RAG as a Service Providers: 8 Managed Options Compared (2026)

The short answer: if your company already runs on AWS, Azure or Google Cloud, the managed RAG service of that cloud is the default first candidate, because your data, identity and billing are already there. For cited answers from a folder of files this week, OpenAI file search or Pinecone Assistant needs the least setup. For a dedicated RAG platform that can also run in your own VPC or data center, test Vectara, Ragie and Progress Agentic RAG.

All product facts below come from each vendor’s official documentation or pricing page as read in November 2026. Confirm anything that affects your decision before you commit. This list covers platforms and APIs only; for teams that build the system for you, see RAG development companies.

Quick picks

What RAG as a service actually manages

A RAG system has two halves. The indexing half turns documents into something searchable: fetch, parse, split into chunks, embed, store. The query half takes a question, retrieves the best chunks, optionally reranks them and hands them to a language model that writes an answer with citations. See our RAG architecture guide.

A managed provider runs most or all of those stages behind an API. Some return only retrieved passages and leave generation to you; some write the answer too. None decide what content is correct, who may see it or whether the answers are good enough to ship.

The eight providers in depth

Amazon Bedrock Knowledge Bases

What it manages: according to the Amazon Bedrock user guide, there are now two types. With a Managed Knowledge Base, Bedrock runs the ingestion, indexing, storage and retrieval infrastructure, with service-managed embedding and reranking models by default. It offers connectors for Amazon S3, SharePoint, Confluence, Google Drive, OneDrive and a web crawler, document-level permission filtering through access control lists at retrieval time (except for the web crawler), and agentic retrieval that breaks complex questions into sub-queries. With a Customer-managed Knowledge Base you operate the vector store yourself, such as Amazon OpenSearch Serverless or Aurora.

What you still build: the application, prompts and evaluation. On a managed knowledge base, Retrieve returns source chunks and AgenticRetrieveStream can add a generated answer with citations; the older RetrieveAndGenerate API works only with customer-managed knowledge bases.

Deployment and data location: inside your AWS account. The documentation lists nine Regions for managed knowledge bases across the US, Europe and Asia Pacific, including AWS GovCloud (US-West) with a reduced feature set.

Billing model: the Bedrock pricing page charges managed knowledge bases per GB of raw data stored per month and per 1,000 retrieval calls, with agentic retrieval at a higher rate.

Who it suits: AWS teams that want permission-aware retrieval without running a vector database.

Azure AI Search

What it manages: Microsoft’s documentation describes two approaches. Classic RAG uses indexers that pull from more than ten Azure data sources, a skills pipeline for chunking and vectorization, hybrid keyword and vector queries and semantic ranking; your application hands the results to a model itself. Agentic retrieval adds a knowledge base object: the service plans sub-queries, runs them in parallel, reranks and returns grounding data with references.

What you still build: in classic RAG, the orchestration between search and the model. In agentic retrieval, less, but check release status: according to the agentic retrieval overview, the generally available API covers minimal, extractive retrieval, while LLM-based query planning and answer synthesis are preview features that Microsoft does not recommend for production.

Deployment and data location: a search service in the Azure region you choose, with document-level security trimming, Microsoft Entra ID permission metadata and network isolation through private endpoints.

Billing model: a Dedicated model billed hourly by search units on a service tier, or a consumption-based Serverless model that is in preview. Agentic retrieval is billed by tokens on the search side, plus Azure OpenAI tokens for query planning and answer synthesis.

Who it suits: Microsoft-centric companies whose content and access rules live in SharePoint and Azure storage.

RAG Engine on Gemini Enterprise Agent Platform (Google Cloud)

What it manages: Google’s documentation now names the service RAG Engine, a component of Gemini Enterprise Agent Platform, although the docs still sit under Vertex AI addresses and use the Vertex AI SDK. It covers ingestion from local files, Cloud Storage and Google Drive, chunking, embedding, an index called a corpus (for example in a RAG-managed database that runs on Spanner), retrieval and grounded generation.

What you still build: the application, other connectors, evaluation and access rules.

Deployment and data location: you choose a Google Cloud region, but read the table carefully. The overview lists many regions as preview and says four (us-central1, us-east1, us-east4 and europe-west1) require an allowlist for new projects. It also states that customer-managed encryption keys and VPC Service Controls are supported, while data residency and Access Transparency controls are not.

Billing model: by component. The billing documentation says the default parser and fixed-size chunking are free, while your project pays for the embedding model, the RAG-managed database tier and any LLM-based parsing or reranking you turn on.

Who it suits: Google Cloud teams that accept regional availability that is still moving.

OpenAI file search

What it manages: according to OpenAI’s API documentation, file search is a hosted tool in the Responses API. You create a vector store, upload files, and the model decides when to search them and returns answers with file citations. The retrieval guide gives the defaults: chunks of 800 tokens with a 400-token overlap, both configurable, plus attribute filters and weights to balance embedding and keyword matches. A separate search endpoint returns chunks without generation.

What you still build: syncing content from your systems into vector stores, deleting what should no longer be there, and permissions, which you handle with attribute filters or separate vector stores.

Deployment and data location: OpenAI’s platform only. The data controls documentation lists regional storage for vector stores in several regions, regional processing for file search in the United States and Europe, and says data residency requires eligibility confirmed with sales. Vector stores are not eligible for zero data retention: they are kept until you delete them.

Billing model: storage across all your vector stores per GB per day after a free first gigabyte, plus tool and model usage listed on the pricing page.

Who it suits: products already on OpenAI models with simple access rules.

Pinecone Assistant

What it manages: Pinecone’s documentation calls it a managed service for RAG chat and agent applications. The assistant handles chunking, embedding and storage in a Pinecone-managed index, and answers through a chat API with references to the files it used. A context API returns snippets for your own model, and each assistant has an MCP server. Supported file types are DOCX, JSON, Markdown, PDF and plain text; chat models come from OpenAI, Anthropic and Google.

What you still build: loading files, metadata for filtering, and your application.

Deployment and data location: Pinecone’s cloud. You choose a US or EU region when you create an assistant and cannot change it later; the documentation says files are uploaded to Google Cloud Storage in the assistant’s region.

Billing model: usage units: ingestion units, chat and context tokens, and storage per GB per month. A free Starter plan includes small allowances; paid plans carry a flat monthly fee or a monthly minimum.

Who it suits: teams that want a managed assistant over files and freedom to switch LLM vendors.

Vectara

What it manages: according to Vectara’s documentation, the file upload endpoint extracts text, chunks and indexes automatically (sentence-based chunking by default, up to 10 MB per file). Queries can blend neural and keyword matching and apply rerankers. What sets it apart is the Factual Consistency Score: a hallucination evaluation model rates each generated answer from 0 to 1 for how well the retrieved results support it.

What you still build: ingestion from your sources, thresholds for what to do with low-scoring answers, and the application.

Deployment and data location: SaaS, or VPC, on-premises and air-gapped deployments in your own environment.

Billing model: annual plans per deployment type. Vectara’s pricing page shows a 30-day free trial and plans starting at $100,000 a year for SaaS, $250,000 for a VPC deployment and $500,000 for on-premises.

Who it suits: enterprises that need answer-level hallucination checks or private deployment and can fund an annual contract.

Ragie

What it manages: Ragie’s site describes a context engine that parses, chunks and indexes text, PDFs, images, audio and video, builds vector, keyword and summary indexes, and serves retrieval with hybrid search and LLM reranking. Content arrives by API upload or through prebuilt connectors such as Google Drive and Notion that keep it in sync. Its pricing page lists partitions and entity extraction on every plan.

What you still build: the application around retrieval, prompts, and the mapping of your tenants or user groups to partitions.

Deployment and data location: Ragie’s site says it can be deployed in the cloud, in your own VPC or on-premises. We did not find hosting regions on the pages we read, so ask.

Billing model: a monthly plan with a page allowance, then per-page charges (fast or high-resolution), per-minute charges for audio and video, and a fee per additional embedded connector.

Who it suits: multi-tenant SaaS features that need connectors and mixed media. For the design questions, see RAG pipeline for SaaS.

Progress Agentic RAG (formerly Nuclia)

What it manages: Progress Software acquired Nuclia, and nuclia.com now leads to Progress Agentic RAG. Per its documentation, content goes into a Knowledge Box, a container that keeps its data isolated from other Knowledge Boxes, through the REST API, SDKs, the dashboard or a Sync Agent that watches third-party storage. The find endpoint combines semantic and keyword search; the ask endpoint runs that search and passes the results to the LLM you choose.

What you still build: the application, retrieval strategy choices and evaluation.

Deployment and data location: two regions, Europe and USA, chosen when you create each Knowledge Box. The pricing page lists hybrid cloud and on-premises options on the Enterprise plan.

Billing model: a monthly subscription with limits on indexed data, plus consumption billed in the vendor’s own token unit. There is a 14-day free trial.

Who it suits: companies that want to choose EU or US data location per knowledge base.

Comparison table

What managed RAG does not solve

These five problems stay with you, and they decide whether the project works.

• Bad or stale content. If two policy documents contradict each other, the service retrieves both. Someone has to own what goes in, what gets removed and how often sources sync.
• Permissions. Only some providers enforce document-level access at retrieval time, and usually for specific sources. Everywhere else you must map users to filters, partitions or separate stores yourself. A wrong mapping leaks data through a perfectly cited answer.
• Evaluation. No provider knows what a correct answer is for your business. Without a test set of real questions and expected answers, you cannot compare vendors or catch regressions. See our RAG evaluation guide.
• Hard documents and hard questions. Scanned tables, engineering drawings, long contracts with cross-references, and questions that need figures from several documents are where default parsers and single-shot retrieval fail. Our article on RAG hallucinations explains how retrieval misses become confident wrong answers.
• Cost at scale and lock-in. Per-query and per-token billing is cheap in a pilot and can be large at production volume. Your index lives in the vendor’s format, so switching means re-ingesting everything and re-testing quality.

How to choose a RAG as a service provider

1. Fix the hard constraints firstWhere data may be stored and processed, which cloud contracts you have, whether preview features are acceptable. This usually cuts the list to two or three.
2. Build a test set before any demoCollect 50 to 100 real questions with the passage that answers each, including some whose answer is not in the documents.
3. Load the same sample into each candidateA few hundred representative files, including your worst: scans, large tables, slide decks.
4. Measure retrieval and answers separatelyIs the right passage in the retrieved set? Is the answer correct and cited? This tells you which half to fix.
5. Test permissions with two usersAsk the same question as a user who may see the source and one who may not.
6. Model one production monthApply each vendor’s billing units to your document volume, sync frequency and query count, including generation tokens.
7. Plan the exitKeep source files, metadata and the test set in your own storage, so a move is a re-ingest and a re-test.

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When to build custom instead

Managed RAG is the right default when your documents are ordinary, your access rules are simple and speed matters more than control. A custom pipeline earns its cost in five situations.

• Retrieval quality is the product. If users pay for the answers, you need control over parsing, chunking, reranking and query rewriting that managed settings do not expose.
• Your documents break default parsers. Drawings, multi-page tables and domain-specific layouts often need custom extraction before indexing.
• Permissions come from several systems. Enforcing them in your own retrieval layer is safer than bending a vendor’s filter model.
• Data cannot leave an environment no vendor supports. Air-gapped networks and strict residency rules narrow the managed list quickly. Our list of on-premise RAG development companies covers that route.
• Volume makes usage billing expensive. At high query volume, running your own vector store and open models can cost less than per-call and per-token fees, if you have people to operate it.

The trade-off runs both ways. Custom means you own ingestion jobs, index migrations, monitoring and on-call, and the first version takes weeks or months instead of days. Our RAG development cost guide breaks down those budgets. A common middle path is to start on a managed service, measure with your test set, and replace only the stage that underperforms, for example your own parser in front of a managed index. For that route, see our RAG development services.

FAQ

Where Gilzor fits

Gilzor does not sell or resell any of the platforms above. We are a software development company with a team in Poland and partial overlap with the US working day, and we work on the part the providers leave to you: we build custom RAG systems and applications, add RAG to existing products on top of a managed service or your own stack, and consult on and evaluate RAG systems so a vendor choice rests on measurements. Not sure whether managed or custom fits? A 2-week fixed-price Discovery Sprint is a contained way to decide. If retrieval quality needs tuning first, see our RAG evaluation services.