# 10 Self-Hosted AI Tools Jul 22, 2025 by Ronan McQuillan AI is becoming central to all kinds of business processes. This offers almost unprecedented potential for efficiency and improvements within workflows. At the same time, though, businesses are constrained by several considerations that can affect their choice of solutions. For large organizations, one of the most prominent examples of this is self-hosting. That is, many enterprises and other security-focused organizations place a high level of emphasis on solutions that can be hosted on their own infrastructure, especially where these relate to mission-critical data or processes. This is particularly important in the context of AI tools, given their centrality to a range of business processes, as well as comparatively high requirements for connecting to data. Today, we’re covering everything you need to know, including: - [What is a self-hosted AI tool?](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#what-is-a-self-hosted-ai-tool) - [Why would we want to run AI tools locally?](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#why-would-we-want-to-run-ai-tools-locally) - [10 self-hosted AI tools](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#10-self-hosted-ai-tools) - [LLMs](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#llms) - [Frameworks](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#frameworks) - [AI platforms, automation tools, and app builders](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#ai-platforms-automation-tools-and-app-builders) - [Observability and management tools](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#observability-and-management-tools) Let’s jump right in. ## What is a self-hosted AI tool? This is something of a broad term. A self-hosted AI tool could be any LLM-powered system or component of our wider AI tool stack that we deploy and host on our own infrastructure, as opposed to accessing it as a cloud-based service. In other words, this could include elements we use to build AI systems from scratch, such as LLMs or vector databases, as well as AI-powered end-user solutions or visual development tools such as no/low-code AI app builders. While the drive to self-host exists across a huge range of solutions, it’s important to note that it can present a few key challenges in the context of AI. In large part, this can come down to the comparatively high computational requirements of AI systems, compared to other types of software. For example, running an LLM locally is often highly resource-intensive, especially for models with larger parameter counts. So, in many instances, businesses may opt for configurations where some elements are self-hosted, but other capabilities still rely on calling out to a cloud service. For example, we might use a self-hosted tool to build automation rules or configure the behavior of an AI system, but still utilize external services to actually connect to an LLM. Alternatively, in some highly-sensitive use cases, we might need the entirety of the system to be hosted on-prem, including our LLM. For this reason, we’ll see a couple of options for building these kinds of solutions a little later when we come to look at specific self-hosted AI tools. ## Why would we want to run AI tools locally? First, though, it’s worth considering why self-hosting is such a priority for many businesses, especially in the context of AI. In other words, what benefits do self-hostable solutions offer us over their wholly cloud-based equivalents? In one way, this comes down to `control`. More specifically, self-hosting provides us with complete control over how our tools are deployed, including any security or auditing measures we may wish to put in place. This is particularly important in an enterprise context, where large amounts of the data and other resources that AI tools might need to interact with are already often self-hosted for security reasons. Besides this, self-hosting can be more cost-effective in many situations, especially where maintaining and managing our own infrastructure works out cheaper than the recurring costs of licensing the equivalent cloud services. Lastly, many teams prioritize self-hosting to remove their dependency on external services. For instance, enabling them to avoid vendor lock-in or other negative impacts of becoming overly reliant on external systems for mission-critical processes. ## 10 self-hosted AI tools With a better understanding of what self-hosted AI tools are and why we might opt for them, we can begin to examine some of the specific options that are available to us. Earlier, we hinted at some of the more granular types of self-hosted AI tools we might want to adopt. Following on from this, we’ve divided our picks by specific kinds of tools. Specifically, we’ve chosen the following clusters of tools: - [AI platforms, automation tools, and app builders](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#ai-platforms-automation-tools-and-app-builders) - [Budibase](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#budibase) - [n8n](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#n8n) - [CrewAI](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#crewai) - [LLMs](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#llms) - [Gemma](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#gemma-3) - [Mistral](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#mistral) - [Frameworks](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#frameworks) - [LangChain](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#langchain) - [AutoGen](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#autogen) - [Observability and management tools](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#observability-and-management-tools) - [LangSmith](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#langsmith) - [Langfuse](https://budibase.com/blog/ai-agents/self-hosted-ai-tools/#langfuse) | Tool | Best for | Category | Technical level | |----------|------------------------|----------------------------|------------------| | Budibase | Internal AI tools | AI workflow platform | Low-medium | | n8n | AI workflows | Automation platform | Medium | | CrewAI | Multi-agent systems | Agent platform | Medium-high | | Gemma | Local open models | LLM family | High | | Mistral | On-prem models | LLM family | High | | LangChain| Custom AI apps | Framework | High | | AutoGen | Multi-agent apps | Framework | High | | LangSmith| Tracing and evals | Observability | Medium | | Langfuse | LLM observability | Observability | Medium | ## AI platforms, automation tools, and app builders First, we have an increasingly popular class of self-hostable AI tools. Broadly speaking, these are tools that enable us to build AI solutions without necessarily requiring extensive technical abilities or coding skills, usually relying instead on low-code experiences. This can take a couple of different forms. For instance, building automation rules, entire applications, or AI agents. So, we’ve chosen platforms from across this segment. #### Budibase Budibase is the open-source AI workflow toolkit for privacy-first teams. Security-conscious organizations choose our platform to build all kinds of AI-powered internal tools. With extensive connectivity for RDBMSs, APIs, NoSQL tools, and more, alongside autogenerated UIs, powerful visual automations, comprehensive AI capabilities, custom RBAC, free SSO, and more, there’s never been a better way to build professional apps at pace. Budibase is used by teams of all sizes to handle workflows including approvals, ticketing, portals, inventory management, and more. We've also recently launched Budibase Agents, empowering teams to build intelligent assistants with their own data, models, tools, and APIs. Budibase Agents are fully optimized for the needs of privacy-first teams who want to adopt AI without sacrificing control over their internal data. #### n8n n8n is a hugely popular open-source automation builder, offering a visual experience for configuring workflows across a huge range of integrated tools. It’s particularly popular with more technical teams, offering extensive flexibility and scope for custom code. In recent years, n8n has also positioned itself as a powerful solution for building AI agents, using visual development tools, including several pre-built templates and nodes for common real-world use cases. In addition, n8n offers a wealth of testing and debugging capabilities, making it a powerful option for developers who want to build production AI tools. #### CrewAI CrewAI is one of the leading platforms for transforming workflows with multi-agent systems. Within this, it offers a range of tools, including a coding framework and a low-code platform in the form of CrewAI Studio. This offers multi-platform support, including self-hosting through Docker or Conda, alongside extensive connectivity for LLMs, data, and tools, all in a low-code interface. On top of this, CrewAI offers extensive capabilities for tracking, monitoring, and iterating over solutions, making it a great choice for deploying multi-agent systems in a wide range of contexts. ### Observability and management tools Lastly, it’s important to consider additional tools that we might need to help us manage our AI systems in production. In particular, we have several options for monitoring AI tools’ behavior and performance in real-world environments. Here are two of our picks. #### LangSmith From the makers of LangChain, LangSmith is a unified observability and evals platform for AI systems. There’s close support for tools built in LangChain, LangGraph, or other tools and platforms. It offers a powerful suite of observability and tracing tools, enabling us to understand AI behavior in non-deterministic workflows, in turn helping to improve the effectiveness of our solutions. On top of this, LangSmith offers the Playground and Prompt Canvas UI, sets of tools for experimenting with prompts and LLMs. There’s also extensive functionality for creating dashboards, helping us to visualize performance. #### Langfuse Lastly, we have Langfuse. This is an open-source, wide-ranging LLM engineering platform with functionality for tracing, evals, prompt management, and monitoring. This positions it as a powerful solution for testing, debugging, and improving AI systems. It’s a particularly impressive offering for prompt management, including composability and version control. On top of this, there’s a suite of tools for deriving datasets from production data for fine-tuning and testing. Langfuse is also highly integratable, offering SDKs for Python and TypeScript, as well as native integrations for a wide range of libraries and support for OpenTelemetry.