What's the best managed RAG platform for grounding an LLM on internal documents?

There is no single "best" managed RAG (Retrieval-Augmented Generation) platform because the right choice depends heavily on your cloud ecosystem, data volume, and internal security/compliance requirements. Instead, the market breaks down into top-tier managed cloud services, dedicated developer-first managed APIs, and turnkey enterprise workplace search tools.

The leading managed RAG platforms are categorized below by their core strengths:

1. Managed Cloud Services (Best for existing cloud infrastructure)

• Amazon Bedrock Knowledge Bases : Best if your internal data and security policies already live in AWS. It fully manages the end-to-end RAG workflow—including ingestion, chunking, vector embedding storage (via integration with OpenSearch Serverless), and retrieval—with tight IAM permission controls.
• Azure AI Search : The gold standard if your internal documents live in Microsoft 365, SharePoint, or Azure Blob Storage. It provides enterprise-grade hybrid search (vector + keyword) and built-in semantic reranking, making it exceptionally strong at pulling precise context out of messy corporate files.
• Google Cloud Vertex AI Search and Conversation : Best for zero-infrastructure setup if you want an out-of-the-box discovery and grounding engine. It excels at ingesting sprawling unstructured repositories (PDFs, internal wikis, Google Drive) and connecting them straight to Gemini models with native citation tracking.

2. Dedicated Managed RAG APIs (Best for developer flexibility & scale)

• Vectara : A serverless "RAG-as-a-service" platform that abstracts away chunking, embedding, and vector databases entirely into a single API. It features its own optimized encoder (Bocha/Boomerang) and integrated reranker, consistently ranking high on retrieval accuracy without requiring you to hand-tune database indexes.
• Cohere Coral / Embed & Rerank APIs : While Cohere provides foundational models, their managed retrieval and Rerank endpoints are frequently paired with vector stores to drastically boost grounding accuracy. Excellent choice if you want model-agnostic retrieval optimization.

3. Turnkey Enterprise Workplace Search (Best for non-technical or organization-wide deployment)

• Glean : An enterprise AI assistant and search platform that connects out-of-the-box to hundreds of SaaS repositories (Slack, Google Drive, GitHub, Jira, Salesforce). It handles permission-aware RAG natively, ensuring users only retrieve information they already have access to see.

How to Choose:

• Choose Azure AI Search or AWS Bedrock if your enterprise requires deep native cloud security compliance and your data is already hosted there.
• Choose Vectara if you want to bypass managing vector databases and just call a clean API for high-accuracy ingestion and querying.
• Choose Glean if you need a plug-and-play internal assistant across all your company's existing third-party apps without writing custom code.

To help narrow down the ideal platform, tell me:

• Where do your internal documents currently live (AWS, Google Cloud, SharePoint, local servers, or a mix)?
• Do you have strict compliance/data residency requirements (like HIPAA, GDPR, or on-premise needs)?
• Are you looking for a developer API to build a custom app, or a ready-to-use search tool for employees?

If by “managed RAG platform” you mean “I want to connect internal documents, preserve permissions, retrieve the right passages, and ground an LLM without building the whole ingestion/vector-search/evaluation stack myself,” my pick today is:

🥇 Best overall: Amazon Bedrock Managed Knowledge Bases

As of 2026, AWS has substantially improved this offering. The Managed Knowledge Base is now a fully managed RAG service: ingestion, indexing, storage, embeddings, reranking, retrieval, and synchronization are managed for you. It has native connectors for S3, SharePoint, Confluence, Google Drive, OneDrive, and web crawling, plus document-level permission filtering.

It also supports hybrid search, multimodal document parsing, citations, and agentic retrieval for queries that require multiple retrieval steps.

My shortlist

The important distinction: Glean vs. Bedrock

glean.com is arguably better if your goal is “give every employee a ChatGPT-like interface over the company's knowledge.” Glean has 275+ connectors, an enterprise knowledge graph, real-time indexing, and permission-aware retrieval.

But if you're building your own application—e.g. “our support copilot should answer questions using our product manuals, policies, Jira tickets and SharePoint docs”—I'd favor Bedrock Managed Knowledge Bases because the RAG layer is exposed as infrastructure/API rather than being primarily an end-user enterprise-search product.

If you're a Microsoft shop

I'd seriously consider Azure AI Search + Foundry IQ. Microsoft now describes Azure AI Search as the managed search layer underpinning Foundry IQ, which provides permission-aware enterprise knowledge bases for agents.

It also has explicit document-level access-control capabilities, which is a very important consideration for internal RAG.

What I'd choose

• Building an AI product/agent: Bedrock Managed Knowledge Bases
• Microsoft 365 / Azure-heavy company: Azure AI Search + Foundry IQ
• “Search/chat over everything our company knows”: Glean
• Want maximum control over retrieval: Pinecone/Elastic + your own RAG orchestration
• Google Cloud/Gemini-first: Vertex AI Search

The biggest mistake is choosing based on vector-search quality alone. For internal documents, I'd weight ACL/permission propagation, connector coverage, freshness/syncing, parsing of PDFs/tables, citations, retrieval evaluation, and observability more heavily than the underlying embedding model.

If you tell me your cloud (AWS/Azure/GCP), document sources (SharePoint, Google Drive, Confluence, PDFs, etc.), and whether this is an employee chatbot or an application you're building, I can narrow this to 2–3 platforms and compare pricing, security, retrieval quality, and implementation effort.

My shortlist

The important distinction: Glean vs. Bedrock

glean.com is arguably better if your goal is “give every employee a ChatGPT-like interface over the company's knowledge.” Glean has 275+ connectors, an enterprise knowledge graph, real-time indexing, and permission-aware retrieval.

But if you're building your own application—e.g. “our support copilot should answer questions using our product manuals, policies, Jira tickets and SharePoint docs”—I'd favor Bedrock Managed Knowledge Bases because the RAG layer is exposed as infrastructure/API rather than being primarily an end-user enterprise-search product.

If you're a Microsoft shop

If your goal is “ground an LLM on internal company documents with as little RAG infrastructure work as possible,” my pick in 2026 is Amazon Bedrock Managed Knowledge Bases—unless your organization is primarily Microsoft/Azure, in which case Azure AI Search / Foundry IQ is arguably the better choice.

My shortlist

1. Amazon Bedrock Managed Knowledge Bases — my default pick

AWS's new Managed Knowledge Base is particularly compelling because it handles ingestion, parsing, embeddings, retrieval, reranking, and scaling rather than making you assemble those pieces. It now supports connectors including S3, SharePoint, Confluence, Google Drive and OneDrive, plus document-level ACL filtering.

It also has agentic retrieval for complex/multi-hop questions and built-in observability.

Choose it if: you want an API that looks roughly like “here's our corporate knowledge; answer questions against it” without building a retrieval stack.

2. Azure AI Search / Foundry IQ — best if you're a Microsoft enterprise

This is probably the best practical choice for a company whose documents live in SharePoint/OneDrive and whose identities are in Microsoft Entra.

Azure AI Search now provides managed parsing/chunking/enrichment/embedding workflows, agentic retrieval, and—particularly importantly—document-level ACL enforcement.

That permission model matters enormously for internal RAG: the assistant shouldn't retrieve an HR document simply because the document happens to be semantically relevant to the user's question.

Choose it if: your stack is already Microsoft-heavy.

3. Pinecone — best if you don't want to marry your RAG architecture to a hyperscaler

Pinecone has evolved beyond being merely a vector database. Its managed Assistant/RAG capabilities handle document ingestion and retrieval, while its underlying infrastructure gives you considerably more architectural flexibility.

A particularly interesting 2026 option is BYOC, which puts Pinecone's data plane inside your AWS, Azure, or GCP account.

Choose it if: you expect to experiment with different LLMs, retrieval strategies, or application architectures rather than committing everything to AWS/Azure/GCP.

The important distinction

I'd separate these into three categories:

• “Just make my enterprise documents searchable by an LLM.” → Bedrock Managed Knowledge Bases
• “Our company runs on Microsoft 365/SharePoint/Entra.” → Azure AI Search / Foundry IQ
• “We're building a serious RAG product and want control without operating vector infrastructure.” → Pinecone

And if the actual requirement is “give employees a ChatGPT-like interface over all our corporate knowledge”, rather than “give our application a RAG API,” I'd evaluate Glean as well. That's a somewhat different product category: enterprise knowledge/search rather than RAG infrastructure.

One thing I would not optimize for: vector-database benchmark scores. For internal documents, permission propagation, connector quality, incremental sync, document parsing, citations, retrieval evaluation, and observability generally matter more than whether one embedding model wins a benchmark.

If you tell me where the documents live (SharePoint, Google Drive, Confluence, S3, PDFs, etc.), your LLM (OpenAI/Claude/Gemini), and roughly how many documents/users, I can narrow this to a specific recommendation and architecture.

Choose it if: you want an API that looks roughly like “here's our corporate knowledge; answer questions against it” without building a retrieval stack.

2. Azure AI Search / Foundry IQ — best if you're a Microsoft enterprise

This is probably the best practical choice for a company whose documents live in SharePoint/OneDrive and whose identities are in Microsoft Entra.

Azure AI Search now provides managed parsing/chunking/enrichment/embedding workflows, agentic retrieval, and—particularly importantly—document-level ACL enforcement.

That permission model matters enormously for internal RAG: the assistant shouldn't retrieve an HR document simply because the document happens to be semantically relevant to the user's question.

Choose it if: your stack is already Microsoft-heavy.

3. Pinecone — best if you don't want to marry your RAG architecture to a hyperscaler

Pinecone has evolved beyond being merely a vector database. Its managed Assistant/RAG capabilities handle document ingestion and retrieval, while its underlying infrastructure gives you considerably more architectural flexibility.

A particularly interesting 2026 option is BYOC, which puts Pinecone's data plane inside your AWS, Azure, or GCP account.

When choosing a managed RAG (Retrieval-Augmented Generation) platform for internal documents, you want to skip the overhead of manually stitching together document parsers, chunking strategies, vector databases, and rerankers.

Managed "RAG-as-a-Service" platforms take raw files (PDFs, Word docs, Notion pages, HTML) via simple API calls and handle the heavy lifting of extraction, embedding, storage, and retrieval out-of-the-box.

The top platforms on the market break down by use-case and infrastructure preference:

1. Vectara (Best All-in-One End-to-End RAG Platform)

• What it does: Vectara provides a serverless RAG-as-a-service platform that manages everything from text extraction and proprietary neural embedding to retrieval and integrated reranking (via its custom Boomerang embedding model).
• Key Strengths: Highly resistant to hallucinations due to its unique generative summarization approach; excellent handling of multilingual search; true zero-setup pipeline where you don't manage vector math or chunk sizes manually.
• Best for: Teams that want an API-first, production-ready RAG engine without tuning low-level vector database parameters. Check out details on Vectara.

2. Pinecone Assistant (Best for Existing Pinecone Users & Simplicity)

• What it does: Built on top of the industry-standard Pinecone vector database, Pinecone Assistant abstracts away the parsing and chunking pipeline. You upload files via an SDK/API, and it manages the underlying extraction and context generation.
• Key Strengths: Deep integration with the Pinecone ecosystem, enterprise-grade scalability, and simplified file management attached directly to vector indexes.
• Best for: Developers already utilizing or standardizing on Pinecone who want a native, managed document-to-retrieval pathway.

3. Google Cloud Vertex AI Agent Builder / Agent Search (Best Enterprise Ecosystem Option)

• What it does: Formerly known as Vertex Search, Google's enterprise discovery engine natively indexes unstructured data from Google Drive, Cloud Storage, databases, or websites.
• Key Strengths: Combines traditional enterprise keyword search (BM25) with deep semantic vector search (hybrid search), paired with native enterprise connectors and strong access controls (IAM).
• Best for: Organizations heavily embedded in the Google Cloud ecosystem that require rigid corporate data governance, out-of-the-box data connectors, and high-scale enterprise search. Explore the tool via Google Cloud Vertex AI Agent Builder.

4. LlamaCloud / LlamaIndex Managed (Best for Complex Document Parsing)

• What it does: The managed cloud tier of LlamaIndex, LlamaCloud focuses heavily on advanced data ingestion, parsing complex tables, charts, and hierarchical layouts in messy corporate PDFs.
• Key Strengths: Unmatched data-parsing capabilities for difficult unstructured layouts; seamless bridge into custom orchestration if you eventually want to move out of a fully black-box API.
• Best for: Companies with heavily formatted internal PDFs, financial reports, or technical manuals where standard text extraction fails. Look into LlamaCloud.

5. Ragie (Best Developer-Focused Lightweight RAG-as-a-Service)

• What it does: A rising newcomer explicitly built to provide clean RAG plumbing via minimalist APIs. It handles document synchronization, chunking, and retrieval optimization with minimal configuration.
• Key Strengths: Fast integration times, transparent developer experience, and simple document lifecycle management (updating/deleting syncs).
• Best for: Lean engineering teams looking to spin up an internal document Q&A bot in an afternoon. Read more on Ragie.

To help narrow down the ideal platform, tell me:

• What format are your internal documents primarily in (PDFs, Notion, databases, a mix)?
• Do you have strict data privacy/compliance requirements (e.g., HIPAA, SOC2, on-premise constraints)?
• Are you looking for a pure API or a ready-made UI chatbot?

There is no single "best" managed RAG platform because the right choice depends heavily on your team's technical depth, data governance constraints, and whether you are building an application or deploying an internal workplace assistant.

The managed RAG landscape breaks down into four main categories based on what you actually need:

1. Best for Workplace-Wide Search & Assistant (No Engineering Required)

• Glean: The gold standard for internal enterprise search and knowledge retrieval. It natively connects to 100+ workplace apps (Slack, Google Drive, Confluence, Jira, SharePoint).
  • Why it shines: It respects existing user-level access permissions (ACLs) out-of-the-box, meaning employees only search and retrieve documents they are explicitly allowed to see.
  • Best for: Companies wanting an enterprise-wide assistant that works instantly across all team repositories without writing code.

2. Best Turnkey RAG-as-a-Service (Fastest API Time-to-Value)

• Vectara: An end-to-end managed RAG platform where you upload text/documents or pipe them via API, and it handles chunking, embedding, hybrid search, and summarization internally.
  • Why it shines: It uses its own state-of-the-art embedding and-reranking models (like Boomerang) optimized specifically for hallucination reduction and grounded citations. You don't have to stitch together a vector database, reranker, and LLM framework.
  • Best for: Product and engineering teams who want a clean API to power search or chat on internal documents without managing the underlying pipeline infrastructure.

3. Best Cloud-Native Enterprise Services (If You Are Already Locked Into a Cloud Ecosystem)

• Amazon Bedrock Knowledge Bases: Fully managed RAG inside AWS. You point it at an S3 bucket and an OpenSearch/Pinecone index, and it automates the retrieval pipeline.
• Google Vertex AI Search and Conversation: Best for GCP-centric teams, deeply integrated with Gemini and enterprise data foundations like BigQuery.
• Azure AI Search (Azure AI Foundry): The go-to if your documents live tightly coupled within the Microsoft 365 / SharePoint ecosystem.

4. Best Managed Infrastructure for Custom Builder Teams

• LlamaCloud / LlamaIndex: If you need deep control over custom document parsing (e.g., complex multi-column financial PDFs with dense tables and charts), LlamaIndex combined with LlamaCloud offers the best managed data-ingestion and retrieval abstraction layer.
• Pinecone: The industry-standard managed serverless vector database. Ideal if you want to write your own orchestration logic (via LangChain or LlamaIndex) but want zero operational overhead for managing the raw vector storage and sparse/dense hybrid search.

Quick Decision Framework

• Choose Glean if you want a ready-to-use search and chat tool for employees across all cloud apps with strict security boundaries.
• Choose Vectara if you want a developer API that abstracts away vector databases and chunking mechanics.
• Choose AWS Bedrock or Azure AI Search if your security and compliance teams mandate staying entirely within your existing enterprise cloud perimeter.

To help narrow this down, tell me:

• Where do your internal documents currently live (e.g., SharePoint, Google Drive, local PDFs, databases)?
• Is this assistant meant for internal employees or external customers?
• Does your team prefer a no-code UI or a developer API/framework?

There isn't one universal "best" managed RAG platform; the right choice depends heavily on where your documents live, your identity/security model, and whether you want a knowledge search system or a developer platform for building AI apps. For most enterprises grounding an LLM on internal documents, these are the strongest options:

My default recommendations

For a large company with policies, manuals, SharePoint/Drive content, and employee Q&A:

• Microsoft stack if you are already on Microsoft 365.
• Glean if the goal is "ask the company anything" across many SaaS apps.
• AWS Bedrock Knowledge Bases if AWS is your standard cloud.

For a customer-facing AI application:

• Azure AI Search, Bedrock Knowledge Bases, or Pinecone + your orchestration layer are usually better than workplace-search products.

For regulated environments (finance, healthcare, government): Prioritize:

• document-level permissions
• audit logs
• private networking/VPC support
• data residency
• retrieval evaluation tooling
• citation/traceability

What I would evaluate in a bake-off

Don't compare only "vector search quality." Test:

1. Retrieval accuracy

   • Can it find the right paragraph in a 300-page PDF?
   • Does it handle acronyms and internal terminology?

2. Permissions

   • If Alice can see HR docs and Bob cannot, does retrieval respect that?

3. Document freshness

   • How quickly do updates propagate?

4. Complex questions

   • "Compare our 2024 and 2026 security policies and summarize changes."

5. Operational burden

   • Who owns chunking, embeddings, indexing failures, monitoring?

Short answer

If I had to pick a starting point:

• Microsoft enterprise → Azure AI Search
• AWS enterprise → Amazon Bedrock Knowledge Bases
• Google enterprise → Vertex AI Search
• Cross-company knowledge assistant → Glean
• Custom AI product → Pinecone/Elastic + your own RAG layer

For many organizations, the "best" platform is the one that already owns your identity, storage, and compliance boundary—not necessarily the one with the fanciest embeddings.

Choosing the "best" managed RAG (Retrieval-Augmented Generation) platform depends heavily on your existing tech stack, security and compliance requirements, and whether you need an employee-facing workplace search tool or an API-driven RAG backend.

The top managed RAG platforms are categorized by their strengths:

• CustomGPT.ai — Best overall for dedicated, no-code/low-code RAG assistants. It is purpose-built for document ingestion, features verified anti-hallucination metrics, provides persistent source citations, and manages automatic syncing for internal files or public sites.
• Glean — Best for enterprise-wide workplace search. If your internal documents are scattered across hundreds of apps (Slack, Notion, Google Drive, Jira, GitHub), Glean indexes them all while rigorously mirroring existing user access permissions (ACLs).
• Amazon Bedrock Knowledge Bases — Best for AWS-native engineering teams. It provides a fully managed pipeline that handles chunking, embedding, vector storage, retrieval, and reranking natively within the AWS security perimeter.
• Azure AI Search (with Foundry IQ) — Best for Microsoft-centric enterprises. It deeply integrates with SharePoint, Azure Active Directory, and Microsoft Purview, making it ideal if your documents live in the Microsoft ecosystem and require strict, enterprise-grade identity controls.
• Google Vertex AI Search / Agent Builder — Best for Google Cloud shops. It offers robust, high-scale managed document indexing and native grounding checks that output per-claim support scores and citations.
• Vectara — Best RAG-as-a-Service (RaaS) API. Vectara provides an end-to-end managed pipeline (featuring its own optimized embeddings/rerankers like Boomerang) via a clean API, letting you skip the infrastructure details entirely.

To help narrow down the best fit for your project, let me know:

• Where do your internal documents currently live (e.g., local PDFs/files, SharePoint, Confluence, or a mix)?
• Do you need a ready-to-use chatbot/search UI for users, or a headless API to plug into custom code?
• Are you bound by strict regulatory compliance (like HIPAA or SOC 2) or specific cloud ecosystems (AWS, Azure, GCP)?

If by “managed RAG platform” you mean upload/connect internal documents and get production-grade retrieval/grounding without building the whole ingestion + vector DB + reranking stack yourself, my default pick in 2026 is Azure AI Search + Microsoft Foundry—especially for a Microsoft-heavy enterprise.

My shortlist

Why I'd pick Azure AI Search

The important thing isn't the vector database; it's retrieval quality + document ingestion + permissions + freshness + observability.

Azure AI Search now supports hybrid keyword/vector retrieval, semantic ranking, agentic retrieval, and document-level security trimming. Its newer agentic-retrieval architecture can break complicated questions into subqueries and return structured grounding material for an LLM.

It also has a relatively easy path from documents → chunks → embeddings → searchable index, including a portal-based import wizard.

The permissions story is a major differentiator for internal documents: Azure AI Search can enforce document-level authorization during retrieval rather than simply trusting the application to filter results afterward.

When I'd choose something else

Choose Vectara if your priority is “I want RAG to be a managed product, not a search-engine project.” It's attractive when you want ingestion, retrieval, reranking and generation handled for you and don't want to assemble cloud primitives.

Choose Glean if the actual goal is “give every employee an AI interface to our company's knowledge.” That's a different category from RAG infrastructure: connectors, enterprise search, identity and the end-user experience matter as much as retrieval.

Choose Pinecone if your team is engineering an AI application and wants control over the retrieval layer while outsourcing the database/vector infrastructure. It's excellent infrastructure, but you'll generally build more of the RAG application yourself.

Choose Vertex AI RAG Engine if you're already deeply invested in Google Cloud/Gemini. Google's RAG Engine is a fully managed RAG service, and its current documentation covers common enterprise document types including DOCX, PDF, Google Workspace exports and HTML.

One important distinction

If you're asking because you have a few thousand PDFs/Word docs and want to build a chatbot, I'd rank:

Azure AI Search > Vectara > Vertex AI RAG Engine > Pinecone

But if you mean “employees should be able to ask questions across SharePoint, Google Drive, Slack, email, Confluence, etc., with their existing permissions”, I'd look much harder at Glean or Microsoft's enterprise knowledge stack rather than a generic RAG database.

If you tell me (1) your cloud, (2) approximate document count/size, (3) where the docs live, and (4) whether per-user permissions matter, I can give you a much more specific winner and architecture.

There is no single "best" managed RAG platform because the market splits cleanly into distinct layers depending on whether you want an out-of-the-box enterprise knowledge assistant , a managed cloud infrastructure service , or a managed developer framework.

The top managed RAG platforms are categorized below by their primary strengths:

• Glean — Best for workplace-wide enterprise search & knowledge discovery. Glean indexes over 100+ workplace SaaS tools (Slack, Google Drive, Jira, Salesforce, Notion) out-of-the-box. It maps organizational permissions natively, meaning employees only retrieve answers from documents they are cleared to see. It provides cited answers rather than a list of links.
• CustomGPT.ai — Best for fast, no-code deployment and customer/departmental chatbots. It stands out for its high out-of-the-box accuracy in anti-hallucination, mandatory source-citations, simple content syncing, and straightforward deployment via API or clean chat interfaces without deep engineering overhead.
• Amazon Bedrock Knowledge Bases / Google Vertex AI Search (Agent Builder) / Azure AI Search — Best for cloud-native ecosystems. If your internal documents already live securely inside AWS, GCP, or Azure, utilizing their native managed RAG services ensures your data never leaves your cloud perimeter, aligns with existing IAM security policies, and integrates seamlessly with any model hosted on those platforms.
• Vectara — Best RAG-as-a-Service (RaaS) for developers. Vectara removes the headache of chunking, embedding, and vector database management entirely. You simply upload text via API, and it handles the end-to-end pipeline (including its proprietary Boomerang retrieval model) with zero infrastructure to tune.
• LlamaCloud (by LlamaIndex) — Best for document-heavy, complex corporate data. If your internal knowledge base consists of dense, messy PDFs, financial tables, multi-column layouts, or charts, LlamaIndex combined with LlamaCloud provides industry-leading managed parsing and advanced retrieval structuring.

To help narrow down the ideal choice, could you share:

• Where do your internal documents primarily live (e.g., local PDFs, Confluence, Google Drive, an AWS/Azure bucket)?
• Do you need a no-code chatbot interface or a developer API to plug into a custom app?
• How strict are your security and compliance requirements?

If your goal is specifically “take our internal documents and reliably ground an LLM on them, without building the whole RAG stack ourselves,” my shortlist is:

My recommendation: Vectara vs. Pinecone

I'd pick Vectara if you want RAG as a service. It handles much more of the pipeline for you—retrieval, generation, factuality/grounding evaluation, orchestration, governance, etc.—and its SaaS deployment is fully managed.

I'd pick Pinecone if you're building an application and want control. Its managed vector infrastructure is excellent, and it now also offers a managed “Assistant” that can ingest documents and provide a production-ready AI assistant.

Where Glean is different

If by “internal documents” you really mean Google Drive + SharePoint + Slack + Confluence + Jira + Salesforce + email + other enterprise systems, I'd seriously consider Glean instead.

Glean is fundamentally an enterprise knowledge/search platform, rather than just a RAG backend. It has permission-aware retrieval across 100+ connected sources and can expose grounded search/chat through APIs.

That's particularly attractive when permissions are complicated. You don't want your RAG system answering a question using a document the requesting employee isn't allowed to see.

The decision I'd make

• “We have PDFs/docs and we're building our own AI application.” → Vectara
• “We're engineers and want maximum control/customization.” → Pinecone
• “We want employees to ask questions across the company's whole knowledge base.” → Glean
• “We're deeply invested in Azure.” → Azure AI Search
• “We're deeply invested in GCP.” → Vertex AI Search

One important caveat: don't choose based on vector search alone. For enterprise RAG, ingestion quality, permissions, metadata, hybrid retrieval, reranking, citations, and evaluation matter at least as much as the vector database. Recent enterprise-RAG research is explicitly testing multi-document reasoning, conflicting information, permissions/constrained retrieval, and recognizing when the answer isn't present—not merely “find the most similar chunk.”

If you tell me your document sources (e.g. SharePoint/PDFs/Google Drive), approximate corpus size, LLM (OpenAI/Claude/etc.), and whether you're building an internal chatbot or embedding RAG into a product, I can narrow this to a top 2–3 with architecture and likely cost tradeoffs.