Best LLM API Providers for Databutton
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Compare the Top LLM API Providers that integrate with Databutton as of July 2025
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This a list of LLM API providers that integrate with Databutton. Use the filters on the left to add additional filters for products that have integrations with Databutton. View the products that work with Databutton in the table below.
What are LLM API Providers for Databutton?
LLM API providers offer developers and businesses access to sophisticated language models and LLM APIs via cloud-based interfaces, enabling applications such as chatbots, content generation, and data analysis. These APIs abstract the complexities of model training and infrastructure management, allowing users to integrate advanced language understanding into their systems seamlessly. Providers typically offer a range of models optimized for various tasks, from general-purpose language understanding to specialized applications like coding assistance or multilingual support. Pricing models vary, with some providers offering pay-as-you-go plans, while others may have subscription-based pricing or free tiers for limited usage. The choice of an LLM API provider depends on factors such as model performance, cost, scalability, and specific use case requirements. Compare and read user reviews of the best LLM API providers for Databutton currently available using the table below. This list is updated regularly.
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OpenAI
OpenAI
OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions. -
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Cohere
Cohere AI
Cohere is an enterprise AI platform that enables developers and businesses to build powerful language-based applications. Specializing in large language models (LLMs), Cohere provides solutions for text generation, summarization, and semantic search. Their model offerings include the Command family for high-performance language tasks and Aya Expanse for multilingual applications across 23 languages. Focused on security and customization, Cohere allows flexible deployment across major cloud providers, private cloud environments, or on-premises setups to meet diverse enterprise needs. The company collaborates with industry leaders like Oracle and Salesforce to integrate generative AI into business applications, improving automation and customer engagement. Additionally, Cohere For AI, their research lab, advances machine learning through open-source projects and a global research community.Starting Price: Free -
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Claude
Anthropic
Claude is an artificial intelligence large language model that can process and generate human-like text. Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues. For now, we’re primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.Starting Price: Free -
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Groq
Groq
Groq is on a mission to set the standard for GenAI inference speed, helping real-time AI applications come to life today. An LPU inference engine, with LPU standing for Language Processing Unit, is a new type of end-to-end processing unit system that provides the fastest inference for computationally intensive applications with a sequential component, such as AI language applications (LLMs). The LPU is designed to overcome the two LLM bottlenecks, compute density and memory bandwidth. An LPU has greater computing capacity than a GPU and CPU in regards to LLMs. This reduces the amount of time per word calculated, allowing sequences of text to be generated much faster. Additionally, eliminating external memory bottlenecks enables the LPU inference engine to deliver orders of magnitude better performance on LLMs compared to GPUs. Groq supports standard machine learning frameworks such as PyTorch, TensorFlow, and ONNX for inference.
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