It is three o’clock on a Wednesday afternoon, and Lin from marketing is looking for last year’s Q4 pricing review. She remembers it living in Notion. She types the phrase into the search box and gets three pages back, every one of them titled “Pricing discussion”, none of them the one she wants. She tries Slack, where somebody definitely posted a screenshot, and works through three sets of keywords without a hit. Then she opens Google Drive, scrolls past a few dozen files, and finds a deck called final_v3_really_final. It belongs to a different product line.
Ten minutes are gone. By now she is not sure the review exists at all. She only knows that if it does, it should be sitting somewhere she can reach.
At thirty people this is the normal state of things. Knowledge sits in four or five systems, half of it in somebody’s Drive and half in a channel that scrolled past in March. A small team gets away with memory and one shout in the group chat. Once headcount grows, a new hire does not even know whom to shout at.
Onyx was built for that afternoon. Its GitHub repository describes the project as the knowledge and context layer for teams and AI agents: it connects to more than fifty applications, pulls in documents, messages and tickets, builds an index over all of it, and answers “how did we set Q4 pricing” from a chat window.
Two terms in that description carry most of the weight. RAG, short for retrieval-augmented generation, means the model itself has never read your company’s files. The system first searches your material for a batch of relevant passages, then puts those passages into the model’s context and asks it to answer from them. A connector is the adapter that brings an outside system in: it authenticates against Notion, Slack or Google Drive, pulls the content, mirrors the permissions, and schedules the next refresh. Without connectors the index is a dry well. Without an index the model can only answer from what it saw during training, which is another way of saying it will guess.
Onyx builds its own index instead of searching live at question time. It keeps a vector index and a keyword index side by side, one for passages that mean the same thing in different words, the other for exact strings like a SKU or a clause number. The project calls the combination hybrid. The README makes its case bluntly: rather than send an agent off to rummage through dozens of MCP servers and burn thousands of tokens on every question, pull the few most relevant source passages out of an index that was built in advance. That is the project arguing for its own design, so read it with that in mind, but the reasoning holds.
Running it is easy; running it well is not
Getting Onyx up is not the hard part. The official install is one line, and Docker Compose does the rest. The hard part starts on Monday.
Onyx ships in two modes. Lite is a light chat interface, and the project says its memory footprint stays under 1GB, but it does not build an index, which means whatever the connectors pull in never reaches a searchable store. The full feature set lives in Standard, and Standard sits on a vector database, background containers running the sync jobs, model services for embeddings and inference, Redis, and object storage. That is not a combination an idle laptop absorbs. The deployment documentation covers Docker, Kubernetes, Helm and Terraform, plus the major clouds, which tells you who the intended operator is. There is a separate page on resource planning, and pages like that are not written for people who expect to figure it out on the way.
Self-hosting means the software runs on hardware you control, an office server or a rented instance, and the upgrades, backups, TLS certificates and the call when syncing breaks at an awkward hour all belong to you. In daily terms: when a connector fails halfway through a Slack sync, somebody reads container logs. When the embedding model changes version, somebody re-runs the embeddings. When permissions arrive from Google Workspace and do not line up, somebody traces why. None of these jobs is heavy. They are continuous, and they tend to land on the same person.
On licensing, the community edition is MIT, and the core RAG, chat, Agents and Actions are inside it. For no money, that is a lot of software. The enterprise edition adds OIDC and SAML single sign-on, self-hosted and regional deployment, white-labeling, and audit export. The project site points to a pricing page with two tiers, Business and Enterprise. Business is sold by team size. Enterprise says contact us. The page publishes no numbers, and for a team that has to get budget approved in advance, that opacity is its own barrier.
So the reasons people go looking for an alternative tend to be concrete. Someone wants a clean chat entry point for the team and has no use for an index. Someone is stuck on syncing tens of thousands of documents and wants a lighter install. Someone already builds on Dify and does not want to run a second platform. And someone has decided that open-source self-hosting should not be their job in the first place. “Onyx is bad” is a rare complaint.
The six options below are grouped by the missing piece, not ranked by feature count. A table makes the differences easier to see.
| Tool | Deployment | License | Data source connectors | Best for |
|---|---|---|---|---|
| Onyx | Docker Compose, Kubernetes, Helm and Terraform; can run fully offline | Community edition MIT; commercial license for Enterprise | 50+ applications per the project, 40+ connectors listed on the pricing page | Mid-size and large teams that need a multi-source index with permissions carried over from the source systems |
| Dify | Docker Compose, cloud, enterprise self-hosting | Dify Open Source License, Apache 2.0 with additional conditions | Document upload and knowledge base pipelines | Product and engineering teams building AI applications and workflows |
| AnythingLLM | Desktop app, Docker, several cloud templates | MIT | Mostly document upload: PDF, TXT, DOCX and similar | Small teams or individuals who want to be running in fifteen minutes |
| Open WebUI | pip, Docker, Kubernetes | Open WebUI license, requires keeping the branding | Local upload, Google Drive, OneDrive and SharePoint, plus a companion sync tool | Teams that already have model serving and only lack a front end |
| LibreChat | Docker, self-hosted server, scales horizontally | MIT | File upload and a separate RAG service | Teams that want a ChatGPT-style interface across several models |
| Glean | Vendor-hosted SaaS | Closed commercial license | 275+ connectors per the company’s site | Large enterprises with budget and heavy compliance requirements |
The column people skip is data source connectors, and it decides whether the product feels like a knowledge tool or a folder with a chat box on top. A tool that accepts an upload and a tool that has already read three years of Slack threads will answer the same question very differently. The license column deserves similar attention. MIT and “Apache 2.0 with additional conditions” are different documents to a lawyer, particularly if the plan is to put the software inside something you sell.
If what you want to build is an application
Suppose a twenty-person e-commerce company. The customer service lead wants a bot that can answer return and exchange policy questions. Operations wants a summary of the previous day’s bad reviews waiting every morning. Neither of them needs a company-wide search box. They need a few fixed pipelines that connect a model, a prompt, a knowledge base and a couple of external endpoints.
Dify is at home in that territory. The center of the product is a visual canvas: drag nodes onto it, connect them, drop knowledge retrieval, conditional branches and HTTP requests in between, then publish the result as a web app or an API. RAG is a pipeline you can inspect stage by stage, from parsing and chunking through vectorizing and retrieval. On the model side it connects to dozens of inference providers and self-hosted options, so it rarely objects to what you already pay for. Deployment is Docker Compose, and the official minimum is 2 cores and 4GiB, which means an ordinary cloud instance is enough to start.
Its license is called the Dify Open Source License, Apache 2.0 with several additional conditions attached. That is one more layer of constraint than plain MIT, and before commercial use it deserves a read of the actual text rather than a summary. Multi-cloud and the enterprise edition cost extra, and SSO, RBAC and an SLA live there.
The price of using Dify in place of Onyx is a change of viewpoint. Dify is for building a thing. Onyx is for looking a thing up. One is flexible, the other is less work. If the goal is that anyone in the company can ask about the company, an application you assemble in Dify will usually need you to add permissions, org structure and audit around it, and that is not a small amount of work.
The barrier, lowered almost to the floor
Take another hypothetical. A three-person design studio keeps contracts, quotes and project retrospectives on individual laptops. There is no IT department, and nobody wants to maintain a server. What they want is software they can install, drop a folder into, and ask questions of.
AnythingLLM is built for that moment. It ships desktop builds for macOS, Windows and Linux: install it, pick a model, drag documents into a workspace, start talking. A local model engine based on llama.cpp is included, so it runs without an API key, with speed determined by the machine you are on. The vector store defaults to LanceDB and also supports PGVector, Qdrant and Milvus. The whole project is MIT, which keeps the paperwork short.
There are limits worth knowing before the folder goes in. Multi-user support and permissions exist only in the Docker version; the desktop build is a single-user tool. Data sources are mostly document uploads. PDF, TXT and DOCX are fine, but if your knowledge lives in Slack conversations and Jira tickets, it will not go and fetch them the way Onyx does. It also collects anonymous telemetry, which can be switched off in settings, and the README is unusually specific about what gets collected. That kind of detail is easy to respect.
For a small team, imperfect but usable today often beats a feature matrix.
Sometimes you only need a front door
A common situation: the company has already standardized model access through Ollama or an OpenAI-compatible gateway. The models are there. What is missing is an interface people are willing to open. Installing Onyx for that is like building a water utility to get a glass of water.
Open WebUI is that interface, and a little more than an interface. It connects to any OpenAI-compatible endpoint and to Ollama directly, and it installs through pip, Docker or Kubernetes. Multi-user support, user groups and granular permissions are all present, along with LDAP/AD, OAuth and SCIM, the authentication methods enterprises tend to ask about. Local RAG is built in: the project says it supports nine vector stores, retrieval runs as a hybrid of BM25 and vectors, and reranking is available. For files, it can pick from Google Drive and OneDrive or SharePoint natively, and a companion sync tool feeds the knowledge base from GitHub, Confluence, Jira, Slack, Notion and more than forty other sources.
That last part needs a plain sentence of its own. The forty-odd sources come from the companion component, not from the main application, and deploying them is a separate exercise. Plenty of people read the feature list, assume it works out of the box, and find out otherwise on installation day.
The license is the Open WebUI license, with a clause requiring the branding to stay in place. Internal use is unaffected. Rebranding it and shipping it as your own product means reading that clause carefully. The feature spread is wide: channels, notes, a calendar, scheduled tasks. One entry point for everything is convenient, and for a team that only wants question and answer, the interface can feel busier than the job requires.
For people who switch models the way other people switch tabs
Another kind of team uses models unevenly. One person insists on Claude for copywriting, someone else runs DeepSeek to keep costs down, and engineering is still connected to Azure. They do not want five websites and five accounts. They want one place to switch between them with the history intact.
LibreChat does that job. The interface is visibly modeled on ChatGPT, with providers collected in a single dropdown and the model changeable in the middle of a conversation. Agents, MCP, a code interpreter, artifacts and search across all messages are included. Multi-user authentication covers OAuth2, LDAP and email login, and an administrator can change roles and permissions in the browser without redeploying. The project is MIT, which is more straightforward than several of the “open source with conditions” licenses in this list.
Its knowledge base runs on a separate RAG service that has to be deployed on its own. If you want to ask questions of company documents, you build the retrieval layer yourself. LibreChat gives you the interface and the API. It does not give you fifty ready-made connectors. For engineers that is a normal afternoon. For an office manager it is a different story.
The same problem, solved by not solving it
The first five options all assume you will do the work. Glean assumes the opposite. It does not offer self-hosting. What you buy is a managed service where the vendor connects the data, builds the index and manages permissions on your behalf. The homepage is direct about the numbers: more than 275 connectors, more than 40 models, permission awareness, and a compliance list that includes SOC 2 Type II, ISO 27001, HIPAA and GDPR.
Its customers are equally specific: organizations with a thousand or more employees, a large pile of systems, and legal or compliance departments holding a veto. For an organization like that, the staffing and compliance cost of running Onyx in-house may well exceed the subscription. Turn it around, and a thirty-person team trying to buy Glean will probably not get a quote at all, because the sales motion is enterprise and there is no self-serve signup.
Glean is in this article because the word alternative sometimes means replacing a different question: not which tool, but who carries the operations. Onyx hands that answer to you. Glean takes it back. The options in between sit at various points along that line.
Pick the pain you want to stop feeling
If somebody on the team already maintains Kubernetes, the documents really are spread across Notion, Confluence, Slack and Drive, and permissions have to follow the source systems, Onyx remains the closest fit. It has put that particular combination together properly, and the cost is that you have to be able to afford to run it. Starting with Lite to test the interface before committing to Standard is the safer path.
If the actual goal is an AI application for customers or an internal process, one where prompts get revised, external endpoints get called and request logs get read, Dify fits better. Its strength is on the building side. Do not expect it to turn into company search on its own.
If there are only a few people, no operations staff, and a need to start today, the AnythingLLM desktop build has little competition. Its ceiling is visible too. More people and more scattered sources mean it is time to move.
If the model layer is already settled and all that is missing is an entry point people will use, Open WebUI is good value. Put the companion sync tool in the deployment plan, or the knowledge base stays empty.
If models change more often than the office coffee supplier, and the team needs one multi-model chat desk, LibreChat feels closest to what people already know. The knowledge base is the part you add yourself, and that is the space it deliberately leaves open.
If the company is large enough that compliance will ask where the data resides and how permissions are audited, and the budget exists, a hosted option like Glean is less trouble. It is not an open-source replacement so much as the option of not doing it yourself.
Back to Lin’s afternoon. She eventually found the review through a shared link from a colleague who had left, a link that had expired long ago, because someone had saved a copy. Luck should not be the main mechanism by which a company knows things. Choosing a tool is choosing what you are willing to pay so that next time luck is not required. Some pay for servers, some pay a subscription, some pay an afternoon of deployment. All three are reasonable. What is worth avoiding is buying a system that nobody owns the syncing for, then finding three months later that the index is full of stale documents and everyone is back in the group chat, asking if anyone has the file.
Related reading
- Tracecat Alternatives: Best Open Source Security Automation Platforms 2026
- Authentik Alternatives: Choosing an Open Source SSO for Small Teams (2026)
- Meta Muse Spark Open Needs 1TB of RAM: The Real Cost of Running Open Models on a Mac
- The Real Barriers Blocking Enterprise AI Agent Adoption in 2026
- Which AI Search Tool Actually Works? Perplexity vs ChatGPT Search vs Kimi vs Metaso in 2026
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