VLLM is designed as a library for large language models (LLMs), though common problems have been noticed with its functionality generally. Most of the pains come from the quite steep learning curve required to properly set up and use the platform, rendering it very hard for new users to understand.
This is why it was documented in detail, but sometimes, that makes it appear complex to a user who is not aware of its application and hence confuses them while trying to install it. Others have reported the failure of the library, sometimes not to scale in work with larger models, resulting in slower performances or even crashes.
It may turn out to be a barrier for the user, and therefore, intensive computationally demanding processes need reliable performance when handling large datasets. Hence, some already started searching for VLLM alternatives that promise smoother integration as well as increased stability for LLMs.
There are up to 9 VLLM Alternatives. It has features like Development, Document-processing and Library Project. The best alternative to VLLM is Datatron, which is Premium. The other best apps like VLLM are WhyLabs, Lightning AI, and BentoML.
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VLLM Information
9 Best VLLM Alternatives
1: WhyLabs
WhyLabs is an AI LLM Security and Observability Platform that enables users to manage every aspect of production management from quality to performance. You can get the insights and datasets to improve AI applications and block harmful interactions. The platform enables you to protect your AI application and ensures your application is secure.
2: Helicone.ai
Helicone.ai is an AI-based platform that is specially created for developers to manage their data for their various development projects. The technology creates insights into user activity, automates complex work, and simplifies data processing. It provides 100% log coverage along with sub-millisecond latency impact to have high productivity in development.
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3: Verta
Verta is an AI Workbench and Model Catalog Platform that provides amazing application development procedures for builders and developers. It provides dataset suggestions for testing, monitoring, and evaluating products. You can go with multiple models, automated testing, prompts, and automation to get quicker results.
4: Lightning AI
Lightning AI is an AI Development Platform that allows you to do more things while building any AI application. Like, you can run prototypes, simulations, batch jobs, queues, and many other things. You can build any type of product such as for personal use or enterprise use. You can build templates according to clients' demands and then share them with others.
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5: Datatron
Datatron is an AI Monitoring and Governance Platform that provides scalability in the production of your AI tool. The platform is built to help data scientists so that they can gather their data and it's like an information management system. You can simplify cataloging, provisioning, managing, and deploying apps and models into production.
6: honeyhive.ai
honeyhive.ai is an AI Evaluation, Testing, and Project Management Platform that provides tools and techniques to make and ship AI products. Using it, you can improve your iteration processing so you can go with changes in the development. You can manage usage, feedback, performance, and quality levels to identify issues.
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7: LangWatch
LangWatch is an AI User Analytics Platform that enables users to track and debug all procedures so they can minimize AI risks. It gives a comprehensive tools kit to visualize, manage, and evaluate your LLM apps to perform better. You can block undesired responses to work it out.
8: LLmonitor
LLmonitor is an AI Observability Platform that enables users to track analytics, usage, tracing, prompts, metrics, and cost. Using this app, you can monitor users, conversations, costs, and many other things. Using tracing, you can debug easily, inspect full requests, unit tests, labels, and create datasets. You can easily integrate it with other tools and host it by yourself.
9: BentoML
BentoML is an open-source AI Unified Inference Platform that enables users to build custom models. You can test, manage, and deploy systems at the production stage without any difficulty. You can make inference APIs, multi-model pipelines, and many other job queues. You can get access to APIs with web-based user interfaces, clients, and REST API.