--- title: "SumizAI vs Mellum" url: "https://sumizai.com/alternative-to/sumizai-alternative-to-mellum.html" date: "2026-08-14T00:00:00+02:00" modified: "2026-08-14T00:00:00+02:00" description: "Looking for a Mellum alternative? SumizAI and Mellum side by side: price, licence, platforms and what each one actually does." tags: ["ai notes", "sumizai", "mellum", "alternative to Mellum"] --- # SumizAI vs Mellum Looking for a Mellum alternative? SumizAI and Mellum side by side: price, licence, platforms and what each one actually does. ## What Mellum is Mellum is the first open-source large language model built by JetBrains, a company best known for its IntelliJ, PyCharm, and WebStorm development environments, used by millions of developers worldwide. The model, available as "4b-base" (4 billion parameters), was optimized specifically for code-related tasks — completing, generating, and understanding source code fragments, rather than general conversation or essay writing. Releasing the model under the open Apache-2.0 licence means JetBrains makes the model weights available to download, run locally, and further modify — an unusual but increasingly common move by large software companies trying to build trust with the developer community instead of locking everything behind a paid API. Key features: A size of 4 billion parameters makes Mellum relatively small compared to giants like GPT or Claude with hundreds of billions of parameters, which has a concrete practical consequence — the model can run locally, on a user's own hardware, without needing the expensive server-class GPUs required by models with tens or hundreds of billions of parameters. Its specialization in code tasks means the model was trained with an emphasis on understanding programming-language syntax, design patterns, and typical code structures, rather than general encyclopedic knowledge or the ability to hold a free-flowing conversation. "Self-Hosted" availability combined with a Python interface suggests the intended audience is engineering teams wanting to integrate the model directly into their own CI/CD pipeline, without sending company code to a third-party external API. The Apache-2.0 licence additionally permits commercial use of the model without licensing fees or an obligation to share modifications, setting Mellum apart from many models released for research and non-commercial purposes only. Who it's for: Mellum is aimed primarily at engineering teams and companies for whom sending their own source code to external large-model APIs (like GPT or Claude) is a problem for security, regulatory-compliance, or simple company-policy reasons that forbid data from leaving internal infrastructure. It's a choice for organizations that want full control over the AI infrastructure supporting their developers — hosted locally, on their own servers, without depending on an external provider's uptime or pricing, which can change overnight. It also suits researchers and open-source enthusiasts interested in analyzing and modifying code-specialized models, without needing budget for expensive API subscriptions. Use cases: In practice, Mellum can power an internal code-completion tool integrated directly into a team's development environment, similar to the feature JetBrains offers commercially in its IDEs under the AI Assistant brand. A company in finance or healthcare, where code can't leave the internal network due to regulation, can host Mellum locally as a secure alternative to cloud coding assistants. A DevOps team can use the model to automatically generate configuration fragments or scripts from a natural-language description, without sending sensitive infrastructure data outside the company firewall. Pricing and business model: Mellum is entirely free and open source under the Apache-2.0 licence — there are no licensing fees for downloading, running, or commercially using the model in a company product. The real cost for users is the infrastructure needed to host the model (server, GPU) and the engineering time spent integrating it into a team's existing workflow. By publishing Mellum as an open project, JetBrains builds reputation and technical trust among developers rather than direct revenue from the model itself, betting that the goodwill translates into sales of paid IDE licences. Limitations and what to watch for: Mellum is a raw model, not a finished app — running it requires technical knowledge: setting up a Python environment, access to appropriate hardware (ideally with a GPU), and basic familiarity with language-model serving tools. For a non-technical person who just wants to chat with AI, Mellum in its current form is practically inaccessible without help from an engineering team to build an interface around it. A size of 4 billion parameters, while practical for local hosting, also means more limited general capability compared to models with hundreds of billions of parameters — Mellum handles code-related tasks well, but shouldn't be expected to match the versatility of large conversational models on tasks outside that specialization. The model has no built-in chat interface or any layer for managing conversation history, so the concept of ai notes in the sense of a saved, searchable conversation doesn't exist at the model level — it would have to be built from scratch as a separate application layer. Companies without their own AI infrastructure team may find the total cost of deployment (hardware, integration, maintenance) higher than a simple commercial API subscription. What's worth checking before choosing: Before deploying Mellum, check the hardware requirements listed in the repository — 4 billion parameters is still a meaningful amount of VRAM if reasonable response speed without significant latency is the goal. Also review the Apache-2.0 licence against your company's specific use case, especially if you plan to combine the model with data subject to industry regulation. Teams that ultimately want to build a ai notes tool around Mellum should expect that the entire interface, conversation-history, and answer-storage layer has to be built from scratch as a separate project. Bottom line: Mellum makes sense for engineering teams with a genuine need to host their own model for code tasks and the resources to maintain it. For someone looking for a ready-made app with ai notes functionality — an interface for talking to AI and automatically saving answers — Mellum as a raw model isn't the right starting point; a finished app built with the end user in mind, rather than technical integration, will serve better. Comparison with SumizAI: Mellum and SumizAI both rely on language models, but in entirely different contexts. Mellum is a code-specialized model meant for technical integration by developers — there's no chat interface or mechanism for turning answers into ai notes here, because it's a raw model, not a finished end-user application for a regular user. SumizAI is a finished end-user app, where a conversation with an AI model (one of several supported providers) leads directly to ai notes saved as a Markdown file in the user's own vault. Anyone building their own developer tools that use AI for code will appreciate Mellum's openness; anyone who wants a ready-made tool for talking to AI and saving answers as ai notes without writing a single line of integration code will find that in SumizAI. **Key facts** - Price: Free - License: Open Source - Origin: Czechia, EU - Category: Large Language Model (LLM), AI Coding Assistant ## What SumizAI is A note-taking application built around a conversation with an AI model. You ask a question, the answer streams back, and the answers worth keeping become Markdown notes — filed into a vault that is a folder on your own disk. A vault is a directory holding `base.md`, a generated table of contents up to six levels deep, and a `notes/` folder with one `.md` file per note. Before a note is written it is checked against the ones already there, so the fourth note about the same idea gets merged instead of added. Links between notes are ordinary Markdown links to files that exist. The model is never ours: you bring your own API key to one of seven providers — Anthropic, OpenAI, Gemini, Groq, OpenRouter, a local Ollama or your own server — and pay that provider directly. A question sends the table of contents plus at most five relevant notes within a 24,000-character budget, and the app shows you which five it used. ## Where they differ - Both mention AI. The difference is whose key it runs on: SumizAI never resells inference — you connect one of seven providers (Anthropic, OpenAI, Gemini, Groq, OpenRouter, a local Ollama or your own server) with your own API key and pay that provider directly. Check what Mellum does with the model bill before comparing prices. - Mellum is open source (Apache-2.0), and SumizAI is not. If reading the source is what decides it for you, that is a real argument for Mellum. The guarantee SumizAI offers instead is structural rather than legal: the notes are Markdown files in a folder you chose, so they open in any editor whether or not the application is running. - AlternativeTo's description of Mellum does not mention Markdown, so check what format your notes end up in before you fill it up. SumizAI writes standard `.md` files to a folder you picked, and an unpaid account can still export all of them. - Mellum can be self-hosted. SumizAI has nothing to host on the desktop — the vault is a folder on your machine — and if you want the model on your own hardware too, a local Ollama is one of the seven providers it speaks to. - Mellum also does tasks. SumizAI does not — no boards, no due dates, no projects. It files knowledge, not work. - AlternativeTo lists no mobile version of Mellum. SumizAI runs on macOS, Windows, iOS and Android. - Both are run from the EU: SumizAI's operator is registered in Poland and the servers are in Germany. Full side-by-side comparison table and pricing: [https://sumizai.com/alternative-to/sumizai-alternative-to-mellum.html](https://sumizai.com/alternative-to/sumizai-alternative-to-mellum.html)