15 Best LLM Tools for AI Product Development [2025 Guide]

LLM tools have emerged as essential enablers for teams looking to develop, deploy, and optimize large language models (LLMs). These tools are the backbone of AI applications, providing the functionality needed to fine-tune, scale, and manage complex models that power everything from chatbots to sophisticated enterprise solutions.

The significance of LLM tools in AI development is undeniable, especially as the demand for advanced LLMOps practices grows. As businesses increasingly rely on large language models to automate tasks, enhance collaboration, and improve decision-making, having the right tools to support deployment, debugging, and real-time performance optimization becomes crucial.

For AI teams, working with LLMs presents a unique set of challenges, including managing vast amounts of data, ensuring the scalability of systems, and fine-tuning models to meet specific business needs. Effective LLM tools address these challenges by streamlining data management, enhancing prompt engineering, and providing solutions for seamless deployment.

In this article, we explore the essential role of LLM tools and how the right platform, such as Orq.ai, can transform AI projects by offering end-to-end solutions for building, operating, and optimizing LLMs.

What Are LLM Tools?

Now that we’ve introduced the concept of LLM tools, let’s dive deeper into their core functionalities and explore the key features that make them indispensable for building and optimizing AI applications.

Definition and Key Features

LLM tools are designed to facilitate the development, optimization, and deployment of LLM applications at scale. These tools enable AI teams to effectively train, finetune, and deploy large language models (LLMs) to create powerful LLM apps. Key features of these tools include:

• Model Orchestration: Coordinating multiple LLM frameworks and models to ensure seamless integration and operation.

• Real-Time Output Control: Adjusting and refining the outputs of LLM applications during runtime to enhance accuracy and relevance.

• Performance Optimization: Fine-tuning models and adjusting configurations to ensure LLM apps run efficiently at scale, with optimal resource usage and minimal latency.
  
  

Credits: Medium

These features are integral to ensuring that LLM tools deliver the precision and performance required for today's AI-powered solutions.

LLM Tools VS LLMOps Tools

While both LLM tools and LLMOps tools serve the same overarching purpose, enabling teams to build and optimize AI models, they operate at different stages of the process.

• LLM tools are specifically built for managing the technical aspects of model development, such as training, fine-tuning, and deployment of LLM applications.

• LLMOps, on the other hand, refers to the operational practices and tools that help deploy, monitor, and maintain these models in production environments.
  
  

Credits: GeeksforGeeks

Understanding what is LLMOps is crucial because it emphasizes the need for specialized tools to manage the lifecycle of LLM apps, ensuring that models not only perform well during training but also stay optimized, scalable, and reliable in production.

In essence, LLMOps tools work alongside LLM tools to create a seamless ecosystem for AI development and deployment, covering everything from data management to real-time model monitoring and adjustments. This synergy is key to the long-term success of LLMOps strategies.

1. LM Studio

LM Studio is a popular tool designed for the development and deployment of LLM applications, particularly useful for teams working on large-scale AI projects. It provides an intuitive interface to manage the training, fine-tuning, and deployment of models. One of its standout features is its customizable API integration, allowing teams to easily connect the tool with other platforms and services.

Another key feature is its robust version control system, which helps keep track of different versions of models, ensuring smooth experimentation and deployment. Additionally, LM Studio supports model orchestration, making it easier for AI teams to manage complex workflows involving multiple models.

However, one of its potential drawbacks is the steep learning curve for new users, especially those without a strong background in machine learning. While the platform provides extensive functionality, it may take some time for teams to fully integrate it into their AI development processes.

Despite this, LM Studio remains an excellent tool for teams that prioritize customization and scalability in their LLM applications.

2. Ollama

Ollama is an LLM platform that excels in simplifying the deployment of LLM apps in production environments. It supports a wide range of inference engines and provides seamless API integration, making it a flexible option for AI teams. Ollama's workflow orchestration features allow teams to easily manage and automate the flow of data and tasks across different stages of the development cycle.

The platform also offers strong support for observability and monitoring, ensuring that teams can track the performance of their models in real-time. This makes it easier to spot potential issues and optimize performance on the fly. Ollama also supports model serving, enabling efficient delivery of models in production.

However, one potential downside is its pricing structure, which may be prohibitive for smaller teams or startups. Additionally, while Ollama offers many advanced features, it lacks some customization options compared to other tools, which could limit flexibility for certain use cases.

3. LangChain

LangChain is an open-source framework designed to make it easier to build LLM applications by providing a suite of powerful tools for prompt engineering, data management, and integration with external APIs. The platform is built to be highly extensible, allowing users to create custom modules and workflows that fit their specific needs.

One of LangChain's key strengths is its ability to work with vector databases, which improves performance by enabling fast and efficient retrieval of relevant information. The tool also provides robust semantic caching capabilities, helping to speed up response times for large-scale applications.

The downside of LangChain is that it may require a higher level of technical expertise compared to other tools. Its open-source nature means that users must invest more time in setup and configuration, especially if they are not familiar with its ecosystem. Additionally, while LangChain is highly customizable, this can also lead to more complex workflows, requiring more effort to maintain.

4. Hugging Face

Hugging Face is one of the most widely used platforms for developing and deploying LLM apps. It provides a rich library of pre-trained models and tools for fine-tuning and deploying models. Hugging Face excels in its user-friendly interface and easy-to-use API integration, which makes it simple for developers to access and implement models.

The platform also supports experiment tracking, allowing users to monitor the performance of their models and easily compare different versions. Hugging Face’s large community contributes to a wealth of resources, tutorials, and documentation, making it a great option for both beginners and experienced teams.

However, one limitation is that Hugging Face’s commercial offerings can become costly at scale, especially for businesses running multiple models simultaneously. Additionally, while the platform is highly accessible, it may not offer the level of customization that some advanced users might require for specific use cases.

5. Gemini

Gemini, developed by Google, is a family of large language models (LLMs) designed to provide cutting-edge capabilities for building and deploying AI-powered applications. Accessible through Google AI Studio and Vertex AI, Gemini models cater to a range of use cases, from general-purpose text processing to advanced multimodal tasks. With options like Gemini Ultra, featuring 540 billion parameters, and Gemini 1.5 Pro, which supports an extensive 2-million-token context window, these models are tailored to handle complex workflows at scale.

Gemini excels in tasks like coding, mathematical reasoning, and multimodal understanding, making it a top choice for enterprises requiring robust AI tools. Its seamless integration with Google’s cloud infrastructure ensures high reliability and scalability for production-grade deployments.

However, the platform's advanced features and high computational requirements may present challenges for smaller teams or those without extensive technical expertise. Additionally, its reliance on Google's ecosystem could be a limitation for organizations seeking more open or self-hosted solutions. Despite these considerations, Gemini stands out as a powerful LLM tool for developers aiming to build sophisticated AI applications.

6. Cohere

Cohere is a platform designed to help teams develop and deploy LLM apps with a strong emphasis on scalability and cost optimization. Cohere’s architecture is optimized for efficient GPU utilization, ensuring that large models can be trained and deployed without incurring excessive computational costs. This makes it an attractive option for enterprises looking to scale LLM applications without breaking the bank.

The platform also includes built-in observability tools, allowing teams to track and monitor model performance in real time. Cohere supports model serving, enabling businesses to quickly deploy models in production environments.

However, Cohere is a relatively new platform, and while it shows great promise, it may lack some of the advanced features offered by more established tools like Hugging Face. Some users may find the learning curve a bit steep when starting out with Cohere, particularly in terms of model configuration and management.

7. Weights & Biases

Weights & Biases is a comprehensive MLOps platform that helps teams track experiments, manage datasets, and optimize model performance. It’s particularly known for its experiment tracking capabilities, allowing teams to monitor the progress of their models and evaluate their performance over time. Weights & Biases also integrates well with other platforms, offering API integration to streamline the deployment process.

The tool’s version control and model management features are particularly useful for teams working with large models and multiple experiments. Additionally, it provides strong security measures to ensure data protection.

One limitation of Weights & Biases is that it’s more focused on experimentation and tracking than on model serving or deployment, meaning users will often need to integrate it with other tools to complete their full workflow. The platform’s pricing can also become prohibitive for smaller teams or startups as the number of experiments scales.

8. TensorFlow Serving

TensorFlow Serving is an open-source tool focused on model serving for machine learning models, including LLMs. It provides high-performance serving of models, allowing for low-latency and high-throughput deployment in production environments. TensorFlow Serving is highly customizable, supporting both batch and real-time inference.

One of the platform’s biggest advantages is its support for GPU utilization, enabling faster model inference. It also integrates seamlessly with TensorFlow, making it a great option for teams already using this popular framework.

However, TensorFlow Serving may not be the best fit for teams using non-TensorFlow models, as it’s primarily optimized for the TensorFlow ecosystem. Additionally, setting up TensorFlow Serving for large-scale deployments can require a higher level of technical expertise compared to other tools.

9. DeepLake

DeepLake is a vector database solution that is specifically designed for AI applications, making it ideal for managing the large datasets used in LLM applications. The platform excels in semantic caching, which speeds up retrieval times for relevant data, significantly improving the efficiency of LLM apps.

DeepLake also integrates well with LLMOps tools and can handle high-scale model serving. This makes it a great option for businesses that need to quickly deploy and serve models across various use cases.

While DeepLake is highly effective for managing large datasets, it may not offer as many features for model fine-tuning or training compared to other platforms. It’s more focused on storage and retrieval, so teams may need to integrate it with other tools for a complete LLMOps workflow.

10. BLOOM

BLOOM is a state-of-the-art open-source large language model developed by Hugging Face in collaboration with the BigScience project. With 176 billion parameters, BLOOM is designed to democratize access to powerful AI capabilities. Its multilingual proficiency spans 46 natural languages and 13 programming languages, making it an exceptional choice for teams working on diverse global applications.

BLOOM’s integration with the Hugging Face ecosystem allows developers to deploy and fine-tune the model effortlessly for specific use cases. The platform’s open-source nature encourages customization and community-driven improvements, while its autoregressive capabilities make it ideal for tasks such as text generation, translation, and summarization.

Despite its impressive capabilities, BLOOM's computational demands may require significant resources for effective deployment, particularly for large-scale applications. Additionally, while the open-source nature reduces cost barriers, organizations without technical expertise might face challenges in managing and optimizing the model. For teams seeking a flexible and inclusive LLM tool, BLOOM represents a compelling option in the world of AI development.

11. MLflow

MLflow is one of few open-source MLOps platforms designed to manage the end-to-end lifecycle of LLM applications. It offers features for experiment tracking, model versioning, and automated deployment, making it a popular choice for teams focused on LLMOps. MLflow's support for GPU utilization allows for fast model training, while its model registry helps maintain and deploy multiple versions of models.

The tool integrates well with existing machine learning pipelines, ensuring smooth collaboration and workflow management. MLflow’s strong tracking capabilities make it ideal for monitoring the performance of various models and models' interactions in production, particularly in LLM apps.

However, while MLflow excels at tracking experiments, it may require additional tools for model serving and real-time monitoring. Users may need to combine it with other platforms for a fully optimized LLM lifecycle.

12. Seldon

Seldon is an MLOps platform focused on model serving, monitoring, and tracing for AI models, including LLM apps. It is built to help deploy models at scale, with support for continuous integration and deployment pipelines. Seldon’s observability tools allow teams to monitor model performance and quickly debug issues in production environments, making it easier to ensure that models are delivering reliable results.

Seldon supports a wide range of machine learning frameworks and provides integration with cloud-native technologies, allowing for robust and scalable deployment. Its platform is also highly customizable, offering flexibility in integrating different components into a team’s AI workflow.

The downside is that Seldon may require additional configuration and technical expertise to fully leverage its capabilities, especially for teams new to LLMOps tools. Additionally, while it excels at deployment and monitoring, it lacks more advanced features for fine-tuning models directly.

13. Chroma

Chroma is a vector database solution optimized for AI applications, particularly those requiring fast and efficient data retrieval for LLM apps. It allows users to store and search large sets of unstructured data, such as text, images, and audio, making it ideal for AI teams working with complex models. Chroma’s semantic caching feature improves the speed of data retrieval, making it highly efficient for large-scale deployments.

Chroma is also well-suited for integration with LLMOps tools, supporting a seamless workflow for teams working on LLM applications. Its ability to handle large-scale data sets allows teams to run and deploy models without worrying about data bottlenecks.

However, Chroma is primarily focused on data management and vector databases, which means it may not offer as much support for model serving or experimentation. Teams will need to integrate it with other tools for a more comprehensive AI development pipeline.

14. Streamlit

Streamlit is an open-source framework that allows developers to rapidly build and deploy interactive web applications for LLM models. It is particularly popular for creating data-driven applications and interactive dashboards. The platform is highly customizable and offers seamless API integration, enabling developers to create user-friendly interfaces for LLM apps.

Streamlit’s simplicity is one of its biggest strengths, as it allows teams to quickly build prototypes without needing extensive web development expertise. It supports real-time monitoring of model outputs and integrates well with machine learning models for dynamic, live demonstrations.

The limitation of Streamlit lies in its focus on building user interfaces. While it excels at frontend design, teams may need to rely on other tools for more advanced backend features like model optimization or complex model orchestration.

15. Vertex AI

Google Cloud’s Vertex AI is a comprehensive platform that helps teams manage the full lifecycle of LLM applications, from data management and training to model deployment and monitoring. Vertex AI integrates with Google Cloud’s infrastructure, enabling teams to scale their LLM apps efficiently. It provides tools for experiment tracking, version control, and GPU utilization, allowing teams to optimize both training and inference times.

One of Vertex AI’s standout features is its strong integration with other Google Cloud services, making it easier for teams to incorporate additional AI and machine learning capabilities into their workflows. The platform also offers excellent security measures and is highly customizable, supporting various machine learning frameworks.

However, its primary limitation is its cost, which can be prohibitive for smaller teams or businesses. Additionally, the platform’s steep learning curve may make it challenging for those unfamiliar with the Google Cloud ecosystem, requiring more time and resources to get up and running.

LLM Tools vs. LLMOps Tools: Deciding the Right Approach

While LLM tools can be valuable for specific tasks like model training and fine-tuning, they often fall short in meeting the demands of full-scale AI product development. Building reliable LLM applications requires more than just individual tools for training, deployment, and testing—it requires LLMOps solutions that integrate every aspect of the development pipeline.

In AI product development, collaboration between technical and non-technical teams is essential. But patching together various LLM tools for different stages often leads to inefficiencies, such as data siloes and disjointed architectures. With observability and deployment tools scattered across different platforms, teams face difficulty in tracking performance, managing workflows, and ensuring seamless communication between stakeholders. This fragmented approach can significantly slow down development cycles and hinder scalability.

An LLMOps platform, on the other hand, unifies these tools into a single, cohesive workflow. It fosters smoother collaboration and allows teams to focus on building, testing, and deploying LLM apps without worrying about integrating different tools. This holistic approach eliminates the risk of disjointed systems, providing a centralized platform for model deployment, real-time monitoring, and performance optimization, making it much easier to scale AI products efficiently.

Orq.ai: The End-to-End LLMOps Platform

Orq.ai is an end-to-end LLMOps platform designed to simplify the creation, deployment, and management of LLM-based applications. By offering seamless integration with top AI models and robust infrastructure for building AI solutions at scale, Orq.ai ensures that AI teams can easily operate, monitor, and optimize their LLMs without the complexity of managing multiple disconnected tools. With Orq.ai, organizations can effortlessly scale their LLM apps, ensuring faster development cycles and reliable performance.

Orq.ai offers a comprehensive suite of features to help AI teams build, test, and deploy LLM applications effectively:

• Generative AI Gateway: Orq.ai offers an AI Gateway through which teams integrate with over 130 top-tier LLM models from leading providers, allowing organizations to explore and test various models for their unique AI use cases within a unified platform.

• Playgrounds & Experiments: Provide a controlled environment to experiment with different AI models, prompt configurations, and RAG-as-a-Service pipelines for knowledge bases. This allows teams to validate their hypotheses, compare model performance, and assess quality before entering production.

• AI Deployments: Orq.ai simplifies moving AI applications from staging to production. Built-in guardrails, fallback models, and regression testing ensure that AI deployments are reliable, secure, and well-optimized from the start.

• Observability & Evaluation: Monitor the performance of LLM apps in real time with detailed logs and intuitive dashboards. Orq.ai enables teams to integrate programmatic, human, and custom evaluations to track progress and optimize performance continuously.
  
  

Orq.ai Platform Overview

Orq.ai is also committed to the highest standards of security and privacy. The platform is SOC2-certified and fully compliant with GDPR and the EU AI Act, making it the ideal solution for organizations concerned with data security and privacy regulations when deploying AI solutions.

Orq.ai provides the tools and infrastructure to streamline your AI development process and optimize your LLM applications. Book a demo today or explore our documentation to learn more about how Orq.ai can help you build, scale, and manage AI applications with ease.

Future Trends in LLM Tools and LLMOps

The landscape of LLM tools is rapidly evolving as AI development demands grow. Future trends indicate a shift towards more specialized LLM platforms that provide greater scalability and real-time performance optimization. LLMs worldwide are expected to become increasingly integrated with advanced analytics, facilitating more nuanced model evaluations and performance tuning. The rise of tool-LLM integrations—such as those connecting popular frameworks like HuggingFace and TensorFlow—is paving the way for more adaptable, customizable LLM tool use in various industries.

As AI applications continue to expand, AI teams need platforms that not only offer flexibility but also streamline workflows and ensure secure, compliant operations. This is where Orq.ai stands out. The platform is already future-proofing AI teams with robust support for LLM apps. Orq.ai enables seamless integration with a wide array of tool-LLM ecosystems, including popular services such as Azure, Vertex AI, and LangChain, ensuring developers can adopt emerging technologies without worrying about compatibility issues.

Looking ahead, the increasing demand for multi-modal models and LLMs optimized for specific domains will drive innovation in LLM platforms. Orq.ai’s capabilities, including automated monitoring, observability, and scalable deployments, ensure that teams are well-equipped to handle these emerging challenges. Orq.ai is committed to providing artificial intelligence tools applications that evolve with the industry, helping organizations build and scale the LLM apps of tomorrow.

LLM Tools: Key Takeaways

As the world of LLM tools continues to evolve, the need for comprehensive platforms to manage and scale these models is more important than ever. While many LLM tools serve niche purposes, building, deploying, and optimizing LLM-based applications requires a robust, all-encompassing solution like Orq.ai. With its seamless integrations, real-time observability, and secure deployment processes, Orq.ai enables AI teams to not only develop but also maintain LLM apps effectively.

By choosing Orq.ai, organizations gain access to an end-to-end LLMOps platform that simplifies complex workflows, offering both scalability and flexibility for any AI product development cycle. With the rise of AI technologies, future-proofing your operations is crucial, and Orq.ai is committed to supporting teams in building AI applications that are reliable, scalable, and ready for the challenges of tomorrow.