2025 Generative AI Software Marketscape

Salesforce, ServiceNow, and OpenText

These applications embed advanced GenAI capabilities within enterprise software, enhancing workflows, customer service, and document management. Solutions like these deliver actionable insights directly in business contexts. However, a key challenge for enterprises is the limited customization options and generally the AI enhancements can’t be used outside of the application—these platforms are often built for general use cases, which may restrict the ability to adapt to niche or highly specialized business processes, requiring additional resources for customized implementations.

MuleSoft, Pega Systems, and UI Path

These applications incorporate GenAI to improve the coordination and automation of cross-departmental workflows. These platforms facilitate intelligent decision-making by integrating data and insights across systems. While automation platforms may superficially appear to include many important ingredients like data and app connectors, orchestration, and observability, they were not architected for GenAI and fall short when it comes to experimentation, evaluation, prompt management, or any form of RAG. 

Databricks, Snowflake, and Dataiku

These apps enable enterprises to manage and analyze large datasets, offering powerful tools for transforming data into actionable insights with GenAI. These platforms are integral for organizations looking to operationalize data insights at scale. The biggest challenge for data platforms is that it’s not a suitable foundation for building GenAI applications—it’s purpose-built for data processing and analysis. Additional challenges remain with limited support for unstructured data and GenAI models. 

Dify, Literal, and Coze

These platforms build responsive chatbots or assistants for dynamic, chat-based interactions and generally aim to address shortcomings with conversational AI applications. While organizations gain greater control over the handling of business-specific data in chats, chat is ultimately a limited use of GenAI and is not ideal for handling sensitive or complex queries and tasks. Chatbots are best for general purpose knowledge, but a poor medium for fine-grained business tasks requiring specialized prompts or sensitive business content that is restricted to select roles in an organization, like reviewing a vendor contract against a corporate policy. 

PromptHub, LangSmith, and PromptGPT

Prompt management software focuses on creating and managing prompts for GenAI models. As GenAI solutions scale across an organization, it becomes increasingly important to centrally manage prompts outside of code. Managing prompts in a tool with a UI provides greater access to users who are not software engineers, promotes reusability, and eases maintenance as prompts and models evolve over time. However, prompt management is only a single component of a larger GenAI solution and it’s now a common feature found in other categories of GenAI tools and frameworks. 

BrainTrust and HumanLoop

Designed to enhance GenAI model performance, these platforms measure the accuracy and relevance of model output using validation logic in code, LLMs-as-a-judge, or human-in-the-loop testing. Since GenAI model evaluation requires prompts and some observability to manually or programmatically grade the results, evaluation software often crosses over into other categories of GenAI tools and frameworks so it’s important to distinguish between the core focus areas of different tools when creating a bespoke GenAI infrastructure.

Arize AI, DataDog, and LangFuse

Observability platforms provide insights into the production operations and performance of GenAI deployed across departments and solutions. One major shortcoming of most observability software is that they focus solely on observing GenAI models and not the AI services that expose the models in applications. This can leave gaps in the monitoring and auditability of GenAI applications and agents. For example, the ability to Model Studiostrack in production who sent which inputs and prompts to what model, how long it took to get a response from the model, how many tokens were used, and what was the output. 

Flowise and LangGraph

GenAI orchestration tools manage complex, multi-step GenAI processes or workflows, often involving different models and tasks. These orchestration tools are more specialized and purpose-built for GenAI than the traditional process automation platforms that have been popular with enterprise IT groups for many years. The limitation is that orchestration only provides the fabric for GenAI processes and still relies on other AI tools and frameworks to formulate a complete GenAI solution.

Elasticsearch, Milvus, MongoDB, Pinecone, PostgreSQL, Qdrant, and Weaviate

Vector databases have greatly benefitted from the popularity of AI assistants and RAG. They enable storing and searching embeddings that drive similarity (vector) search. Vector search is great for when you need to interpret natural language—like in a chat—but vector databases fall short when you don’t want to search based on similarity. Enterprises need to utilize different search techniques for different tasks and vector search is only one aspect of a comprehensive RAG strategy—structured (lexical) search and graph search are also very important. The truth is that hybrid search is the most flexible approach, so you want to ensure your database supports multiple types of data structures and search techniques. 

LLumiverse, LangChain, LlamaIndex, and Haystack

LLM frameworks provide developers with the libraries and development tools needed to build highly customized GenAI solutions from scratch. 

The primary challenge for enterprises is the high resource investment needed for custom development. While frameworks can help you build solutions, you still need to bring in all the other components and figure out how to connect them. Then these components need to be managed for each use case. Essentially, you are building solutions from scratch which demands skilled personnel, substantial time, and technical oversight, often resulting in longer deployment timelines and increased costs—especially challenging for enterprises aiming to scale rapidly or maintain competitive agility.

OpenAI, Google Vertex AI, IBM watsonx™, Amazon Bedrock, Azure OpenAI, Groq, Replicate, Together AI, Hugging Face, and Mistral AI

Inference providers specialize in delivering fully trained GenAI models that can be utilized for specific tasks. These services are optimized for running AI models at scale, allowing organizations to use AI capabilities without building or managing their own infrastructure. Inference providers focus on serving real-time predictions or outputs based on input data, ensuring speed, accuracy, and scalability.

They often include APIs for integrating directly with enterprise applications, however this is truly a “build from scratch approach” that risks vendor and/or model lock-in. When integrating via direct API with an inference provider, organizations may end up locking into a specific model and provider which makes it incredibly difficult to switch models or providers later. This is a significant problem with the rapid introduction of new LLMs and commoditization of better, faster, and cheaper GenAI models.