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Google is a Leader in The Forrester Wave™: AI Foundation Models for Language, Q2 2024

Today, we are excited to announce that Google is a Leader in The 2024 Forrester Wave™: AI Foundation Models for Language, Q2 2024, receiving the highest scores of all vendors evaluated in the Current Offering and Strategy categories.

“Gemini is uniquely differentiated in the market especially in multimodality and context length while also ensuring interconnectivity with the broader ecosystem of complementary cloud services.” – The Forrester Wave™: AI Foundation Models for Language, Q2 2024

Download the complimentary copy of The Forrester Wave™: AI Foundation Models for Language, Q2 2024.

Generative AI is transforming how we interact with technology. Powerful managed models are now in the hands of developers, who are building innovative new apps, experiences, and agents for end users. Tuning models is more accessible than ever, requiring only 1% of the data needed in the past. Across the spectrum, gen AI continues to accelerate.

At Google, we have a deep history of AI research and innovation, including the Transformer architecture, diffusion models, and other pioneering efforts central to today’s gen AI applications. 

Gemini is our multimodal family of models, the result of a large-scale collaborative effort by teams across Google, including Google DeepMind and Google Research. Built from the ground up to seamlessly combine and understand text, code, images, audio, and video, Gemini models are helping developers to create cutting-edge AI agents across virtually all industries. 

Gemini is available to customers through Vertex AI, Google Cloud’s fully-managed, unified platform for developing, deploying and monitoring machine learning models at scale. Equipped to support both generative and predictive AI models, Vertex AI enables customers to customize and deploy Gemini and other AI models with enterprise-ready tuning, grounding, monitoring, and inference capabilities, all alongside leading AI infrastructure and easy-to-use tooling to build AI agents.  

State-of-the-art performance

The following Gemini models are available for enterprise customers via Vertex AI:

Gemini 1.5 Pro: Earlier this year we announced Gemini 1.5 Pro, now GA, providing our customers with an industry-leading, breakthrough context window of 1 million tokens that enables accurate processing across large documents, codebases, or entire videos with a single prompt. For use cases that require an even larger context window — such as analyzing very large code bases or extensive document libraries — customers will soon be able to try Gemini 1.5 Pro with up to a 2 million token context window (sign up here to join the waitlist for the 2 million token context window).

Gemini 1.5 Flash, also now GA, offers our groundbreaking context window of 1 million tokens, but is lighter-weight than 1.5 Pro and designed to efficiently serve with speed and scale for tasks like chat applications. 

Gemini 1.0 Pro: Designed to handle natural language tasks, multiturn text and code chat, and code generation. A new version is generally available with decreased latency and improved quality, along with supervised tuning capabilities. 

Gemini 1.0 Pro Vision: Supports multimodal prompts. You can include text, images, and video in your prompt requests and get text or code responses.

Vertex AI

Vertex AI makes it possible to customize and deploy Gemini, empowering developers to build new and differentiated applications that can process information across text, code, images, and video at this time. With Vertex AI, developers can:

Discover and use Gemini, or select from a curated list of more than 130 models from Google, open-source, and third-parties that meet Google’s strict enterprise safety and quality standards. Developers can access models as easy-to-use APIs to quickly build them into applications.

Customize model behavior with specific domain or company expertise, using tuning tools to augment training knowledge and even adjust model weights when required. Vertex AI provides a variety of tuning techniques including prompt design, adapter-based tuning such as Low Rank Adaptation (LoRA), and distillation. We also provide the ability to improve a model by capturing user feedback through our support for reinforcement learning from human feedback (RLHF).

Augment models with tools to help adapt Gemini Pro to specific contexts or use cases. Vertex AI Extensions and connectors let developers link Gemini Pro to external APIs for transactions and other actions, retrieve data from outside sources, or call functions in codebases. Vertex AI also gives organizations the ability to ground foundation model outputs in their own data sources, helping to improve the accuracy and relevance of a model’s answers. We offer the ability for enterprises to use grounding against their structured and unstructured data, and grounding with Google Search technology.

Manage and scale models in production with purpose-built tools to help ensure that once applications are built, they can be easily deployed and maintained. Customers can evaluate models with Automatic Side by Side (Auto SxS), an on-demand, automated tool to compare models. Auto SxS is faster and more cost-efficient than manual model evaluation, as well as customizable across various task specifications to handle new generative AI use cases.

Build AI agents in a low code / no code environment. With Vertex AI Agent Builder, developers across all machine learning skill levels can use Gemini models to create engaging, production-grade AI agents in hours and days instead of weeks and months. 

Deliver innovation responsibly by using Vertex AI’s safety filters, content moderation APIs, and other responsible AI tooling to help developers ensure their models don’t output inappropriate content.

Help protect data with Google Cloud’s built-in data governance and privacy controls. Customers remain in control of their data, and Google never uses customer data to train our models. Vertex AI provides a variety of mechanisms to keep customers in sole control of their data including Customer Managed Encryption Keys and VPC Service Controls.

Recent innovations 

Ongoing innovation in Vertex AI is designed to bring the best models from Google and the industry alongside an end-to-end model building platform and capabilities to develop and deploy agents faster — all built on a foundation of scale and enterprise readiness. Recent product innovations include: 

Batch API, is a super-efficient way to send large numbers of non-latency sensitive text prompt requests, supporting use cases such as classification and sentiment analysis, data extraction, and description generation. It helps speed up developer workflows and reduces costs by enabling multiple prompts to be sent to models in a single request.

Context caching, in public preview this month, lets customers actively manage and reuse cached context data. As processing costs increase by context length, it can be expensive to move long-context applications to production. Vertex AI context caching helps customers significantly reduce costs by leveraging cached data.  

Controlled generation, coming to public preview later this month, lets customers define Gemini model outputs according to specific formats or schemas. Most models cannot guarantee the format and syntax of their outputs, even with specified instructions. Vertex AI controlled generation lets customers choose the desired output format via pre-built options like YAML and XML, or by defining custom formats. JSON, as a pre-built option, is live.  

LlamaIndex on Vertex AI simplifies the retrieval augmented generation (RAG) process, from data ingestion and transformation to embedding, indexing, retrieval, and generation. Now Vertex AI customers can leverage Google’s models and AI-optimized infrastructure alongside LlamaIndex’s simple, flexible, open-source data framework, to connect custom data sources to generative models. 

Genkit, announced by Firebase, is an open-source Typescript/JavaScript framework designed to simplify the development, deployment, and monitoring of production-ready AI agents. Facilitated through the Vertex AI plugin, Firebase developers can now take advantage of Google models like Gemini and Imagen 2, as well as text embeddings. 

Grounding with Google Search, now generally available, allows you to connect models with world knowledge, a wide possible range of topics, or up-to-date information on the internet. By grounding Gemini models with Google Search, we offer customers the combined power of Google’s latest foundation models along with access to fresh, high-quality information to significantly improve the completeness and accuracy of responses. 

Gemma 2 is the next generation in our family of open models built for a broad range of AI developer use cases from the same technologies used to create Gemini. Gemma 2 models will soon be available in Vertex AI Model Garden.

Imagen 3, coming soon to Vertex AI, is our highest-quality text-to-image generation model yet, able to generate an incredible level of detail and produce photorealistic, lifelike images.

How customers are innovating with Gemini models

Vertex AI has seen strong adoption with API requests increasing nearly 6X from H1 to H2 last year. We are really impressed with the amazing things customers are doing with Gemini models particularly because they are multimodal and can handle complex reasoning so well.

Samsung: Samsung recently announced that their Galaxy S24 series is the first smartphone equipped with Gemini models. Starting with Samsung-native applications, customers can take advantage of summarization features across Notes and Voice Recorder. Samsung is confident their end users are protected with built-in security, safety, and privacy in Vertex AI.

Jasper: Jasper, an AI marketing platform that enables enterprise marketing teams to create on-brand content and campaigns at scale, is using Gemini models to quickly generate marketing campaign content for their customers. Teams can now move faster while maintaining a high quality bar for content, ensuring it adheres to brand voice and marketing guidelines.

Quora: Quora, the popular question and answer platform, is using Gemini to help power creator monetization on their AI chat platform, Poe, where users can explore a wide-variety of AI-powered bots. Gemini is enabling Poe creators to build custom bots across a variety of use cases including writing assistance, generating code, personalized learning, and more.

We are honored to be a Leader in The Forrester Wave™: AI Foundation Models for Language, Q2 2024 as well as the The Forrester Wave: AI Infrastructure Solutions, Q1 2024 report. We have channeled decades worth of AI R&D expertise into building models, ultra-scale infrastructure and Vertex AI capabilities to benefit our customers. We are committed to ongoing research and innovation in the field of AI. 

Google has everything it takes to lead the AI market – enormous AI infrastructure capacity, a very deep bench of AI researchers, and a growing number of enterprise customers in Google Cloud.” – The Forrester Wave™: AI Foundation Models for Language, Q2 2024

The full report can be accessed here: The Forrester Wave™: AI Foundation Models for Language, Q2 2024.

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