Best Enterprise RAG Platforms for 2026: A Buyer's Guide

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TL;DR: The enterprise RAG market reached $1.94B in 2025 and is projected to hit $9.86B by 2030 at a 38.4% CAGR (MarketsandMarkets, Nov 2025). The market has split into three layers: turnkey RAG platforms (Onyx, Glean, Cohere North, Vectara), cloud RAG services tied to a hyperscaler (AWS Bedrock Knowledge Bases, Azure AI Search, Google Gemini Enterprise), and RAG infrastructure or frameworks you assemble yourself (Pinecone, LlamaIndex, LangChain, Elastic). MIT's 2025 GenAI Divide report found 95% of enterprise GenAI pilots fail to reach measurable P&L impact, and that vendor-partner deployments succeed roughly 67% of the time versus 33% for in-house builds. Picking the right layer for your team is the single biggest determinant of whether your RAG project ships. This guide covers all 11 platforms with current pricing, deployment models, customer proof points, and a decision framework.

Vendor pricing, valuations, and product launches verified against primary sources as of May 2026. Last updated 2026-05-08.

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What Is Enterprise RAG?

Enterprise RAG (retrieval-augmented generation) is the architecture that grounds an LLM's answers in a company's own indexed data. The pattern was introduced by Lewis et al. (2020) at NeurIPS and has since become the standard way to deploy AI over private knowledge.

In production, an enterprise RAG system does five things:

1. Ingests content from connected sources (Confluence, Slack, Google Drive, SharePoint, Jira, GitHub, Salesforce, Zendesk, file shares).
2. Chunks and embeds documents into a vector index, typically alongside a keyword index for hybrid search.
3. Retrieves the most relevant passages at query time using hybrid search (vector + BM25) and a reranker.
4. Generates a grounded answer by passing those passages to an LLM with citations.
5. Governs the entire pipeline with permission inheritance from source systems, audit logs, SSO, and (often) data residency controls.

Vanilla RAG (a vector DB plus a chat script) and enterprise RAG are not the same product. Enterprise RAG adds connectors, permissions, evaluation, observability, and governance. That gap is most of the engineering work and most of the procurement cost.

How Enterprise Buyers Should Think About the 2026 Market

The most common procurement mistake is buying a vector database when you needed a platform, or buying a platform when you needed a framework. The three layers of the market serve different buyers.

McKinsey's State of AI 2025 reports that 88% of organizations now use AI regularly in at least one business function, up from 78% a year earlier, and 23% are scaling agentic AI. Microsoft's 2025 Work Trend Index found that 75% of global knowledge workers use AI tools regularly, with usage nearly doubling in six months. The buyer pool has changed. Most procurement happens at the platform layer because most enterprises are not staffing a multi-quarter custom build.

What Is Onyx?

Onyx is an open-source (MIT-licensed) enterprise AI platform with RAG built in. It indexes 40+ enterprise connectors, runs hybrid search over an OpenSearch-backed vector store with permission-aware retrieval, and supports any LLM (OpenAI, Anthropic, Google, DeepSeek, Llama, Mistral, Qwen) via cloud APIs or local inference through Ollama, vLLM, or LM Studio. On top of search, it provides AI chat, multi-step deep research, and custom agents with MCP tool use, so teams can deliver workforce-facing AI without stitching together a vector DB, an embedding service, a reranker, and a chat UI.

Onyx is in production at Ramp (115K queries per month, 30-50x ROI per Ramp's published metrics), Thales (1,400 MAU across 82,000 employees), Astranis, and UC San Diego (37K+ users on a fully air-gapped deployment with local LLMs), among 1,000+ enterprise customers.

Onyx makes this list because it is one of the platforms a serious buyer should evaluate. The trade-offs section under Onyx below names real limitations, not marketing-safe ones.

How Enterprise RAG Actually Works

Three technical building blocks separate a serious RAG platform from a Friday-afternoon prototype.

Hybrid Retrieval (Vector + BM25)

Pure vector search has known failure modes for specific identifiers, acronyms, and exact-phrase queries. The standard production pattern, documented by Weaviate and others, combines dense vector search (semantic similarity) with sparse keyword search (BM25) and fuses the result lists, typically with Reciprocal Rank Fusion. Practitioner benchmarks consistently show hybrid retrieval delivering meaningfully better recall than either dense or sparse alone, often in the 15-30% range on enterprise corpuses. VentureBeat data shows enterprise intent to adopt hybrid retrieval tripled from 10.3% to 33.3% in a single quarter of 2025 as RAG programs hit the scale wall.

Reranking

After hybrid retrieval returns the top 100-200 candidates, a reranker (a smaller, focused model) reorders them by deeper relevance. Cohere's Rerank 4 (Dec 2025) and BGE Reranker v2 are the most-deployed options. Databricks reported a +15 percentage point retrieval accuracy improvement on enterprise benchmarks after adding reranking to Mosaic AI Vector Search. ColBERT-style late-interaction retrievers (Khattab and Zaharia, 2020) offer a different trade-off, encoding queries and documents at the token level for higher accuracy at higher index size.

Permission-Aware Retrieval

Enterprise RAG must filter documents by who is allowed to read them. The two paradigms are early binding (filter pre-retrieval, the default in production) and late binding (filter post-retrieval). Doing this correctly requires syncing access control lists from every source system, propagating them into the index, and enforcing them at query time. Many platforms wave at "permissions" while only enforcing them at the chat-UI level. That is not the same product.

Why This Matters For Procurement

Hallucinations do not go away with RAG. The 2024 Stanford RegLab study, published in the Journal of Empirical Legal Studies in 2025, found that production legal RAG systems still hallucinated on a non-trivial share of queries: LexisNexis Lexis+ AI on 17% and Thomson Reuters Westlaw AI-Assisted Research on roughly 33%. They were still better than a base LLM alone, but not error-free. Vectara's open Hughes Hallucination Evaluation Model leaderboard tracks this empirically across 7,700+ articles. The implication: evaluation, citation, and human-in-the-loop review are part of the platform stack, not afterthoughts.

What to Look For in an Enterprise RAG Platform

Use these criteria for any platform-layer evaluation. They are ordered by how often they end up being the deciding factor in real deals.

• Connector breadth and depth. How many SaaS apps does it sync from out of the box? Does it sync continuously or on a schedule? Does it preserve permissions from the source?
• Permission inheritance. ACL syncing is hard. Verify the platform filters at retrieval time, not at the UI.
• Retrieval quality. Hybrid search, reranking, and (increasingly) graph or agentic retrieval. Ask vendors for evaluation results on your data.
• Model choice. Locked to one provider, or any LLM? Frontier rankings change every quarter.
• Deployment options. Cloud, customer VPC, self-hosted, air-gapped. For regulated industries, this is often a hard requirement.
• Generation surface area. Does it stop at search-with-answers, or extend to chat, deep research, agents, and MCP tool use?
• Evaluation and observability. Trace which chunk drove which answer. Run RAGAS or equivalent over time.
• Pricing transparency. Per-seat, usage-based, or "contact sales"? Watch for opaque enterprise minimums.
• Open source. Auditability, no vendor lock-in, freedom to extend. Matters more for some buyers than others.

The 11 Best Enterprise RAG Platforms for 2026: Comparison Table

Vendors define "connector" inconsistently. Some count any HTTP source. Some count only deeply integrated, permission-syncing connectors. Verify the specifics for the apps you care about.

Detailed Reviews

1. Onyx

What it is: A full-stack open-source enterprise AI platform combining RAG-powered search, multi-model chat, deep research, and custom agents in one product. Retrieval uses OpenSearch for hybrid search (vector + BM25) with reranking, contextual retrieval, and LLM-based knowledge graphs for cross-document context. Permission inheritance from source systems is enforced at retrieval time.

Connectors: 40+ including Slack, Confluence, Jira, Google Drive, SharePoint, Salesforce, GitHub, Notion, HubSpot, Zendesk, Linear, Gmail, Outlook. Continuous sync. Permissions preserved from each source.

RAG architecture: Hybrid retrieval, multi-pass indexing, contextual chunking, optional Agent Search using StructRAG techniques. Any LLM works for generation: OpenAI, Anthropic, Google, DeepSeek, Llama, Mistral, Qwen via cloud APIs or local inference (Ollama, LM Studio, vLLM). Deep research adds multi-step agentic investigations over the indexed corpus. Onyx benchmarks published in early 2026 reported a 64-76% win rate on workplace-question quality versus ChatGPT, Claude, and Notion AI on a 220K-document corpus across GitHub, Gmail, Drive, and Slack.

Deployment: Self-hosted via Docker Compose, Kubernetes (Helm), or Terraform. Managed cloud also available. Fully air-gapped deployments are supported with local LLMs and zero internet dependency. UC San Diego runs Onyx air-gapped on local GPUs for 37,000+ users.

Security and governance: SOC 2 Type II, GDPR compliant, ISO 27001 in progress. SSO via OIDC and SAML, SCIM/IdP integration, granular RBAC, audit trails. The MIT-licensed codebase means security teams can audit the implementation rather than trusting a marketing claim.

Pricing: Free community edition (fully functional, MIT). Cloud Business plan at $20/user/month (annual) or $25/user/month (monthly). Enterprise tier with tiered per-seat pricing for larger deployments and self-hosted enterprise installs.

Customer proof: 23K+ GitHub stars. 1,000+ enterprise customers including Ramp, Brex, Thales, L3Harris, NASA, Astranis, Roku, UC San Diego.

The Head of Engineering at Thales Group, comparing Onyx to Microsoft Copilot in production, said: "People are using both Copilot and Onyx. In the end, they are very happy with Onyx. The result given is better, even when they use the same underlying model."

Best for: Workforce AI deployments where self-hosting, model freedom, or open-source auditability matter. Regulated industries that need data sovereignty or air-gapped deployment. Engineering-led teams that want extensibility (custom connectors, REST APIs, MCP server, agent SDK, embeddable chat widget).

Trade-offs: Connector count (40+) is smaller than Glean's 100+, though it covers the most common enterprise apps. Self-hosted deployment requires Docker or Kubernetes operational knowledge. The managed cloud option removes the ops burden but loses some of the data-sovereignty advantages.

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2. Glean

What it is: The category leader for cloud-hosted enterprise AI search and assistants. Glean built a knowledge graph over the corpus, layered RAG-powered search and answers on top, and is now extending into agents.

Connectors: 100+ enterprise connectors, the broadest in the market. Real-time permission syncing is a genuine technical strength.

RAG architecture: Proprietary GraphRAG combining vector retrieval with a custom knowledge graph that captures relationships between people, documents, and concepts. Tenant-tuned reranking. The Glean Model Hub provides access to 15+ LLMs with per-step model selection.

Deployment: Primarily cloud-hosted. On-premises is available through a Dell partnership announced May 20, 2025, running on Dell AI Factory infrastructure for healthcare, finance, and other regulated sectors. A "Cloud-Prem" model runs the tenant in the customer's own cloud. Both are vendor-managed; there is no open-source self-hosting on arbitrary infrastructure.

Funding and traction: $150M Series F at a $7.2B valuation in June 2025, led by Wellington Management. Sacra estimates Glean reached $208M ARR in 2025, up 89% year over year. 100M+ agent actions per year on the platform.

Pricing: Not published. Third-party reporting and procurement leak data consistently put list price around $50/user/month with a $60K+ annual minimum, mandatory ~10% support fees, and first-year TCO between $300K and $1M+ depending on size (sources).

Customer proof: Booking.com, Grammarly, Duolingo, Deutsche Telekom, Confluent, Databricks, Plaid, Motive.

Best for: Large enterprises (1,000+ employees) with budget for a premium, vendor-managed product and a need for the broadest connector library out of the box.

Trade-offs: Expensive and opaque pricing. No true open-source option. Long implementation cycles (months) compared to faster-to-deploy alternatives. On-prem is Dell-specific and Glean-managed. Limited customization of retrieval or platform behavior. Not a fit for fully air-gapped environments.

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3. Cohere North

What it is: Cohere North, launched in early-access January 2025, is Cohere's private-deployable agentic AI workspace. It packages Cohere's Command generative models, Compass search (Embed v4 + Rerank 4), and customizable agents into an enterprise product positioned as the alternative to Microsoft Copilot and ChatGPT Enterprise.

Connectors: Growing connector library covering common workplace apps. Less mature than Glean or Onyx; Cohere has emphasized enterprise data integrations as a roadmap priority.

RAG architecture: Cohere Embed v4 for embeddings, Cohere Rerank 4 for reranking (released December 2025), Command-family models (or BYO) for generation. Strong reranking is the headline technical advantage. Cohere internal testing claims an 80%+ reduction in task completion time versus manual search.

Deployment: Cloud, hybrid, or fully isolated VPC deployments. Cohere has invested heavily in sovereign deployment: a $725M Cambridge, Ontario data center co-funded by a $240M Canadian federal investment (March 2025), MoUs with the Canadian and UK governments, and 2025-2026 partnerships with Bell Canada, Thales for naval defense, Hanwha Ocean, and Saab.

Funding: $500M raised August 2025 at a $6.8B valuation, extended by $100M in September 2025 to a $7B valuation. Investors include Radical Ventures, Inovia, AMD Ventures, NVIDIA, Salesforce Ventures.

Customer proof: Royal Bank of Canada, Dell Technologies, LG CNS, Ensemble Health Partners, Palantir, Oracle (which has built 100+ generative AI use cases on Cohere Embed and Rerank inside Fusion Cloud Apps).

Best for: Enterprises that want Cohere's models and reranking in a private deployment, particularly defense, financial services, and public sector buyers with strict data residency requirements who do not need open-source flexibility.

Trade-offs: Smaller connector ecosystem than the leaders. Closed-source. Pricing requires sales conversations. Model choice is centered on Cohere even when BYO is allowed.

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4. Vectara

What it is: A managed RAG-as-a-Service platform that handles the full retrieval pipeline behind an API: ingestion, embedding via the proprietary Boomerang model, hybrid retrieval, reranking, generation, and citation. Vectara is positioned for builders embedding RAG into apps, with strong emphasis on grounding accuracy.

RAG architecture: Boomerang embedding model (multi-language, zero-shot). Hughes Hallucination Evaluation Model (HHEM) for grounding evaluation, runs in 0.6 seconds on an RTX 3090 versus ~35 seconds for RAGAS using a frontier LLM judge on a 4096-token context. Hallucination Corrector launched May 2025, claims hallucination rates under 1% on sub-7B-parameter LLMs. Conversational AI / Agent API launched September 2025.

Customers: Broadcom selected Vectara (2025) for agentic conversational AI customer service for enterprise clients.

Deployment: Cloud-only SaaS. No self-hosted or on-prem option.

Pricing: Free entry tier; usage-based scale tier; enterprise contracts often above $50K/year. Vectara pricing.

Best for: Product teams embedding RAG into customer-facing applications where grounding accuracy and citation quality are differentiators.

Trade-offs: Not a workforce-facing product. Limited connector ecosystem. Cloud-only excludes regulated and air-gapped use cases.

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5. AWS Bedrock Knowledge Bases

What it is: Amazon Bedrock Knowledge Bases is AWS's managed RAG service. It handles ingestion, configurable chunking, embedding, vector storage, retrieval, and generation against Bedrock-hosted models, with native IAM integration.

Vector stores: Aurora PostgreSQL, OpenSearch Serverless, OpenSearch Service Managed Cluster (added March 2025), Neptune Analytics (for GraphRAG), MongoDB Atlas, Pinecone, Redis Enterprise Cloud, and Amazon S3 Vectors, which reached general availability in December 2025 across 14 regions with up to 2 billion vectors per index and a claimed 90% cost reduction versus alternatives for infrequent-query workloads.

Connectors: S3 is the primary ingestion path. Native connectors exist for Confluence, Salesforce, SharePoint, and Slack, with custom Lambda chunking for anything else. Most teams ETL into S3.

RAG architecture: Semantic, hierarchical, and fixed chunking plus custom Lambda chunking. GraphRAG via Neptune Analytics. Multimodal retrieval (images, charts, tables, audio, video, structured DB). Hybrid search added in April 2025 for Aurora PostgreSQL and MongoDB Atlas. Built-in NL2SQL.

Deployment: AWS only. Inherits AWS regions and compliance posture (FedRAMP High, HIPAA-eligible, ISO).

Best for: Teams already standardized on AWS, building RAG into custom applications. Organizations that want IAM-native access control and the option to keep all data in their AWS account.

Trade-offs: Locked into AWS and Bedrock's model catalog. No turnkey workforce UI; you build the front end. Connector coverage requires significant ETL for SaaS sources. Usage-based pricing across embeddings, vector storage, queries, and generation can be hard to forecast.

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6. Azure AI Search + Azure OpenAI

What it is: Microsoft's RAG building blocks inside Azure. Azure AI Search provides hybrid retrieval and indexing; Azure OpenAI provides generation. The "On Your Data" feature wires them together with a default RAG pattern, and the broader Azure AI Foundry stack adds agent and orchestration tooling.

RAG architecture: Hybrid retrieval (vector + BM25 + Microsoft's semantic ranker), Azure-managed reranking, configurable chunking and field mapping, vector storage inside the search index. Generation is via Azure OpenAI with grounding. Document-level access control and security trimming enforce permissions at retrieval time.

Connectors: Pull from Azure data sources (Blob Storage, Cosmos DB, SQL, Files), plus indexers for SharePoint, OneLake, and others. Microsoft Graph integration is the strongest path for M365 content.

Deployment: Azure only. Azure RBAC governs access.

Best for: Microsoft 365 and Azure shops building internal AI apps grounded in Azure-resident data. Often paired with Microsoft 365 Copilot for the workforce-facing piece.

Trade-offs: Locked into Azure and Azure OpenAI for first-class generation. You build the workforce-facing UI. Non-Microsoft connectors are sparse. Pricing has many SKUs and requires careful capacity planning.

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7. Google Gemini Enterprise Agent Platform

What it is: At Cloud Next 2026, Google rebranded Vertex AI to the Gemini Enterprise Agent Platform, consolidating former Vertex AI, Agentspace, and Gemini Code Assist Enterprise into one product with per-agent pricing, a no-code Workspace Studio builder, and a 200-model Model Garden.

RAG architecture: Google-quality semantic search and ranking, generative answers with multi-turn follow-ups, multimodal retrieval, integration with Gemini models for generation. The platform now includes Agent Development Kit, Agent Studio (low-code visual canvas), Agent Garden (prebuilt templates), Agent Engine runtime with observability dashboards (token usage, latency, error rates), simulation environment, agent registry, and a third-party agent marketplace. MCP support across Google Cloud and Workspace.

Connectors: Native connectors for Jira, Confluence, Salesforce, ServiceNow, and a growing list, plus website crawlers and structured data ingestion.

Deployment: Google Cloud only.

Best for: Google Cloud-native organizations and teams building applications on Gemini. Use cases that benefit from Google's web-scale ranking and multimodal capabilities.

Trade-offs: GCP lock-in. No standalone workforce chat product without build effort. Usage-based pricing across query, indexing, and generation tiers. Model choice is centered on Google.

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8. Pinecone (with Assistant)

What it is: Pinecone is the most-deployed managed vector database. Pinecone Assistant, now generally available, layers a managed RAG API on top, handling chunking, embedding, retrieval, reranking, and generation against connected files.

Customers: 5,000+ paying customers including Notion, Gong, Zapier, Shopify, CS Disco. Notion's Q&A AI runs on Pinecone serverless and reportedly cut costs 60% after migration.

RAG architecture: Best-in-class managed vector retrieval (serverless and pod-based options), hybrid search via sparse-dense indexes, namespaces for multi-tenant isolation, metadata filtering, and an Evaluation API for benchmarking. Pinecone Assistant adds chunking and grounded generation with citations at $0.05 per assistant-hour plus $5 per 1M context-processed tokens.

Pricing: Pricing: Standard plan minimum $50/month (raised October 2025), Enterprise minimum $500/month. Storage at $0.33/GB/month with read units at $8.25 per 1M and write units at $2 per 1M.

Deployment: Cloud-only across AWS, GCP, Azure regions.

Best for: Engineering teams that have chosen "build" over "buy" and need the highest-quality managed vector store. Pinecone Assistant is a fast path to a working RAG endpoint for product teams.

Trade-offs: Not a workforce platform. No connector ecosystem; ingestion is your responsibility. Permissions are an application-layer concern. Costs scale with index size and query volume.

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9. LlamaIndex / LlamaCloud

What it is: LlamaIndex is a popular open-source RAG framework with deep retrieval, indexing, and agent abstractions (~49K GitHub stars). LlamaCloud is the hosted service, running LlamaIndex pipelines and LlamaParse (the company's parser for complex PDFs, tables, and handwriting) as a managed product. LlamaParse v2 launched in 2025 with simplified pricing.

Connectors: LlamaHub provides hundreds of community-contributed loaders. Production-grade enterprise connectors with permission syncing are not the focus; most are file or API loaders.

RAG architecture: Comprehensive retrieval primitives (vector, keyword, hybrid, recursive, graph-based), reranking, query routing, and an agent framework. Highly composable. Pairs with any vector DB and any LLM.

Pricing: LlamaCloud pricing: 1,000 credits = $1, with 10K free credits per month. LlamaParse v2 tiers from 3 credits per page (Cost-Effective) to 45 credits per page (Agentic Plus). Enterprise: private VPC deployment on AWS, Azure, or GCP, also available on AWS Marketplace and Azure Marketplace.

Best for: Engineering teams building custom RAG applications who want first-class abstractions for advanced retrieval patterns and complex document parsing.

Trade-offs: It is a framework, not a workforce product. You own the application, the operations, the connectors, the permissions, and the UI. LlamaCloud is mostly the parsing and ingestion layer.

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10. LangChain / LangSmith / LangGraph

What it is: LangChain is the most-deployed open-source RAG and agent framework (~115K GitHub stars, ~28M monthly downloads, +220% star growth and +300% download growth Q1 2024 to Q1 2025). LangSmith is the hosted observability and evaluation product. LangGraph is the stateful agent framework for production agent workflows. LangChain reached unicorn status in October 2025 with a $125M Series B at a $1.25B valuation, led by IVP with Sequoia, Benchmark, CapitalG, ServiceNow Ventures, Workday Ventures, Cisco Investments, and others.

Customers: Cisco, Uber, LinkedIn, BlackRock, JPMorgan, Microsoft, Morningstar, BCG, Klarna. Roughly 400 companies run LangGraph Platform in production.

RAG architecture: Composable retrievers (vector, keyword, hybrid, parent-document, multi-vector, ensemble), document loaders, chunkers, reranking integrations, and prompt templates. LangSmith adds traces, evals, and dataset-driven testing. LangGraph adds graph-based agent orchestration with checkpointing and human-in-the-loop support. Pairs with any vector DB and any LLM.

Pricing: LangSmith pricing: Developer tier free (5K traces/month, 14-day retention), Plus $39/seat/month with $2.50 per 1K trace overage, Enterprise custom. LangGraph Platform (renamed LangSmith Deployment in October 2025) is metered at $0.001 per node executed plus standby compute, with cloud, hybrid (control plane SaaS plus data plane in customer VPC), or self-hosted deployment.

Best for: Engineering teams building custom RAG and agent applications who want the broadest framework ecosystem and a mature observability stack. The strongest enterprise pull is LangSmith for tracing and evaluation.

Trade-offs: A framework, not a workforce product. Large API surface area; teams sometimes report version churn and breaking changes between releases. You own the application, operations, connectors, permissions, and UI.

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11. Elastic + Elasticsearch Relevance Engine (ESRE)

What it is: Elastic provides search infrastructure with ESRE adding LLM integration primitives, vector search, and hybrid retrieval. Two pieces of context to know: Elastic's standalone Enterprise Search product (App Search and Workplace Search) is end-of-life and will not ship in 9.x, with managed connectors deprecated in favor of self-managed connectors. Separately, Elastic re-added the AGPLv3 license in September 2024, restoring OSI-recognized open source after the 2021 SSPL move. Elastic was named a Leader in Forrester's Wave for Cognitive Search Platforms, Q4 2025.

Connectors: Native connectors for Salesforce, SharePoint, Google Drive, Slack, GitHub, and others. Migration is required: Elastic-managed connectors are removed in 9.0.

RAG architecture: Mature hybrid search (BM25 + dense vectors + ELSER sparse vectors), strong reranking, learned-to-rank, and broad relevance tooling. ESRE adds RAG primitives for chunking, embedding, and LLM calls. You assemble the application.

Deployment: Self-hosted, Elastic Cloud, or hybrid.

Best for: Engineering teams that already operate Elasticsearch or want maximum control over indexing, ranking, and relevance.

Trade-offs: Search infrastructure, not a finished RAG product. Building an enterprise RAG application on top requires substantial development. License changes and Enterprise Search EOL have created procurement friction over the past two years.

Recommended Stacks by Buyer Profile

Pattern 1: Workforce AI for a mixed-tool company

Profile: 200-10,000 employees. Tools span Slack, Google Drive, Confluence, Jira, GitHub, Salesforce, and more. Goal: a single AI surface for employees to search and chat over company knowledge.

Recommended stack:

• Platform: Onyx (self-hosted or cloud) or Glean if budget is unconstrained and pure cloud is acceptable.
• Models: Frontier (OpenAI, Anthropic, or Gemini) for chat, with a cost-tuned embedding model. Cohere Embed v4 or OpenAI text-embedding-3-large are common upgrades from defaults.
• Why: A turnkey platform handles connectors, permissions, and the chat experience. Building this from a vector DB and a framework is months of work and ongoing maintenance. The MIT GenAI Divide report found vendor partnerships succeed roughly 67% of the time versus ~33% for in-house builds.

Pattern 2: Regulated or air-gapped enterprise

Profile: Defense, aerospace, healthcare, finance, EU-regulated workloads, or any organization that cannot send data to a third-party cloud.

Recommended stack:

• Platform: Onyx self-hosted (Docker or Kubernetes) with local LLMs via Ollama, vLLM, or similar. Cohere North in private VPC if Cohere models are a fit.
• Models: Open-weight models (Llama, Mistral, Qwen, DeepSeek) on internal GPUs.
• Vector DB: OpenSearch (bundled with Onyx) or self-hosted Elastic if you have existing infrastructure.
• Why: Only configurations with no third-party data egress meet ITAR, FedRAMP, CMMC, and certain GDPR/EU AI Act requirements. Glean's Dell on-prem and Cohere private deployment are vendor-managed alternatives if you accept that constraint.

Pattern 3: RAG embedded in a custom or customer-facing product

Profile: Engineering team building RAG into a SaaS product, internal app, or domain-specific workflow. The end users are not the buyers of an enterprise AI platform.

Recommended stack:

• Cloud RAG service if committed to a hyperscaler: AWS Bedrock Knowledge Bases (with S3 Vectors for cost), Azure AI Search + Azure OpenAI, or Google Gemini Enterprise.
• Or build with infrastructure: Pinecone, Weaviate, or Qdrant for the vector DB; LlamaIndex or LangChain for the retrieval pipeline; your choice of LLM; Cohere Rerank 4 or BGE Reranker v2 for reranking; RAGAS for evaluation.
• Or use Onyx as a backend: Onyx exposes REST APIs and an MCP server. Ramp built its customer-facing "Ramp Assist" feature on Onyx.

How to Choose: A Decision Framework

Run through these questions in order. Each one narrows the field.

1. Is this for employees, or for a product I'm building? Employees: stay at the platform layer (Onyx, Glean, Cohere North). Product: drop to cloud RAG or infrastructure layers.
2. Do I need self-hosted or air-gapped deployment? True self-hosted on my own infrastructure: Onyx (open-source), or Elastic for infrastructure-only. Vendor-managed on-prem acceptable: Glean (via Dell), Cohere North.
3. Am I locked into a hyperscaler? AWS: Bedrock Knowledge Bases. Azure: Azure AI Search + Azure OpenAI. GCP: Gemini Enterprise.
4. How much do I care about model choice? A lot: Onyx (any LLM, including local), Pinecone, LlamaIndex, and LangChain are model-agnostic. Some: Glean Model Hub (15+), Bedrock catalog. Not really: hyperscaler RAG services lock you to that cloud's models.
5. What is my budget tolerance? Tight: Onyx free community edition self-hosted, or Onyx Cloud at $20/user/month. Mid-market: Onyx Cloud, GoSearch, Pinecone for builders. Large enterprise: any platform; compare Glean and Onyx Enterprise on three-year TCO.
6. How important is open source and extensibility? Critical: Onyx (MIT), LlamaIndex (MIT), Elastic (AGPLv3). Nice to have: most others are closed.

Recommendation

Most enterprises evaluating RAG platforms in 2026 should start at the platform layer. Stitching together a vector DB, an embedding service, a reranker, a chunking pipeline, a connector framework, a permission model, and a chat UI is technically possible and almost always a worse use of engineering time than buying or self-hosting a finished product. The MIT data on pilot failure rates is consistent with what most teams discover six months in: vendor-built platforms ship; in-house builds frequently do not.

Among platform options, Onyx and Glean dominate the workforce conversation for different reasons. Glean is the safe, premium, vendor-managed choice for large enterprises with budget. Onyx is the open-source, model-agnostic, self-hostable alternative that runs in production at Ramp, Thales, L3Harris, Astranis, and UC San Diego, including fully air-gapped deployments. If self-hosting, model freedom, or open-source auditability matter to your team, Onyx is the clearer fit. If they do not, evaluate both on retrieval quality with your data and three-year total cost of ownership.

For builders embedding RAG into a product, the right move is matching the layer to the team. Pick the cloud RAG service that aligns with your hyperscaler. Or assemble Pinecone plus LlamaIndex plus a reranker if you need maximum flexibility. Do not buy a workforce platform to power a backend feature.

Run a proof of concept with your actual documents and your actual users. RAG quality is corpus-specific, and benchmarks rarely predict how a system performs on your data. The differences between platforms become obvious within a week of real usage.

Try Onyx for free, self-host the open-source version and connect your first data source in under a day, or book a demo for a regulated or large-scale deployment.

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Frequently Asked Questions

What is enterprise RAG?

Enterprise RAG is retrieval-augmented generation deployed on a company's private data with the connectors, permission inheritance, evaluation, governance, and observability that production usage requires. It differs from hobbyist RAG (a vector DB and a chat script) by treating the entire stack, including ACL syncing, audit logs, and SLAs, as part of the product.

What is the difference between a RAG framework and a RAG platform?

A RAG framework (LangChain, LlamaIndex, Haystack) gives you the building blocks: retrievers, embedders, chunkers, prompt templates, agents. You write the application, deploy it, and operate it. A RAG platform (Onyx, Glean, Cohere North, Vectara) is a finished product: connectors, indexing, retrieval, generation, governance, and a UI all included. Frameworks are right when embedding RAG into your own product. Platforms are right when delivering an AI experience to a workforce.

RAG vs fine-tuning: which is better for enterprise AI?

For most enterprise use cases, RAG is the better default. RAG handles changing knowledge cheaply (re-index on update, no retraining cost), provides citations, and respects permissions per query. Fine-tuning is more cost-efficient at very high query volumes for fixed tasks but costs $500-$5,000+ per retrain, does not produce citations, and cannot enforce per-user permissions. The two are complementary: many production systems fine-tune for tone or task and use RAG for facts. Contextual AI's analysis covers the trade-offs in more depth.

How much does enterprise RAG cost?

Three pricing patterns. Workforce platforms: $20-60/user/month, plus LLM API costs of roughly $5-15/user/month for a moderately active user. Self-hosted open source: free at the license layer plus $1,000-10,000/month in infrastructure for a mid-sized deployment, plus engineering operations. Cloud RAG services: usage-based, typically $0.10-1.00 per 1,000 user queries plus storage. Annual TCO for a 200-user Glean deployment is reported in the $300K-1M+ range; the equivalent on Onyx Cloud is roughly $48K-72K/year before LLM costs.

Does RAG eliminate hallucinations?

No. Stanford RegLab found that production legal RAG systems still hallucinated on 17-33% of queries in a peer-reviewed 2024-2025 study. RAG dramatically reduces hallucinations versus a bare LLM, but does not eliminate them. Mitigations include reranking, hybrid search, citation verification, HHEM or RAGAS evaluation, and human review for high-stakes outputs.

Do I need a vector database for enterprise RAG?

In platform deployments, yes, but it is bundled and invisible. Onyx uses OpenSearch, Glean uses a proprietary store, AWS Bedrock Knowledge Bases lets you choose between OpenSearch Serverless, Aurora, S3 Vectors, Pinecone, MongoDB Atlas, Neptune Analytics, and Redis Enterprise Cloud. You only pick a vector DB explicitly when building from infrastructure (Pinecone, Weaviate, Qdrant, Milvus, Chroma).

Is open-source enterprise RAG actually production-ready?

For workforce platforms, yes. Onyx (MIT) is in production at Ramp (115K queries/month), Thales (1,400 MAU across 82,000 employees), and Astranis, as well as fully air-gapped at UC San Diego on local GPUs for 37,000+ users. For RAG frameworks, LangChain and LlamaIndex are widely deployed in production. Open source does not mean immature; it means auditable. Note that license changes have hit some adjacent products: Open WebUI moved from BSD-3 to a custom restrictive license in 2025, and Elastic re-added AGPLv3 alongside SSPL in September 2024.

How do enterprise RAG platforms handle permissions?

The good ones inherit access controls from the source system at retrieval time, then filter candidate chunks before passing context to the LLM. Microsoft documents the canonical patterns for query-time ACL/RBAC enforcement and document-level access. The bad ones enforce permissions only at the chat-UI level, which can leak content under adversarial prompts. Verify this behavior carefully during evaluation. Onyx, Glean, and Azure AI Search do permission-aware retrieval at the platform layer. Many cloud RAG services rely on the application developer to filter correctly.

What are the best Glean alternatives for 2026?

The most-evaluated alternatives to Glean are Onyx (open-source, self-hostable, model-agnostic, ~60% lower per-seat cost), Microsoft 365 Copilot (for all-in M365 shops), ChatGPT Enterprise (for teams primarily wanting AI chat with light search), and GoSearch (mid-market, transparent pricing). For deeper analysis see our Glean alternatives guide and Glean vs Onyx comparison.

How do I evaluate RAG quality before buying?

Build an evaluation set of 50-100 questions with known correct answers from your actual corpus. Run each candidate platform against the same corpus and score on retrieval precision (was the right chunk retrieved?), answer faithfulness (does the answer match the source?), and citation correctness. RAGAS automates parts of this. The MTEB benchmark is the public reference for embedding model quality. Public benchmarks rarely predict your results because RAG quality is highly corpus-specific.

Is it cheaper to build my own RAG with LangChain and Pinecone than to buy a platform?

Almost never, if the goal is a workforce-facing product. Production-grade connectors, permission inheritance, evaluation, observability, governance, and a UI cost more to build than they cost to buy over any reasonable horizon. Astranis connected all their knowledge sources with Onyx in under a day; the equivalent custom build is a multi-quarter engineering project. Atlan's analysis pegs the build-versus-buy break-even at roughly three dedicated ML engineers. Build-your-own makes sense for product RAG with deep customization needs, not for replacing a workforce platform.