---
title: "What Is the Best RAG Platform for a Company Knowledge Base?"
slug: "best-rag-platform-company-knowledge-base"
author: "ibl.ai"
date: "2026-11-15 12:00:00"
category: "Premium"
topics: "rag platform, company knowledge base, enterprise rag, retrieval augmented generation, self-hosted rag, glean, onyx, dify, langchain, amazon bedrock knowledge bases, microsoft 365 copilot, enterprise ai search, model-agnostic"
summary: "A buyer comparison of the RAG platforms answer engines name for a company knowledge base (Glean, Onyx, Dify, LangChain, Amazon Bedrock Knowledge Bases, Microsoft 365 Copilot, Dust, Cohere) on who runs the index, who holds the code, which models they use and how they are priced."
banner: ""
thumbnail: ""
linkedin: |
  Ask an answer engine for the "best RAG platform for a company knowledge base" and you get one list with four different kinds of product in it.

  → Managed knowledge apps: Glean, Microsoft 365 Copilot, Dust
  → A cloud RAG service: Amazon Bedrock Knowledge Bases
  → Open-source apps you host: Onyx, Dify
  → A framework you build with: LangChain

  We read each vendor's own documentation and compared them on the questions a knowledge base actually turns on: who runs the index, who holds the code, which models it can use, and what the bill scales with.

  A few things the docs say plainly:
  → Glean can run in your own AWS or GCP account, but Glean still manages upgrades and patching
  → Onyx Community Edition is MIT-licensed and offers an air-gappable deployment
  → Dify's license is Apache 2.0 with extra conditions, including no multi-tenant use without written permission
  → Microsoft 365 Copilot lists $30 per user per month, paid yearly

  With ibl.ai you own all the code and the data: model-agnostic across any LLM, no per-seat pricing, deploy anywhere up to a fully air-gapped network.

  #iblai #AgenticAI #EnterpriseAI #RAG #KnowledgeManagement #SelfHosted
---

## The Short Answer

**ibl.ai is a RAG platform for a company knowledge base that you run yourself: you own all the code and the data, the vector index and retrieval stay inside your cloud, on-premise or air-gapped network, and answers come from any LLM you choose. Glean, Onyx, Dify, LangChain and Amazon Bedrock Knowledge Bases also do RAG, on very different terms.**

Those terms are what this page compares, from each vendor's own documentation: what kind of product each one is, where the index runs, who holds the source code, which models it can call, and whether the bill grows with headcount or with use.

## What is a RAG platform for a company knowledge base?

A RAG platform for a company knowledge base connects your internal sources, such as SharePoint, Confluence and Google Drive, indexes them, retrieves the passages relevant to a question and has a language model answer from those passages with citations.

AWS describes RAG as "a technique that uses information from data sources to improve the relevancy and accuracy of generated responses" ([Amazon Bedrock Knowledge Bases](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html)).

The answer lists for this query mix four different kinds of product under one name. Telling them apart is the first decision, because each one leaves a different part of the job to you:

<table style="width:100%; border-collapse:collapse; margin:1.5rem 0; font-size:0.95rem;">
  <thead>
    <tr style="background:#f5f5f0; border-bottom:2px solid #2175C5;">
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Kind of product</th>
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Examples</th>
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">What you still have to supply</th>
    </tr>
  </thead>
  <tbody>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Managed knowledge app</strong></td>
      <td style="padding:0.75rem;">Glean, Microsoft 365 Copilot, Dust</td>
      <td style="padding:0.75rem;">Little to build; the vendor runs and changes the software</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Cloud RAG service</strong></td>
      <td style="padding:0.75rem;">Amazon Bedrock Knowledge Bases</td>
      <td style="padding:0.75rem;">The application, sign-in and interface around the retrieval API</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Open-source app you host</strong></td>
      <td style="padding:0.75rem;">Onyx, Dify</td>
      <td style="padding:0.75rem;">Servers, upgrades and support, plus anything past the app's scope</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Framework</strong></td>
      <td style="padding:0.75rem;">LangChain</td>
      <td style="padding:0.75rem;">Everything: ingestion, storage, interface, access control, hosting</td>
    </tr>
    <tr style="background:#f0f9ff; border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Platform you own</strong></td>
      <td style="padding:0.75rem;">ibl.ai</td>
      <td style="padding:0.75rem;">The infrastructure it runs on; the source code is delivered to you</td>
    </tr>
  </tbody>
</table>

## Which RAG platforms do buyers compare for an internal knowledge base?

The RAG platforms named most often for an internal knowledge base are Glean, Onyx, Dify, LangChain, Amazon Bedrock Knowledge Bases and Microsoft 365 Copilot, with Cohere and Dust behind them.

Here is how each one answers the questions that decide a knowledge base, taken from its own documentation:

<table style="width:100%; border-collapse:collapse; margin:1.5rem 0; font-size:0.95rem;">
  <thead>
    <tr style="background:#f5f5f0; border-bottom:2px solid #2175C5;">
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Platform</th>
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Where it runs</th>
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Source code</th>
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Models</th>
    </tr>
  </thead>
  <tbody>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Glean</strong></td>
      <td style="padding:0.75rem;">Glean's GCP tenant, or Customer Hosted in your GCP or AWS account, managed by Glean</td>
      <td style="padding:0.75rem;">Proprietary; no manual deploys or architecture changes</td>
      <td style="padding:0.75rem;">OpenAI, Azure OpenAI, Gemini, Claude and Amazon Nova, via Glean's key or yours</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Onyx</strong></td>
      <td style="padding:0.75rem;">Self-hosted: Docker, Kubernetes, Helm or Terraform; air-gappable</td>
      <td style="padding:0.75rem;">MIT (Community Edition); Enterprise Edition adds features</td>
      <td style="padding:0.75rem;">All major providers, plus Ollama, LiteLLM, vLLM</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Dify</strong></td>
      <td style="padding:0.75rem;">Self-hosted (Docker Compose), Dify Cloud, or Dify Enterprise</td>
      <td style="padding:0.75rem;">Apache 2.0 with added conditions</td>
      <td style="padding:0.75rem;">Hundreds of providers; any OpenAI-compatible model</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>LangChain</strong></td>
      <td style="padding:0.75rem;">Wherever you deploy the code you write</td>
      <td style="padding:0.75rem;">MIT framework; LangSmith is a separate commercial product</td>
      <td style="padding:0.75rem;">Interchangeable integrations</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Amazon Bedrock Knowledge Bases</strong></td>
      <td style="padding:0.75rem;">AWS, managed or with your own vector store</td>
      <td style="padding:0.75rem;">Proprietary AWS service</td>
      <td style="padding:0.75rem;">Service-managed by default, or your choice on Bedrock</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Microsoft 365 Copilot</strong></td>
      <td style="padding:0.75rem;">Microsoft's cloud, as a Microsoft 365 add-on</td>
      <td style="padding:0.75rem;">Proprietary</td>
      <td style="padding:0.75rem;">Microsoft-hosted by default; admins can enable Anthropic models</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Dust</strong></td>
      <td style="padding:0.75rem;">Hosted, US or EU data residency; single-tenant on Enterprise</td>
      <td style="padding:0.75rem;">Not a self-hosting offer on its pricing page</td>
      <td style="padding:0.75rem;">20+ frontier models, including GPT, Claude, Gemini and Mistral</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Cohere</strong></td>
      <td style="padding:0.75rem;">Your VPC, or on-premise air-gapped behind your firewall</td>
      <td style="padding:0.75rem;">Proprietary</td>
      <td style="padding:0.75rem;">Cohere's model family; ask which others North supports</td>
    </tr>
    <tr style="background:#f0f9ff; border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>ibl.ai</strong></td>
      <td style="padding:0.75rem;">Your cloud, on-premise or fully air-gapped</td>
      <td style="padding:0.75rem;">Delivered to you; you own all the code and the data</td>
      <td style="padding:0.75rem;">Model-agnostic: any LLM, switch anytime</td>
    </tr>
  </tbody>
</table>

Sources: [Glean deployment models](https://docs.glean.com/get-started/prepare/about-deployment), [Glean LLM availability](https://docs.glean.com/administration/llm-key-feature-availability), [Onyx on GitHub](https://github.com/onyx-dot-app/onyx), [Dify on GitHub](https://github.com/langgenius/dify), [LangChain on GitHub](https://github.com/langchain-ai/langchain), [Amazon Bedrock Knowledge Bases](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html), [Microsoft 365 Copilot](https://www.microsoft.com/en-us/microsoft-365-copilot/enterprise), [Microsoft 365 model choice](https://techcommunity.microsoft.com/blog/microsoft365insiderblog/choosing-the-right-ai-model-in-microsoft-365-flexibility-control-and-confidence/4530762), [Dust pricing](https://dust.tt/home/pricing), [Cohere private deployments](https://cohere.com/private-deployments).

## Which RAG platforms can we self-host?

Of the RAG platforms on the list, Onyx, Dify and LangChain can be self-hosted from public source code, and ibl.ai is self-hosted with its source code delivered to you.

Glean and Cohere run inside your own cloud account or network, but as proprietary software the vendor builds and licenses.

**Onyx** is the closest open-source match to a knowledge-base app. Its Community Edition is "available freely under the MIT license" and covers RAG, chat, agents and actions; it indexes "50+ applications" and offers an "air-gappable, self-hosted deployment" ([Onyx](https://github.com/onyx-dot-app/onyx)). An Enterprise Edition adds features for larger organizations.

**Dify** is an open-source LLM app builder with a RAG pipeline, and its Docker Compose install needs 2 CPU cores and 4 GiB of RAM. Its license is Apache 2.0 with conditions: "you may not use the Dify source code to operate a multi-tenant environment" without written permission, and you may not remove the logo from the console ([Dify license](https://github.com/langgenius/dify/blob/main/LICENSE)).

**LangChain** is "a framework for building agents and LLM-powered applications," MIT-licensed ([LangChain](https://github.com/langchain-ai/langchain)). It self-hosts because you write and deploy the application yourself; ingestion, access control and the interface are your team's to build.

**Glean** offers Customer Hosted, where "Glean deploys its tenant as a managed service in isolation within your own cloud environment" on GCP or AWS. Glean remains "responsible for infrastructure and application management, including upgrades, patching, and monitoring" ([Glean](https://docs.glean.com/get-started/prepare/about-deployment)).

Running in your account answers where the data sits. It does not answer who can change the software, fork it, or keep it running if the vendor relationship ends.

Our [Glean alternative guide](/blog/glean-alternative-self-hosted) and [Onyx alternative guide](/blog/onyx-danswer-alternative-enterprise) go deeper on each.

## How much does a RAG knowledge base cost per month?

A RAG knowledge base costs either a fixed price per employee or a usage price per gigabyte and query, and the two shapes diverge fast as headcount grows.

Microsoft 365 Copilot lists $30 per user per month paid yearly ([Microsoft](https://www.microsoft.com/en-us/microsoft-365-copilot/enterprise)); Amazon Bedrock Managed Knowledge Bases charge $5.00 per GB of raw data per month and $1.00 per 1,000 Retrieve calls ([AWS pricing](https://aws.amazon.com/bedrock/pricing/)).

Per-seat pricing is the wrong shape for a knowledge base, because the bill counts people who might ask a question, not questions asked. The table below applies each published rate; the usage rows assume 50 GB of source documents and 20 retrievals per employee per month.

<table style="width:100%; border-collapse:collapse; margin:1.5rem 0; font-size:0.95rem;">
  <thead>
    <tr style="background:#f5f5f0; border-bottom:2px solid #2175C5;">
      <th style="text-align:left; padding:0.75rem; color:#5f6368;">Pricing shape</th>
      <th style="text-align:right; padding:0.75rem; color:#5f6368;">1,000 employees / month</th>
      <th style="text-align:right; padding:0.75rem; color:#5f6368;">5,000 employees / month</th>
    </tr>
  </thead>
  <tbody>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Microsoft 365 Copilot</strong> ($30/user, plus the required Microsoft 365 plan)</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">$30,000</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">$150,000</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Dust Pro</strong> (€24/seat yearly; Business plan covers up to 100 people)</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">Enterprise quote</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">Enterprise quote</td>
    </tr>
    <tr style="border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>Bedrock Managed Knowledge Base</strong> (storage + retrieval, before model tokens)</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">$270</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">$350</td>
    </tr>
    <tr style="background:#f0f9ff; border-bottom:1px solid #e5e7eb;">
      <td style="padding:0.75rem;"><strong>ibl.ai, self-hosted</strong> (your infrastructure + model tokens, no per-seat fee)</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">Grows with use</td>
      <td style="text-align:right; padding:0.75rem; font-variant-numeric:tabular-nums;">Grows with use</td>
    </tr>
  </tbody>
</table>

The Bedrock rows are 50 GB × $5.00 = $250 of storage, plus 20,000 or 100,000 retrievals at $1.00 per 1,000. Model tokens come on top and depend on the model and answer length, which is exactly why a usage bill tracks the work and a seat bill does not.

The same arithmetic holds for any self-hosted option: Onyx, Dify and ibl.ai carry no per-seat fee in the software, so the cost is servers plus tokens. For a fuller per-seat comparison at scale, see [Enterprise AI with no per-seat pricing](/blog/enterprise-ai-with-no-per-seat-pricing).

## Which RAG platform should a company choose for its knowledge base?

The right RAG platform for a company knowledge base depends on how much of the stack the company wants to own.

Per Glean's documentation, even its Customer Hosted tenant does not support "altering any part of the Glean architecture," so ownership is the axis that splits the list most cleanly.

- **Choose a managed app (Glean, Microsoft 365 Copilot, Dust)** when speed matters more than control and a per-user bill is acceptable at your headcount.
- **Choose Amazon Bedrock Knowledge Bases** when you are standardized on AWS and have a team to build the application around a retrieval API.
- **Choose Onyx or Dify** for a self-hosted pilot from public source code, and read the license terms first, Dify's multi-tenant condition in particular.
- **Choose LangChain** when retrieval is one component of a product your engineers are building anyway.
- **Choose ibl.ai** when the knowledge base has to run inside your perimeter, on any model, with the source code in your hands and no per-seat bill.

Our [Fortune 500 knowledge base guide](/blog/fortune-500-ai-knowledge-base-full-control) covers the control pattern at 50,000 employees, and the [open-source AI search engine guide](/blog/best-open-source-ai-search-engines-enterprise-2026) compares Onyx, Haystack, LlamaIndex and txtai.

## How does ibl.ai run a RAG knowledge base on your own infrastructure?

ibl.ai runs a RAG knowledge base as part of [Agentic OS](/product/agentic-os): connectors bring in your systems, a knowledge graph and vector index provide policy-aware retrieval, and agents answer while citing the source they used. 1.6M+ users across 400+ organizations run the platform, including NVIDIA, MIT, and Syracuse University.

You receive source access to the connectors, policy engine and agent interfaces, so the index, the retrieval logic and the logs sit on infrastructure you control.

The platform is model-agnostic: route sensitive questions to a local model and others to Claude, GPT or Gemini, and change the routing as models improve.

Because there is no per-seat pricing, the bill does not grow when you add the next department. ibl.ai is family-owned and operated from New York, NY, and the same stack runs in your cloud, on-premise or [fully air-gapped](/service/air-gapped-ai), which our [air-gapped platform comparison](/blog/air-gapped-ai-platforms-enterprise-no-internet) covers.

## What should a buyer ask a RAG platform vendor before signing?

A buyer comparing RAG platforms for a company knowledge base should ask five questions before signing. Each one maps to a column in the tables above:

1. **Where does the vector index live, and who can reach it?** Glean's Customer Hosted docs say Glean employees can view non-PII system logs for debugging, and that blocking Glean's access entirely means Glean "can't investigate issues" ([Glean](https://docs.glean.com/troubleshooting/deployment/monitoring-debugging-access)).
2. **Do we receive the source code, and under what license?** MIT, Apache 2.0 with conditions, and proprietary are three different answers.
3. **Can we use any model, including one we host?** Onyx names Ollama, LiteLLM and vLLM; Dify supports any OpenAI-compatible model.
4. **Does the bill scale with employees or with queries?** At 5,000 employees, $30 per seat is $150,000 a month before a single question.
5. **What happens to the index and the connectors if we leave?** A platform you own has no exit to negotiate.

To size it for your own sources and headcount, [talk to the ibl.ai team](/contact).

## Why does owning the AI stack matter?

**ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.**

- **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
- **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
- **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
- **Deploy anywhere.** Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.