Top Enterprise RAG & Agentic Retrieval Platforms In 2027

Enterprise RAG has moved past the demo stage. In 2026 the conversation shifted from “can a chatbot answer questions about our documents” to “can an agent plan a multi-step search, respect document permissions, cite its sources and do it fast enough for thousands of users at once.” The cloud providers renamed and rebuilt their search products around agents, specialist vendors added agent harnesses and MCP servers, and the infrastructure underneath got a lot more attention from the teams who have to keep it running.

That shift matters because retrieval quality now sets the ceiling on answer quality. A frontier model reading the wrong five passages still gives a wrong answer. Agentic RAG makes the problem sharper: an agent may issue several queries per user request, filter on structured metadata, re-rank results and loop back for more context, so latency and relevance compound across every step.

This guide covers the platforms enterprise teams are shortlisting for 2027. Some are retrieval engines you build on, some are fully managed RAG as a service, and some are complete enterprise AI search products with agents on top. Each entry was checked against the vendor’s own website, and we focus on what each one actually ships: how it retrieves, where it can be deployed, and who it suits best.

Enterprise RAG Platforms At A Glance

Platform	Type	Retrieval Approach	Deployment	Best For
Vespa	AI search and retrieval engine	Hybrid text, vector and tensor search with multi-phase ML ranking	Vespa Cloud, Vespa Cloud Enclave (AWS, Azure, GCP), open source self-hosted	Large-scale, real-time RAG and agentic retrieval with custom ranking
Vectara	Enterprise agent platform	Hybrid retrieval, re-ranking, hallucination detection	SaaS, VPC, on-prem, air-gapped	Regulated and technical industries needing grounded agents
Glean	Enterprise AI search and agents	Permission-aware search across 250+ connectors	SaaS	Company-wide knowledge search and employee agents
Contextual AI	Context engineering platform	Agent retrieval with sentence-level citations	Multi-tenant SaaS, single-tenant SaaS, private VPC	Agents over technical documentation
Cohere	Models plus agentic platform	Embed and Rerank models, North and Compass	SaaS, private VPC, on-prem	Private deployments with sovereign data needs
Azure AI Search (Foundry IQ)	Cloud search and knowledge bases	Agentic retrieval, hybrid search with RRF	Microsoft Azure	Microsoft-centric enterprises
Amazon Bedrock Knowledge Bases	Managed RAG service	Managed vector store, re-ranking, agentic retriever	AWS	AWS-native agent builds
Google Agent Search (formerly Vertex AI Search)	Managed retrieval and grounding	Google-quality search, grounding APIs, Vector Search	Google Cloud	Google Cloud customers and site search
Pinecone Assistant	Managed RAG API	Automatic chunking, embedding, retrieval and re-ranking	Pinecone cloud	Fast document Q&A without pipeline work
Weaviate	Open source vector platform	Vector, keyword and hybrid search, Query Agent	Weaviate Cloud, self-hosted	Teams wanting an open source vector layer with agents
LlamaIndex	Framework plus document cloud	LlamaParse parsing, indexing pipelines, agents	LlamaCloud, customer VPC	Complex documents and agent workflows
Ragie	RAG as a service	Vector, keyword and summary indexes, agentic retrieval	Cloud, customer VPC, on-prem	Product teams embedding RAG for their own customers

Best Enterprise RAG And Agentic Retrieval Platforms In 2027

1. Vespa: The AI Search Platform For Production RAG At Any Scale

Vespa is an AI search platform that enterprises use as the retrieval layer beneath RAG applications and agents. It runs hybrid search, machine-learned ranking and model inference in one engine, instead of spreading them across a vector database, a keyword index and a reranking service. It suits teams whose prototype has outgrown its stack: corpora that change constantly, agents that fire several queries per request, and answers whose quality depends on which passages reach the model. Vespa stays model-agnostic, connecting to OpenAI, Anthropic and Gemini models and to LangChain, LlamaIndex and Haystack through standard APIs.

For agentic workloads, one Vespa query can combine dense embeddings, keyword signals and structured metadata filters, so an agent can search documents and structured records together without calling two systems. Vespa computes on tensors rather than only single vectors, which supports multiple embeddings per document for ColBERT- and ColPali-style retrieval, including visual retrieval over page images of scanned or layout-heavy documents. Ranking runs in phases: a fast first phase recalls candidates, then heavier ONNX, XGBoost or custom models re-rank only those inside the engine, so custom ranking models go live without a separate model-serving tier. Ingestion is continuous with no index rebuilds, and Vespa cites billions of constantly changing items, thousands of queries per second and latencies under 100 milliseconds; for per-user and private data, its streaming search mode is, by Vespa’s account, 20x cheaper than indexing.

To shorten the step from prototype to production, Vespa’s RAG Blueprint, launched on Vespa Cloud in July 2025, packages hybrid search, phased ranking and modular retrieval pipelines into a template for production applications, and Vespa Cloud itself is offered as a managed service for production RAG. Organizations with residency or control requirements can run Vespa Cloud Enclave inside their own AWS, Azure or GCP account, or self-host the open source engine from GitHub.

The references lean towards AI-native and high-traffic consumer companies. Perplexity used Vespa in its AI search infrastructure because it needed keyword and semantic retrieval, structured filtering, passage ranking and continuous indexing in one engine, and it still runs Vespa for other use cases. Elicit, Spotify, Vinted, Qwant, Kleinanzeigen, DeviantArt and RavenPack Bigdata.com, which runs billion-scale vector search over financial documents, also appear in Vespa’s case studies. Behind them is a long record at Yahoo, where around 150 applications ran on Vespa when it was spun out as the Trondheim-based Vespa.ai in October 2023; Yahoo remains its largest customer and an investor. If you are also comparing engines, our best vector databases guide features Vespa too.

2. Vectara: Grounded Enterprise Agents With Flexible Deployment

Vectara now presents itself as an enterprise agent platform built around a simple promise: unify data, deliver context and trust every answer. The platform handles ingest and multimodal parsing with metadata auto-tagging, then retrieval and grounding through hybrid search, re-ranking and built-in hallucination detection and evaluations. On top sits an agent harness with skills, tools, MCP support, memory and sub-agent workflows.

Deployment flexibility is a real differentiator. Vectara runs as fully managed SaaS, inside a VPC on AWS, Azure or GCP, on-prem in your data center, or fully air-gapped, with SSO, entitlements, audit logging and tenant management in every option. Teams can use Vectara’s own domain model or bring Claude, GPT, Gemini, Llama, Mistral, Nemotron or Gemma.

The company leans into technical verticals, especially semiconductors and manufacturing. Broadcom has standardized on Vectara as its enterprise agentic AI platform and reports a 30% reduction in failure analysis time, while Altera went from proof of concept to production in three months. Gartner lists Vectara as a Representative Vendor in Enterprise AI Search.

3. Glean: Permission-Aware Enterprise AI Search For The Whole Company

Glean is the best-known name in enterprise AI search for employees. It connects to more than 250 workplace tools and builds what it calls Enterprise Context, so its Assistant and search results draw on the documents, tickets and conversations a company actually runs on.

Permissions are enforced from the start: users only see what they are already allowed to see.

Glean Agents add a builder, an agent library and governance controls, and Glean Intelligence handles model routing across more than 40 LLMs. The compliance list is long, covering SOC 2 Type II, ISO 27001, ISO 42001, HIPAA, TX-RAMP Level 2 and GDPR. Named customers include Booking.com, Zillow, T-Mobile, Databricks, Ericsson and DBS.

Glean suits organizations that want a finished product for internal knowledge rather than a retrieval engine to build on. It is less of a fit when the RAG application is customer-facing or needs custom ranking logic.

4. Contextual AI: Context Engineering For Expert Agents

Contextual AI calls its product the context layer for expert AI. It targets agents that work over dense technical documentation and institutional knowledge, the kind of material where generic chunk-and-embed pipelines tend to fall apart. Teams can start from a prebuilt agent, describe one in natural language, assemble it in a drag-and-drop composer or build directly against the component APIs.

Outputs are designed to be verifiable, with sentence-level citations and visual bounding boxes pointing back to the source, and deterministic workflow steps can be added where an agent should not improvise. Document entitlements are inherited at query time, and role-based access control is built in.

Deployment runs as multi-tenant SaaS, single-tenant SaaS or a private VPC, with SOC 2, HIPAA, GDPR and CCPA coverage and no training on customer data. Qualcomm and ShipBob are named customers on its site.

5. Cohere: Retrieval Models And A Private Agentic Platform

Cohere is often already inside enterprise RAG stacks through its Embed and Rerank models, both recently updated to new versions. Many teams that build their own retrieval use Rerank as the quality step between first-pass search and generation. Cohere also offers Parse for document parsing and Command for generation.

For organizations that want a complete product, North is Cohere’s agentic enterprise AI platform, deployable on the customer’s own terms, and Compass covers intelligent search and discovery. Private deployments run on-premises or in isolated VPCs, and Model Vault provides dedicated, isolated inference. Trust badges include SOC 2, ISO 27001, GDPR and CCPA.

Customers and partners named on its site include Oracle, Dell Technologies, RBC, TD Bank, SAP, Salesforce and Fujitsu. CoreWeave used North to rebuild its customer support in 90 days. Cohere is a strong pick when data sovereignty rules out sending documents to a shared cloud model.

6. Azure AI Search (Foundry IQ): Agentic Retrieval For Microsoft Shops

Microsoft has rebranded Azure AI Search as Foundry IQ, with existing deployments, APIs and pricing unchanged. The headline addition is agentic retrieval: query planning, iterative steering, re-ranking and answer synthesis handled by the service, so an agent can break a complex question into sub-queries without custom orchestration code.

Underneath, hybrid search runs text and vector queries in one request and merges them with Reciprocal Rank Fusion, using HNSW or exhaustive KNN for vectors and BM25 for full text. Foundry IQ knowledge bases can be reused across agents and connect to SharePoint, OneLake, Azure Blob Storage, MCP sources and the web. A data import wizard automates parsing, chunking, enrichment and embedding.

Security is a strong point for enterprise RAG, with document-level ACL synchronization and query-time trimming based on each user’s permissions. For companies already standardized on Microsoft 365 and Azure, it is usually the default to beat.

7. Amazon Bedrock Knowledge Bases: Managed RAG Inside AWS

Amazon Bedrock Knowledge Bases is AWS’s fully managed RAG service. AWS manages the vector store, embedding and re-ranking models, and connectors pulled from Amazon S3, SharePoint, Confluence, Google Drive, OneDrive and a web crawler. Smart parsing picks a strategy per data type and handles text, images, audio, video and multiple languages.

The optional Agentic Retrieval feature identifies intent and relevant sources, then iterates retrieval behind a single API call, priced at a flat rate per query. Knowledge Bases plugs natively into Amazon Bedrock AgentCore with retrieval traces and session memory, works with MCP and can serve as a retriever for Strands Agents, LangChain, CrewAI and LlamaIndex.

Teams that need full control can bring their own vector stores and models. It is a natural fit for AWS-native organizations that want RAG without running search infrastructure themselves.

8. Google Agent Search: Google-Quality Retrieval And Grounding APIs

Google’s product formerly known as Vertex AI Search is now Agent Search on Gemini Enterprise Agent Platform. It brings Google Search-quality retrieval to enterprise data and serves two jobs: search experiences for intranets and public websites, and out-of-the-box grounding for generative AI apps. For RAG it manages ETL, OCR, chunking, embedding, indexing, retrieval and summarization.

Teams that prefer to assemble their own pipeline can use the DIY grounding APIs, which include the Document AI Layout Parser, a Ranking API, a grounded generation API and a check grounding API, plus Vector Search as a standalone vector database. Read-only connectors cover Jira, Confluence and Salesforce, and generative answers come with citations.

Compliance coverage includes HIPAA, ISO 27000-series and SOC 1, 2 and 3, with VPC Service Controls to prevent data exfiltration. It suits Google Cloud customers and anyone who wants a managed website search with generative answers.

9. Pinecone Assistant: The Fastest Path To Document Q&A

Pinecone Assistant sits on top of Pinecone’s vector database and removes most of the RAG pipeline work. You upload files to an assistant, and Pinecone handles chunking, embedding, retrieval and re-ranking, then answers questions about the content through an API. Files stay private to each assistant and can be removed at any time.

Developers can create an assistant in a few lines of Python with custom instructions and a region setting, and no-code builders can connect it through tools such as the n8n Pinecone Assistant node. The first assistant is free, with pay-as-you-go pricing after that and an enterprise trial through sales.

It is a good fit for teams that want working RAG quickly and are comfortable with Pinecone’s managed cloud, rather than tuning every retrieval stage themselves.

10. Weaviate: Open Source Vector Search With A Query Agent

Weaviate describes itself as vector search, RAG and memory in one open source platform, with more than 20 million open source downloads. It supports pure vector, semantic and hybrid search with an adjustable weighting between vector and keyword matching, and built-in embedding modules remove the need for a separate embedding pipeline.

For agentic retrieval, the Query Agent translates natural-language questions into database queries, and Engram adds per-user memory. Deployment options include Shared Cloud, Dedicated Cloud, DigitalOcean managed Weaviate and self-hosting, with RBAC, SOC 2 and HIPAA listed.

Case studies include Loti, with 9 billion vectors in production, and DocsBot, which stores more than 50,000 tenants in a single cluster. Weaviate suits teams that want an open source foundation with managed options when they need them.

11. LlamaIndex: Document Parsing And Agent Workflows For Messy Data

LlamaIndex started as the open source framework many developers used for their first RAG prototype, and it has grown into a document processing cloud. LlamaParse handles agentic OCR across more than 50 file types, including embedded images, multi-page tables and handwritten notes. Extract, Split, Classify and Index products cover the rest of the pipeline, and LiteParse offers local, open source parsing with no cloud calls.

The open source LlamaIndex framework and Workflows remain the orchestration layer for agents. LlamaCloud runs in LlamaIndex’s cloud or in a customer VPC, with global and EU regions, and the company states HIPAA, GDPR and SOC 2 compliance.

LlamaIndex reports more than one billion documents processed and names KPMG, Carlyle, Lazard and PepsiCo among its users. It is the right layer when document quality, not search speed, is your main RAG problem, and it pairs well with a dedicated retrieval engine.

12. Ragie: RAG As A Service For Product Teams

Ragie is a fully managed RAG as a service platform, originally developed for the Glue messaging app. It builds vector, keyword and summary indexes for each knowledge base and combines them in hybrid search, with LLM re-ranking, recency bias and agentic retrieval for multi-step questions.

Ingestion covers text, PDFs, PowerPoint, images, audio and video, and agentic OCR pulls tables, forms and charts with coordinates for traceability. Connectors for Google Drive, Notion, Confluence and Slack can be embedded in your own product so your customers can link their data, and partitions isolate data by tenant. An MCP server gives agents permission-scoped access.

Ragie holds SOC 2 Type II and lists GDPR, HIPAA and CCPA, with cloud, VPC and on-prem deployment. It is a practical choice for SaaS teams adding RAG features for their own users. If you need help building on any of these, our list of RAG and vector search development firms covers implementation partners.

How To Choose An Enterprise RAG Platform

Do you need a retrieval engine or a finished product?

This is the first fork. Products such as Glean and Google Agent Search give employees or website visitors answers quickly, with connectors and permissions handled for you. Engines such as Vespa and Weaviate give you control over schemas, ranking and serving, which matters when RAG is part of your own product or relevance is a competitive advantage. Managed RAG as a service options such as Ragie and Pinecone Assistant sit in between..

How does the platform handle hybrid search and ranking?

Pure vector search misses exact terms, product codes and names; pure keyword search misses meaning. Ask how the platform combines the two, whether filters run during retrieval or after it, and where re-ranking happens. Platforms that run machine-learned ranking next to the data, as Vespa does with multi-phase ranking, avoid extra network hops and leave room to tune relevance as the corpus grows.

Will it hold up under agentic RAG workloads?

Agentic RAG multiplies queries: an agent that plans three sub-queries and re-checks one turns a single question into four or five retrieval calls. Check published latency figures, behavior at your document count and query volume, and whether updates are searchable in real time or wait for batch re-indexing.

Where can it be deployed, and who controls the data?

Enterprise RAG often touches contracts, health records or source code. Look for VPC, bring-your-own-cloud or on-prem options, and confirm which certifications appear on the vendor’s trust page. Vespa Cloud Enclave, Vectara’s air-gapped option and Cohere’s private deployments all keep data inside your boundary.

How are permissions enforced at query time?

A RAG system that leaks a salary spreadsheet into an answer is a security incident. Ask whether the platform syncs document ACLs from source systems and trims results per user at query time, as Glean, Azure AI Search and Contextual AI describe. If you build on an engine, plan how permissions will reach the index as filterable fields.

How will you measure answer quality?

Pick a platform that shows what was retrieved for every answer and lets you evaluate it. Citations, retrieval traces and built-in evaluation or hallucination detection make failures easier to debug and improvements easier to prove. For broader context on agent builds, see our list of firms powering AI agents with vector search, RAG and LLM orchestration.

Conclusion

Enterprise RAG in 2027 is less about connecting a model to a folder of PDFs and more about building a retrieval layer agents can trust. Cloud providers bundle agentic retrieval into their ecosystems, specialist vendors ship grounded agents with strict deployment options, and managed services hide the pipeline entirely.

Which one fits depends on what you are building. For an internal assistant over workplace tools, Glean or your cloud provider’s service will get you there fastest; for document-heavy pipelines, LlamaIndex, Ragie or Pinecone Assistant remove most of the work. When the RAG workload is large, changes constantly or sits inside your own product, Vespa is the strongest fit on this list, because it keeps hybrid retrieval, tensor-based ranking and real-time updates in one engine and has carried production traffic at Yahoo and for AI-native companies such as Perplexity. Start with your data, your permissions model and your latency budget, then pick the platform that fits all three.