Best Artificial Intelligence (AI) APIs for TensorFlow
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Compare the Top Artificial Intelligence (AI) APIs that integrates with TensorFlow as of June 2025
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This a list of Artificial Intelligence (AI) APIs that integrates with TensorFlow. Use the filters on the left to add additional filters for products that have integrations with TensorFlow. View the products that work with TensorFlow in the table below.
What is Artificial Intelligence (AI) APIs for TensorFlow?
Artificial Intelligence APIs are software that provide access to advanced technology, AI, and machine learning algorithms designed to solve complex problems. They allow developers to create applications with smarter artificial intelligence features such as natural language processing, image recognition, and more. Many companies use AI APIs to automate tasks or gain insights into customer data so they can improve their products or services. AI APIs are constantly evolving, enabling businesses to benefit from cutting-edge technologies while decreasing the time required for development. Compare and read user reviews of the best Artificial Intelligence (AI) APIs for TensorFlow currently available using the table below. This list is updated regularly.
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1
Vertex AI
Google
Vertex AI provides robust AI APIs that enable developers to integrate advanced machine learning and artificial intelligence capabilities into their applications. These APIs facilitate easy access to pre-trained models, allowing businesses to add AI features such as natural language processing, image analysis, and predictive analytics into their existing systems. Vertex AI’s APIs are designed to be user-friendly and flexible, supporting various programming languages and platforms. New customers receive $300 in free credits, allowing them to experiment with the available APIs and integrate AI functionality into their products. With these APIs, businesses can enhance their applications with cutting-edge AI capabilities without having to build models from scratch.Starting Price: Free ($300 in free credits) -
2
Qloo
Qloo
Qloo is the “Cultural AI”, decoding and predicting consumer taste across the globe. A privacy-first API that predicts global consumer preferences and catalogs hundreds of millions of cultural entities. Through our API, we provide contextualized personalization and insights based on a deep understanding of consumer behavior and more than 575 million people, places, and things. Our technology empowers you to look beyond trends and uncover the connections behind people’s tastes in the world around them. Look up entities in our vast library spanning categories like brands, music, film, fashion, travel destinations, and notable people. Results are delivered within milliseconds and can be weighted by factors such as regionalization and real-time popularity. Used by companies who want to incorporate best-in-class data in their consumer experiences. Our flagship recommendation API delivers results based on demographics, preferences, cultural entities, metadata, and geolocational factors. -
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spaCy
spaCy
spaCy is designed to help you do real work, build real products, or gather real insights. The library respects your time and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry standard with a huge ecosystem. Choose from a variety of plugins, integrate with your machine learning stack, and build custom components and workflows. Components for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and more. Easily extensible with custom components and attributes. Easy model packaging, deployment, and workflow management.Starting Price: Free -
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Google AI Edge
Google
Google AI Edge offers a comprehensive suite of tools and frameworks designed to facilitate the deployment of artificial intelligence across mobile, web, and embedded applications. By enabling on-device processing, it reduces latency, allows offline functionality, and ensures data remains local and private. It supports cross-platform compatibility, allowing the same model to run seamlessly across embedded systems. It is also multi-framework compatible, working with models from JAX, Keras, PyTorch, and TensorFlow. Key components include low-code APIs for common AI tasks through MediaPipe, enabling quick integration of generative AI, vision, text, and audio functionalities. Visualize the transformation of your model through conversion and quantification. Overlays the results of the comparisons to debug the hotspots. Explore, debug, and compare your models visually. Overlays comparisons and numerical performance data to identify problematic hotspots.Starting Price: Free -
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Distributed AI is a computing paradigm that bypasses the need to move vast amounts of data and provides the ability to analyze data at the source. Distributed AI APIs built by IBM Research is a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge computing environments. Each Distributed AI API addresses the challenges in enabling AI in distributed and edge environments with APIs. The Distributed AI APIs do not focus on the basic requirements of creating and deploying AI pipelines, for example, model training and model serving. You would use your favorite open-source packages such as TensorFlow or PyTorch. Then, you can containerize your application, including the AI pipeline, and deploy these containers at the distributed locations. In many cases, it’s useful to use a container orchestrator such as Kubernetes or OpenShift operators to automate the deployment process.
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