Best Enterprise Generative AI Platforms
Enterprise Generative AI platforms offer full-stack generative AI capabilities designed to integrate with enterprise data and applications. These platforms offer language models that can generate text, code, and images, like other generative AI tools. Enterprise Generative AI tools differentiate themselves by allowing for full integration with companies’ internal data and processes. Enterprise Generative AI models can be fine-tuned to better suit company voice, use case, and compliance standards. Vendors will also have options for retrieval...
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Zoom AI Companion delivers real-time capabilities to help users improve productivity and work together more effectively. Zoom AI Companion is available throughout the Zoom platform, with features for products including Meetings, Team Chat, Contact Center, Phone, Docs, Mail and Calendar.…
Writer is a full-stack generative AI platform. It consists of Writer-built LLMs, a graph-based RAG, AI guardrails, and a flexible application layer. Writer boasts users at enterprises like L’Oreal, Vanguard, and Accenture.
Astra DB from DataStax is a vector database for developers that need to get accurate Generative AI applications into production, fast.
Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation…
An Application Platform for Large Language Models — a platform crafted to bridge the gap between LLMs and enterprise applications that includes tools and frameworks that helps developers and architects to craft precise, efficient interactions with LLMs. Users can design prompt templates,…
Knowledge Workers struggle to leverage GenAI for knowledge generation across vast systems and documents. GateAI transforms data into actionable insights, creating focused scopes that empower smarter, faster decision-making for knowledge workers. The platform is specifically designed for knowledge workers, to enable them to make faster decisions with trusted AI insights. Through the platform's Human-in-the-loop framework, GateAI enables knowledge worker…
For enterprises, Microsoft 365 Copilot (or just Microsoft Copilot) is a generative AI operating as an intelligent virtual assistant for work. Through a chat interface, business users can use it to solve a variety of complex tasks.
AI21 supports the development of enterprise AI Systems and foundation models, stating their mission is to supply cutting-edge deep tech research into enterprise-ready AI systems. AI21 offers privately deployed models with tailored solutions for organizations.
Amazon Bedrock offers a way to build and scale generative AI applications with foundation models, providing a developer experience to work with a broad range of FMs from AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon.
An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides…
An advanced generative AI, DeepSeek LLM is an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese.
Teammately is an autonomous AI agent designed for AI engineers to build, evaluate, and refine AI products, models, and agents. It empowers users to define objectives, and then autonomously iterates using LLMs, prompts, RAG, and ML to achieve results beyond human-level manual iteration.…
IBM's third generation of AI language models, fit for purpose and open sourced, these enterprise-ready models are built to deliver exceptional performance against safety benchmarks and across a wide range of enterprise tasks from cybersecurity to RAG.
Google Gemini (formerly Bard) is an AI chat service, presented as a creative and helpful collaborator. Gemini for Workspace is available via two plans: a Gemini Enterprise add-on, replacing Duet AI for Workspace Enterprise, and a new Gemini Business add-on, at a lower price point,…
Cohere's language models helps businesses explore, generate, search for, and act upon information. Cohere can be used to power an enterprise chat agent that answers questions grounded in company knowledge, and that can take actions and drive processes. Or Cohere's multilingual embedding…
Dynamiq is a GenAI operating platform designed for enterprises and developers looking to build, deploy, and manage AI applications with efficiency and security. It consolidates the entire GenAI lifecycle into a single platform.
Key Features & Capabilities
•Multi-Agent Orche…
Alibaba Cloud provides Tongyi Qianwen (Qwen) model series, including the Qwen large language model (LLM); Qwen-VL, the large language vision model; Qwen-Audio, the large language audio model; Qwen-Coder, the coding model; and Qwen-Math, the mathematical model.
An open-source language models available free under an Apache 2.0 license, specialized on business use cases and leveraging private data and usage feedback. The Mistral 7B transformer model is presented as fast-deployed and easily customisable.
Amazon Q is a generative AI assistant designed to give users fast, relevant answers to pressing questions and solve problems, generate content, or take actions using the data and expertise found in a company's information repositories, code, and enterprise systems.
Anthropic is an AI research company whose product, Claude, is an AI assistant available for a variety of business tasks. Boasting a 100K+ token windows, Claude can handle complex multi-step instructions over large amounts of content.
Alibaba Cloud Model Studio is a platform for generative AI development with access to foundation models (FMs) Qwen-Max, Qwen-Plus, Qwen-Turbo, and Qwen 2. Users can customize these models with enterprise data by setting up a Retrieval-Augmented Generation (RAG) architecture.
Zerve simplifies the complexities of tracking, managing, collaborating on, and deploying Data Science and AI/ML projects. The Zerve development platform, built specifically for code-first data teams, enhances transparency and reduces the time from prototype to production.
OpenAI headquartered in San Francisco, aims to ensure that artificial general intelligence benefits all of humanity. OpenAI’s API provides access to GPT-3, which performs a wide variety of natural language tasks, and Codex, which translates natural language to code.
NVIDIA BioNeMo™ is a generative AI platform for drug discovery that simplifies and accelerates the training of models using an organization's own data and scaling the deployment of models for drug discovery applications. BioNeMo offers a path to AI model development and deployment,…
Haystack is an open source LLM framework to build customizable, production-ready LLM applications. Users can connect components (models, vector DBs, file converters) to pipelines or agents that can interact with data. Haystack also offers production-ready tools to quickly build generative…
Learn More About Enterprise Generative AI Platforms
What is Enterprise Generative AI?
Enterprise Generative AI platforms offer full-stack generative AI capabilities designed to integrate with enterprise data and applications. These platforms offer language models that can generate text, code, and images, like other generative AI tools. Enterprise Generative AI tools differentiate themselves by allowing for full integration with companies’ internal data and processes. Enterprise Generative AI models can be fine-tuned to better suit company voice, use case, and compliance standards. Vendors will also have options for retrieval augmented generation (RAG), which allows models to access and generate outputs based on enterprise data.
These platforms are used to automate and/or assist with a variety of repetitive business processes as well as create custom applications. The customizable nature of generative AI means that use cases are broad and include a variety of departments, from marketing to HR, and industries, from finance to healthcare.
Security, compliance, and governance are important factors for Enterprise Generative AI platforms. Enterprise-grade products offer greater control over how proprietary data is used. Some Enterprise Generative AI platforms include AI Governance tools. AI Governance features can help monitor generative AI outputs’ compliance with internal brand, compliance, and privacy standards.
Enterprise Generative AI Features
Most products in the Enterprise Generative AI category have the following features:
- Vendor-owned LLMs
- Text, image, and/or code generation
- Fine-tuning to account for company voice and other requirements
- Retrieval augmented generation (RAG), allowing models to access and use enterprise data
- API for integration and custom application building
- Usage analytics
- Customizable privacy, security, and compliance options
Enterprise Generative AI Comparison
When purchasing an Enterprise Generative AI platform, buyers should keep in mind:
- Compliance and Governance Requirements: Some vendors offer greater control over ensuring prompts and model outputs comply with brand, legal, and regulatory compliance than others. Consider your company’s compliance requirements, and whether or not you need or want a separate AI Governance tool.
- Technological Complexity vs. Customizability: While all Enterprise Generative AI platforms have APIs for integration and custom app building, some vendors offer more options for premade templates and prebuilt interfaces for prompting their models than others. Depending on your company’s use case, these plug-and-play options may or may not be desirable.
- Use Case: Some vendors offer models that are trained on specialized datasets and may be better suited for industry-specific use cases out of the box. Some vendors offer models that can generate only text, while others offer models that generate text, images, and code. Consider exactly how you hope to use Generative AI and what kind of training and capabilities you might benefit from.
- Accuracy: For enterprises and large scale deployments in particular, it is crucial that generative AI outputs accurately represent company data and policies. Compare how models have performed in common benchmark tests and what methods of knowledge retrieval each model uses. It is a good idea to test and evaluate a few top candidates directly - try a few fact- and/or retrieval-based prompts that are similar to how you plan on using the model.
Start an Enterprise Generative AI comparison here
Pricing Information
Companies must contact vendors to obtain custom pricing quotes for Enterprise Generative AI platforms. Mid-tier pricing is generally based on usage, while enterprise pricing may take other factors such as number of users into account as well. Most vendors offer free and/or relatively cheap entry-level pricing for individual users or small teams.