What Tools Are Used in LLMOps?

LLMOps tools help manage the full lifecycle of large language models, from development to deployment and monitoring. These tools support building applications, tracking model performance, and maintaining reliable AI systems. 

Common examples include frameworks such as LangChain and LlamaIndex for building LLM applications, monitoring platforms like LangSmith and Arize Phoenix for tracking performance, model registries such as MLflow or Weights & Biases, and vector databases like Pinecone that support retrieval augmented generation systems. 

In this blog you will learn what tools are used in LLMOps, how they support large language model systems, and the categories of platforms used to build, deploy, and maintain generative AI applications.  

Prompt Engineering and LLM Application Frameworks 

When exploring what tools are used in LLMOps, prompt engineering frameworks are among the most important components. These tools help developers build applications powered by large language models and manage prompt workflows efficiently. 

Popular LLM application frameworks include: 

LangChain 

LangChain is widely used for building LLM powered applications. It allows developers to connect language models with APIs, tools, and external data sources. 

Key uses include: 

• building chatbot applications 
• integrating APIs with LLM systems 
• managing prompt chains and workflows 

Also Read: What is LangChain Used For? 

LlamaIndex 

LlamaIndex helps connect large language models with external data sources such as documents or databases. It is commonly used in retrieval based AI systems. 

Key uses include: 

• connecting LLMs with knowledge bases 
• building retrieval augmented generation pipelines 
• organizing large document collections for AI queries 

Also Read: Top 10 Agentic AI Frameworks to Build Intelligent AI Agents in 2026 

Semantic Kernel 

Semantic Kernel is a framework used to integrate LLM capabilities into software applications. It allows developers to add AI features directly into services and workflows. 

Key uses include: 

• building AI powered applications 
• integrating prompts with existing systems 
• managing AI workflows within software products 

These frameworks simplify development and integration, which is why they are frequently mentioned when discussing what tools are used in LLMOps. 

Common LLM Frameworks Used in LLMOps 

Tool	Main Purpose	Typical Use
LangChain	Build LLM applications	Chatbots, AI agents, workflow orchestration
LlamaIndex	Connect LLMs with external data	Retrieval systems and knowledge search
Semantic Kernel	Integrate AI capabilities into software	AI enabled applications and services

These frameworks form the foundation of many LLM applications and play a major role in answering what tools are used in LLMOps for prompt management and application development. 

Also Read: Free AI Tools You Can Use for Writing, Design, Coding & More 

Vector Databases Used in LLMOps 

When exploring what tools are used in LLMOps, vector databases play a critical role in building retrieval based AI systems. Large language models often need external knowledge sources to generate accurate responses. Vector databases make this possible. 

Common vector databases include: 

Pinecone 

Pinecone is a managed vector database designed for fast and scalable embedding search. It is commonly used in production level RAG systems. 

Typical use cases include: 

• building knowledge search systems 
• powering AI assistants 
• enabling document retrieval for LLM responses 

Also Read: Is FAISS Vector Database? 

Weaviate 

Weaviate supports semantic search and vector indexing. It allows developers to build applications that search meaning instead of exact keywords. 

Typical use cases include: 

• semantic document search 
• knowledge base retrieval 
• AI powered content discovery 

Milvus 

Milvus is an open source vector database built for large scale similarity search. It is often used in applications that require handling millions of embeddings. 

Typical use cases include: 

• large scale embedding retrieval 
• AI search platforms 
• recommendation systems 

These platforms allow LLM systems to retrieve relevant information from documents, datasets, or knowledge bases before generating answers. 

Also Read: What Is FAISS and How Does It Work? 

Common Vector Databases Used in LLMOps 

Vector Database	Main Purpose	Typical Use
Pinecone	Managed vector search	Retrieval augmented generation systems
Weaviate	Semantic search and indexing	Knowledge search applications
Milvus	Large scale embedding retrieval	AI search and recommendation systems

Vector databases support retrieval pipelines and knowledge integration. This is why they are frequently mentioned when discussing what tools are used in LLMOps. 

Also Read: Which Is Better FAISS Or Chroma for Your Project? 

Monitoring and Evaluation Tools in LLMOps  

Monitoring LLM outputs is essential because generative AI systems can produce incorrect or unsafe responses.  

LLMOps tools track model performance and response quality.  

Popular monitoring tools include:  

• LangSmith: Used to track prompt experiments and application performance.  
• Helicone: Observability platform for LLM applications.  
• Arize AI: Provides monitoring for model performance and AI system behavior.  

These tools help teams detect issues such as hallucinations, latency problems, and response quality issues.  

Also Read: Difference Between LangGraph and LangChain 

Tool	Role
LangSmith	Prompt debugging and experiment tracking
Helicone	Observability for LLM apps
Arize AI	Monitoring AI system performance

These platforms help organizations answer what tools are used in LLMOps when running generative AI applications at scale.  

Deployment and Infrastructure Tools for LLMOps  

Running LLM systems also requires infrastructure tools that manage APIs and deployment pipelines.  

Common deployment tools include:  

• Docker for containerized environments  
• Kubernetes for scalable model deployment  
• API gateways to connect applications with language models  

These tools ensure that LLM applications can run reliably in production environments.  

In real AI platforms, infrastructure tools often work alongside LLM specific frameworks.  

Conclusion  

Understanding what tools are used in LLMOps helps explain how modern generative AI systems are built and managed. LLMOps platforms support prompt management, vector retrieval, monitoring, and deployment. By combining frameworks, vector databases, and monitoring tools, teams can deploy large language models while maintaining reliability and performance.  

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