Best Glean Alternatives

Search alone is no longer the differentiator it once was. The tools that matter now either close the gap between finding information and acting on it, or let a team keep full control over how and where that search actually runs.

Teams used to ask "where is the document?" Increasingly, they're asking something closer to "why is the pipeline off against the board deck, and can this get fixed before the next call?" That's a different kind of question, and it points to a real shift underway in enterprise AI: from retrieving information to actually doing something with it.

This guide compares the strongest alternatives to Glean for that shift.

What Glean actually does well, and where it stops

Glean's real strength is unification:

• Connects a team's scattered SaaS tools, Slack, Docs, a CRM, and more, into one indexed, searchable surface
• Replaces the guesswork of "which app and which channel was this in" with a single place to look
• Genuinely useful for "find this document" or "who said what about this" kinds of questions

The limits show up once the question moves past retrieval:

• Agent capabilities remain more limited than a purpose-built agentic platform
• Better at summarizing what it finds than at reasoning across systems
• Can't reliably run code or write a result back into a CRM without a person doing that part by hand

That's the specific gap the alternatives below are built to close.

Best Glean alternatives

Tool	Primary strength	Agentic action	Governance	Best for
PromptQL	Agentic semantic layer with an auditable reasoning trace	Multi-step planning across connected data backends	GDPR and ISO 27001, dedicated VPC or bring-your-own-cloud	Auditable reasoning over structured, connected data
Gumloop	Multi-step agentic workflows with live writeback	Full read and write to external systems, with code execution	SOC 2 Type II, GDPR, VPC deployment option	Teams that need AI to execute, not just retrieve
Microsoft Search	Native Microsoft 365 and Azure integration	Limited, via Power Platform connectors	Entra ID, Microsoft Purview	Large, Microsoft-first enterprises
Google Vertex AI Search	Custom LLM tuning grounded in enterprise data	Grounding in enterprise data, not agentic loops	Google Cloud IAM, VPC Service Controls	AI-intensive teams already on Google Cloud
Elastic Workplace Search	Developer-controlled relevance tuning, open-source core	API-driven, custom build possible	Elastic Security, audit logging	Engineering-led teams that want to build, not buy
AWS Kendra	Managed NLP search natively inside AWS	Minimal, service-bound	AWS IAM, KMS, CloudTrail	AWS-native shops needing managed search
Onyx	Open-source, self-hostable enterprise search	Chat and agents on top of retrieval, MIT-licensed	Full infrastructure control, self-hosted	Teams that want Glean's core value without vendor lock-in

1. PromptQL

PromptQL takes a different approach to the search-versus-action split: it uses the underlying model for planning, not for generating the final answer directly. That distinction matters, since it means every answer comes with a reasoning trace that can actually be inspected, rather than a fluent summary that has to be taken on trust.

How it works: PromptQL sits as an agentic semantic layer over a team's existing data, including Slack, Google Docs, Snowflake, and Salesforce CRM, scoping access end to end across retrieval, creation, and update. It runs in a customer's own cloud, with deployment described as possible in hours, and enterprise plans offering a dedicated VPC or bring-your-own-cloud option. PromptQL Tag builds a shared wiki as the team works, complete with suggestions, page-change notifications, revision history, and a full audit trail, and shared threads are accessible via a public link with no signup required for viewers.

Strengths:

• Reasoning stays auditable, since the model plans rather than freely generating a final, unverifiable answer
• Independently listed as built for GDPR and ISO 27001:2013 compliance, alongside SOC 2 Type II and HIPAA
• Output becomes durable, shared knowledge instead of a chat reply that disappears into scrollback

Limitations:

• Requires connecting and configuring real data sources rather than working out of the box against a demo
• Built around structured, connected data reasoning, not the kind of broad, general-purpose consumer search Glean offers

2. Gumloop

Gumloop starts from a different premise than search: instead of returning a summary and leaving the actual work to a person, it reasons across live data, runs code, and writes results back into a team's existing tools.

How it works: Multi-step workflows pull live data, reason across it, and push results directly into connected systems, without requiring a custom API integration to be built first. Gumloop generates live HTML artifacts with external sharing links and full revision histories, so an output persists as something reopenable rather than a one-off chat reply.

Strengths:

• Built for direct action: reading and writing to external systems, not just retrieving and summarizing
• Gumloop is SOC 2 Type II and GDPR compliant, and can run on its own secure infrastructure or inside a customer's own VPC
• Shareable artifacts with revision history turn agent output into something a team can revisit, not just a transient answer

Limitations:

• Requires more setup than a pure search tool, since it's built around executing workflows, not just indexing content
• A better fit for teams whose bottleneck is action, not for a team whose primary need is still simple document search

3. Microsoft Search

For an organization already deep in SharePoint, Outlook, and the Azure identity plane, Microsoft Search extends what's already there rather than introducing a new platform.

How it works: It surfaces results across the Microsoft 365 graph, with native understanding of existing security groups, and hands off multi-step workflow execution to Power Automate rather than running agentic loops natively.

Strengths:

• Deep, native integration with Microsoft 365 and the existing Azure identity plane
• No new security model to learn, since it inherits an organization's existing Entra ID groups

Limitations:

• Rollout tends to be a deliberate project rather than a quick setup, and connecting non-Microsoft data sources takes real work
• No native multi-step workflow execution, it depends on Power Automate, which only helps if a team has already standardized on that stack

4. Google Vertex AI Search

Google's enterprise search offering leans into customization: Vertex AI Search grounds generative answers in a team's own structured and unstructured data, tuned with custom LLMs rather than a fixed model.

How it works: Answers are grounded directly in connected enterprise data, with integrations that come standard for teams already running BigQuery and Cloud Storage.

Strengths:

• Deep customization for teams willing to invest in tuning it to their own data
• Strong fit for organizations already standardized on Google Cloud infrastructure

Limitations:

• Setup is genuinely complex, closer to building an engine than adopting a finished product
• Grounding in enterprise data isn't the same as agentic, multi-step action, it stays closer to retrieval

5. Elastic Workplace Search

Elastic doesn't try to be a polished, out-of-the-box AI assistant. It's a relevance engine with an open-source core and an API surface built for a team that wants to tune it themselves.

How it works: Relevance tuning happens programmatically, so search quality improves in direct proportion to the engineering effort a team puts into it, rather than out of the box.

Strengths:

• Fully auditable, horizontally scalable retrieval layer
• A strong fit for engineering-led teams that want to build a custom relevance model rather than accept a vendor's defaults

Limitations:

• No generative AI or agentic loops without significant custom work on top
• Not plug-and-play, it requires real engineering investment to get real value

6. AWS Kendra

AWS Kendra is the natural choice for a team already deep in the AWS ecosystem. It's a fully managed, NLP-based search service with native connectors into S3, RDS, and the broader AWS catalog.

How it works: Search stays IAM-bound, KMS-encrypted, and CloudTrail-logged by default, with pricing that scales alongside query volume.

Strengths:

• Native, low-friction integration for teams whose data already lives inside AWS
• Compliance posture that fits neatly into an existing AWS-centric governance model

Limitations:

• Execution is almost entirely service-bound and read-only by default, there's no agentic, code-executing action here
• Pricing that scales with query volume can surprise teams coming from flat-fee alternatives

7. Onyx

Onyx is the open-source option for teams that want Glean's core value, cross-app search and AI-generated answers, without giving up infrastructure control or committing to a closed-source, cloud-only platform.

How it works: Onyx connects to more than 40 workplace tools, inherits source permissions from those connected systems, and is fully self-hostable, with the source code available to inspect and modify directly.

Strengths:

• Full infrastructure control and source-code auditability, a genuine fit for data-sovereign or cost-constrained teams
• No six-figure annual commitment required to get started, with a free, self-hosted community tier available

Limitations:

• Self-hosting means a team owns the operational burden a managed SaaS platform would otherwise absorb
• Best suited to teams with the technical capacity to run and maintain it themselves

How to choose

The right pick depends on what's actually missing right now, action beyond search, deep ecosystem integration, full infrastructure control, or simply a cheaper way to get Glean's core value. A few starting points:

• Need AI to reason across systems with an auditable trail, not just retrieve documents: PromptQL
• Need AI to actually execute, reading and writing to real systems: Gumloop
• Already deep in Microsoft 365 and Azure: Microsoft Search
• Already standardized on Google Cloud and want deep customization: Google Vertex AI Search
• Have the engineering team to build and tune your own relevance model: Elastic Workplace Search
• Already running on AWS and want a managed, native search service: AWS Kendra
• Want Glean's core value without vendor lock-in or per-seat cost: Onyx

Conclusion

Search alone isn't the differentiator it used to be. The tools that matter now either close the gap between finding information and acting on it, or they let a team keep full control over how and where that search actually runs. Pick based on which problem is actually costing time: not enough action, too much ecosystem friction, or too little control.

Frequently Asked Questions

What are the leading alternatives to Glean for enterprise AI search and knowledge management in 2026?

Gartner lists Google, Microsoft, AWS, Elastic, and Algolia as top all-time alternatives. The 2026 market also includes agent-first platforms like Gumloop and PromptQL, which shift focus from indexed retrieval to multi-step reasoning, code execution, and direct writeback to business systems.

How do top Glean competitors compare on security, data residency, and compliance like SOC 2 and HIPAA?

Security posture varies sharply across platforms. Key differences include: - Gumloop: advertises SOC 2 Type II, HIPAA readiness, SSO+SCIM, Zero Data Retention, VPC deployment, and per-user audit traces. - PromptQL: says it runs in your cloud with GDPR and ISO 27001 readiness, offering dedicated VPC or BYOC. - Microsoft and Google: tie identity to their own IAM fabrics.

Which Glean alternatives offer agentic AI capabilities like multi-step reasoning and code execution without training on proprietary data?

Architectural approaches differ fundamentally between the two agent-first platforms: - Gumloop: operates as an agent-first platform with multi-step reasoning, live data pull and writeback, and code execution that creates shareable HTML artifacts. - PromptQL: uses an agentic semantic layer where the LLM plans across data backends but does not generate the final answer directly, avoiding black-box retrieval.

What is PromptQL and how does its approach using an agentic semantic layer differ from Glean’s retrieval-augmented generation?

PromptQL is an agentic semantic layer that sits on top of existing data backends like Slack, Snowflake, and Salesforce CRM. Instead of retrieving and summarizing documents, it uses LLMs for planning and generates auditable reasoning trails, persistent artifacts, and a shared wiki with full revision histories.

What are the most important evaluation criteria for an enterprise AI platform that avoids black-box answers and hallucination?

Prioritize auditable reasoning trails, an architecture where the LLM plans but doesn't hallucinate the final output, end-to-end scoped access across retrieval and writeback, and per-user audit traces. Persistent artifacts with revision histories and policy-controlled external sharing also prevent transient chat from being trusted as institutional memory.

How do Glean alternatives handle shared wikis, persistent knowledge artifacts, revision histories, and audit trails?

Gumloop generates live HTML artifacts with external sharing links and revision histories. PromptQL Tag builds a shared wiki as it works, including suggestions, page-change notifications, and a full audit trail. These models turn agent output into durable company assets rather than chat replies that disappear into scrollback.