# Lamini

2026-11-11에 검증된 OpenAITools.app 텔레메트리에 따르면, Lamini(lamini.ai)은(는) Large Language Models (LLMs)에 색인된 AI 플랫폼으로 월 4.16천회 방문을 기록 중이며, 전월 대비 트래픽 변화량은 +12(+0.3%)입니다. 주요 지리적 시장은 미국(트래픽 점유율 92.9%)이며, 무료+유료, 가격 문의 가격 모델로 운영됩니다. 월간 트래픽 기준 주요 경쟁 대안으로는 Google Gemma 오픈 모델 (월 490.09만회 방문), Aleph Alpha (월 17.51만회 방문), Scale (월 61.69만회 방문) 등이 있습니다.

Lamini is an enterprise LLM platform for building, fine-tuning, controlling and deploying custom large language models. It targets teams that need their own accurate, private models rather than a generic API: you bring your data, fine-tune open models on the platform, and serve them with tuned runtimes designed to reduce hallucinations. It covers use cases like text-to-SQL, classification, RAG and function calling, and is known for running workloads on AMD GPUs.

## 주요 텔레메트리 및 사양

| 지표 | 값 |
| :--- | :--- |
| 도구 이름 | Lamini |
| 핸들 | lamini |
| Status | active |
| Origin | independent |
| Maintenance | active |
| 공식 도메인 | lamini.ai |
| 웹사이트 URL | https://lamini.ai |
| 카테고리 | Large Language Models (LLMs), AI 개발자 도구, AI Agent, AI 텍스트 분류기 |
| 가격 모델 | 무료+유료, 가격 문의 |
| 월간 방문 수 | 4,158 |
| 전월 대비 트래픽 변화 | 12 |
| 전월 대비 성장률 | +0.3% |
| 주요 시장 | 미국 |
| 시장 점유율 | 92.9% |
| 직접 방문 | 1,637 |
| 자연 검색 방문 | — |
| 소셜 방문 | — |
| 추천 방문 | 425 |
| 이메일 방문 | 148 |
| 리뷰 점수 | 4.3 |
| 저장 수 | 1 |
| 지원 플랫폼 | 웹사이트 |
| 결제 플랫폼 |  |
| 속성 | 기능: 웹사이트; 가격: 무료+유료; 가격: 가격 문의 |
| 태그 | LLM 플랫폼, 파인튜닝, 환각 감소, RAG(검색 증강 생성), 텍스트-SQL 변환, 분류, 함수 호출, 안전한 LLM 배포, AMD GPU, AI 에이전트 |

## 개요

Lamini is an enterprise LLM platform for building, fine-tuning, controlling and deploying custom large language models. It targets teams that need their own accurate, private models rather than a generic API: you bring your data, fine-tune open models on the platform, and serve them with tuned runtimes designed to reduce hallucinations. It covers use cases like text-to-SQL, classification, RAG and function calling, and is known for running workloads on AMD GPUs.

## 가격 및 요금제

Freemium entry point with enterprise pricing available on request. Pricing is sales-led for scaled deployments; check the official Lamini site for the current free tier, plan details and enterprise terms.

## 주요 기능 및 역량

- **Custom model fine-tuning:** Train and fine-tune open large language models on your own domain data so output matches your vocabulary, formats and policies.
- **Hallucination reduction:** Tuned inference runtimes and grounding techniques aimed at cutting hallucinations and raising accuracy on domain-specific tasks.
- **RAG, text-to-SQL and classification:** Supports common enterprise patterns — retrieval-augmented generation, text-to-SQL, text classification and function calling.
- **AMD GPU acceleration:** Run training and inference workloads on AMD GPUs as an alternative GPU supply path for LLM workloads.
- **Secure, controlled deployment:** Develop and serve models in an enterprise-oriented environment that keeps data and model control with your team.

## 주요 활용 사례

- **Private domain LLM (Enterprise AI team):** Fine-tune an open LLM on internal documents and serve it privately so answers stay accurate and data never leaves your control.
- **RAG and text-to-SQL (Data platform engineer):** Run retrieval-augmented generation and text-to-SQL over your own databases to power internal analytics assistants with fewer hallucinations.
- **Classification and tool calling (ML engineer):** Train classification and function-calling models on labelled business data for routing, tagging and automation tasks.
- **GPU supply flexibility (Infrastructure lead):** Run fine-tuning and inference on AMD GPUs to diversify hardware supply and control infrastructure costs.

## 강점

- Purpose-built for custom, high-accuracy LLMs rather than one-size-fits-all APIs
- Explicit focus on reducing hallucinations and improving domain accuracy
- Supports the enterprise staples: RAG, text-to-SQL, classification and function calling
- AMD GPU workloads offer an alternative to single-vendor GPU supply
- Emphasis on private, controlled deployment suits data-sensitive organisations

## 한계

- The marketed endpoints can be expensive at scale and pricing is sales-led, so you need a conversation to budget enterprise use
- Deployment and fine-tuning workflows assume ML engineering skills, so it is not a no-code tool
- You are tied into its ecosystem and runtimes for training and serving
- Small teams without an existing data pipeline will find setup and data preparation heavy
- Feature specifics and current documentation should be verified directly on the site, since third-party descriptions go stale quickly

## 자주 묻는 질문

### What is Lamini?
Lamini is an enterprise platform for developing and controlling custom large language models. Teams use it to fine-tune and deploy their own accurate, private LLMs rather than relying only on a general-purpose hosted model.

### Who is Lamini for?
It is aimed at enterprises and engineering teams that need models trained on their own data with higher accuracy and control, including use cases such as text-to-SQL, classification, RAG pipelines and function calling.

### How does it reduce hallucinations?
Lamini combines fine-tuning on your own data with tuned inference runtimes. The core claim is that grounding the model in your data and running it efficiently cuts down hallucinations and improves accuracy on domain tasks.

### What hardware does it use?
Lamini is known for running LLM training and inference workloads on AMD GPUs, which is relevant if you want an alternative to GPU supply from a single vendor.

### How much does it cost?
Lamini markets itself with a freemium entry point and enterprise pricing on request. The public site is the authoritative source for current plans and costs, so check there for the latest figures.

### Can I deploy models privately?
Yes — the platform is designed so that enterprises can deploy and control their own models rather than sending all inference to a third-party API, which matters for data privacy and governance.

### Lamini의 월간 트래픽과 성장 추세는 어떠한가요?
Lamini은(는) 현재 월 4.16천회 방문을 기록 중이며, 전월 대비 트래픽 변화량은 +12(성장률 +0.3%)입니다. 주요 유입 시장은 미국(점유율 92.9%)이며, 여기에는 월 직접 방문 1.64천회와 월 자연 검색 방문 —회가 포함됩니다.

### Lamini은(는) 무료인가요? 가격 모델은 무엇인가요?
Lamini은(는) 무료+유료, 가격 문의 가격 모델로 운영됩니다. Freemium entry point with enterprise pricing available on request. Pricing is sales-led for scaled deployments; check the official Lamini site for the current free tier, plan details and enterprise terms. 지원되는 결제 및 체크아웃 플랫폼에는 등록되지 않음 등이 있습니다.

### 2026년 최고의 Lamini 대안은 무엇인가요?
OpenAITools.app의 카테고리 트래픽과 기능 적합도를 기준으로 할 때 Lamini의 상위 5개 대안은 다음과 같습니다: 1. Google Gemma 오픈 모델 (월 490.09만회 방문); 2. Aleph Alpha (월 17.51만회 방문); 3. Scale (월 61.69만회 방문); 4. Together AI (월 164.59만회 방문); 5. Abliteration.ai (월 52.28만회 방문).

### Lamini은(는) 어떤 플랫폼과 핵심 AI 역량을 지원하나요?
Lamini은(는) Large Language Models (LLMs), AI 개발자 도구, AI Agent, AI 텍스트 분류기으로 분류되며 다음 플랫폼을 지원합니다: 웹사이트. 핵심 역량 태그에는 LLM 플랫폼, 파인튜닝, 환각 감소, RAG(검색 증강 생성), 텍스트-SQL 변환, 분류 등이 있습니다.

## 대안

| 도구 이름 | 월간 방문 수 | 가격 모델 | 비교 |
| :--- | :--- | :--- | :--- |
| [Google Gemma 오픈 모델](https://openaitools.app/ko/tool/gemma-open-models-by-google) | 4,900,914 | 무료+유료, 유료 | [비교](https://openaitools.app/ko/compare/gemma-open-models-by-google-vs-lamini) |
| [Aleph Alpha](https://openaitools.app/ko/tool/aleph-alpha-com) | 175,069 | 가격 문의 | [비교](https://openaitools.app/ko/compare/aleph-alpha-com-vs-lamini) |
| [Scale](https://openaitools.app/ko/tool/scale) | 616,915 | 가격 문의 | [비교](https://openaitools.app/ko/compare/lamini-vs-scale) |
| [Together AI](https://openaitools.app/ko/tool/together-ai) | 1,645,930 | 무료+유료, 유료 | [비교](https://openaitools.app/ko/compare/lamini-vs-together-ai) |
| [Abliteration.ai](https://openaitools.app/ko/tool/abliteration-ai) | 522,766 | 무료+유료, 유료, 가격 문의 | [비교](https://openaitools.app/ko/compare/abliteration-ai-vs-lamini) |
| [Portkey](https://openaitools.app/ko/tool/portkey-ai) | 195,650 | 무료+유료, 유료, 가격 문의 | [비교](https://openaitools.app/ko/compare/lamini-vs-portkey-ai) |
| [RightNow Inference(RunInfra)](https://openaitools.app/ko/tool/runinfra) | 18,110 | 무료 체험, 유료, 가격 문의 | [비교](https://openaitools.app/ko/compare/lamini-vs-runinfra) |
| [OrcaRouter](https://openaitools.app/ko/tool/orcarouter) | 975,300 | 무료+유료 | [비교](https://openaitools.app/ko/compare/lamini-vs-orcarouter) |
| [Prem](https://openaitools.app/ko/tool/prem) | 45,892 | 가격 문의 | [비교](https://openaitools.app/ko/compare/lamini-vs-prem) |
| [BLACKBOX.AI](https://openaitools.app/ko/tool/blackbox-ai-1) | 622,975 | 무료 체험, 유료 | [비교](https://openaitools.app/ko/compare/blackbox-ai-1-vs-lamini) |
| [LangWatch](https://openaitools.app/ko/tool/langwatch) | 52,098 | 가격 문의 | [비교](https://openaitools.app/ko/compare/lamini-vs-langwatch) |
| [Vapi](https://openaitools.app/ko/tool/vapi) | 662,301 | 무료+유료 | [비교](https://openaitools.app/ko/compare/lamini-vs-vapi) |

---
출처: https://openaitools.app/ko/tool/lamini