintaining low latency.

Layout Analysis API

Identifies document elements like tables, figures, and headers to preserve context for LLMs.

Groundedness Check

A specialized API that verifies if an LLM's response is actually supported by the provided context.

Table Extraction

Converts complex PDF tables into structured Markdown or JSON without losing cell relationships.

Multi-language Support

Optimized performance for English and Korean, with expanding support for other languages.

Custom Model Tuning

Allows enterprises to train Solar on proprietary data for specialized industry terminology.

Pricing

Free Trial

Free

Free credits for API testing (typically $10-$30)
Access to Solar-mini and Solar-pro
Access to Layout Analysis API
Standard rate limits

Popular

Pay-as-you-go

Usage-based monthly

Solar-mini: ~$0.15 per 1M tokens
Layout Analysis: ~$0.10 per page
Groundedness Check: ~$0.007 per request
No upfront commitment

Enterprise

Custom annual

On-premise deployment
Private cloud hosting
Custom fine-tuning services
Dedicated support and higher rate limits

Pricing checked 4 months ago

Pricing guidance

Best plan for most users: The Pay-as-you-go plan is best for most developers as it allows for scaling based on actual document volume without heavy upfront costs.

Free plan enough? No — the free credits are strictly for initial prototyping and will be exhausted quickly during a proper POC.

Upgrade when:

When document volume exceeds 10,000 pages per month
When data security policies mandate on-premise hosting
When you need custom fine-tuning on proprietary datasets

Watch out for:

Rate limits on the standard API can be restrictive for bulk historical data processing
Layout Analysis is billed per page, which can scale quickly for large PDF archives

Competitive pricing for LLM tokens, but premium positioning for specialized Document AI and layout analysis features.

Pros & Cons

Strengths

Superior RAG-ready document parsing
The Layout Analysis tool is significantly better at handling multi-column layouts and nested tables than standard open-source OCR libraries.
High performance-to-cost ratio
Solar-mini provides GPT-3.5 or better performance at a fraction of the size, leading to faster inference and lower compute costs.
Enterprise-grade privacy options
Offers genuine on-premise and private cloud deployment options for industries with strict data residency requirements.

Weaknesses

Smaller developer ecosystem
Compared to OpenAI or Anthropic, there are fewer community tutorials, pre-built integrations, and third-party wrappers.

Affects: Solo developers and small startups
Niche focus on Document AI
While excellent for documents, it lacks the broad multimodal capabilities (like advanced image generation or complex audio) of larger frontier models.

Affects: Teams building general-purpose multimodal apps
Documentation gaps
Some advanced features and edge-case behaviors are less documented than those of more established Western AI labs.

Affects: Developers requiring deep technical troubleshooting

Real User Sentiment

Generally positive, with users highlighting the technical efficiency of the Solar model and the accuracy of the layout engine.

Users tend to like

Accuracy of table extraction from PDFs
Speed and low latency of Solar-mini
Ease of integrating the Groundedness Check into RAG pipelines
Performance on the Hugging Face leaderboard

Users commonly complain about

API documentation can be sparse for complex use cases
Limited availability of SDKs in languages other than Python
Higher cost per page for layout analysis compared to basic OCR

Recurring tradeoffs

Users trade a broad ecosystem for specialized document processing accuracy.

Happiest users

Engineers building RAG systems for legal, financial, or technical departments where document structure is critical.

Often frustrated

Developers looking for a 'one-stop-shop' with the same level of third-party integration as OpenAI.

Use Cases

Financial Report Analysis

Extracting structured data from complex quarterly earnings PDFs for investment research.

Legal Document Review

Using Layout Analysis to maintain clause hierarchy and table references in contracts.

Customer Support Automation

Powering a RAG-based bot that answers questions from technical product manuals.

Data Privacy Compliance

Deploying a private LLM instance to process sensitive PII without sending data to public clouds.

Academic Research

Summarizing and querying large batches of scientific papers while preserving figure and table context.

Frequently Asked Questions

How does Solar-mini compare to GPT-3.5 or GPT-4?

Solar-mini is a 10.7B parameter model designed to match or exceed GPT-3.5 performance in a much smaller footprint. While it may not match GPT-4's complex reasoning, it offers significantly lower latency and cost for specialized tasks like RAG and summarization.

What is the pricing for the Layout Analysis API?

The Layout Analysis API is typically billed at approximately $0.10 per page. This is separate from the token costs associated with the Solar LLM inference.

Does Upstage offer a free plan?

Upstage provides a trial period with free credits (usually around $10-$30) upon signing up for the Upstage Console, but there is no permanent free tier for high-volume use.

Can I deploy Upstage models on my own servers?

Yes, one of Upstage's primary enterprise selling points is the ability to deploy Solar models on-premise or within a private cloud environment for maximum data security.

What languages does Upstage support?

Upstage is highly optimized for English and Korean. It supports many other languages for OCR and general LLM tasks, though performance may vary for less common dialects.

How does the Groundedness Check work?

The Groundedness Check API takes a source document and an LLM-generated response as input, then returns a score indicating whether the response is factually supported by the source, helping to prevent hallucinations in RAG systems.

Why trust this page?

This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.

Funding & Company

Founded

2020

Stage

Series c

Total Raised

$269.6M

Latest Round

Series C (Apr 2026)

Notable Investors

SK Networks KT Korea Development Bank SoftBank Ventures Asia Amazon AMD Saje Partners Hyundai Motor & Kia

Upstage has raised approximately $270 million, culminating in a $126 million Series C in April 2026 that established it as a unicorn. [9, 21] This significant and recent funding from major corporate and financial investors provides a strong capital base for product development and global expansion.

Full funding report high confidence

Market Signals & Traffic

Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.

Estimated visits: 101,042
Global rank: #310,291
Snapshot: Apr 2026
Traffic trend: Rising