AI tools are everywhere, but most teams are still asking the same two questions. How do we actually use this in our daily work? And which platform fits our workflow best?

Glean and VectorShift answer that in very different ways.

• Glean promises an intelligent assistant for your entire company, turning scattered files into reliable, permission-aware answers. 

• VectorShift takes a different route, giving builders the tools to design full AI workflows, automate decisions, and deploy custom agents without writing code.

Both are powerful. Both are built for work.
But only one will fit how your team actually operates.

In this guide, we will break down the real differences (beyond the marketing) so you can decide where to place your bet.

And if you're short on time, here’s a TL;DR for a quick side-by-side.

Glean vs VectorShift: Quick Comparison

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1. Platform Overview

1.1 Glean: The Knowledge Layer for Large Organizations

Glean is built for teams that have too much information spread across too many tools. It helps employees find exactly what they need, no matter where it lives. 

What sets Glean apart is how deeply it integrates with an organization’s existing data stack. It searches documents, understands roles, permissions, and organizational context to show each person only what they’re allowed to see. 

This makes it far more reliable than generic AI chatbots, especially in companies where data sensitivity and security are important. However, it’s less focused on task automation or logic flows. 

Keeping these pros and cons in mind, Glean is best suited for two core use cases:

• Enterprise-wide document search: Employees can search across tools like Slack, Notion, Confluence, and Drive using a single interface. No need to guess where a file or message lives.

• Internal Q&A assistant: Instead of asking coworkers or pinging support teams, employees can query the assistant for company-specific answers, and receive responses grounded in access-controlled, up-to-date content.

Now, let’s see where VectorShift stands. 

1.2 VectorShift: The No-Code Operating System for AI Workflows

Unlike Glean, VectorShift isn’t solely focused on helping you find information. It is a no-code platform where anyone can build AI-powered tools that automate tasks, connect data, and make decisions. 

Instead of giving you a fixed assistant, VectorShift gives you building blocks (called nodes) that let you define exactly what happens step by step: take user input, fetch data, call an LLM, apply logic, hit an API, and send the result wherever it’s needed. 

Thanks to this flexibility, VectorShift’s flexibility supports a range of high-leverage use cases, such as,

1. Custom AI agents: Build agents that can take inputs, query a vector store, generate a structured summary, and send results to your CRM, Slack, or email, autonomously.

2. Automated operational workflows: Replace manual workflows like lead enrichment, competitor monitoring, or weekly reporting with fully automated, logic-driven pipelines.

3. Branded search & chat interfaces: Deploy search or chat widgets on websites, support portals, or intranets, each powered by your internal knowledge base and fully styled to match your brand.

4. Multi-step API orchestration: Call external APIs, run conditionals, transform the response, and use LLMs only when needed, combining reasoning and automation without writing backend code.

That makes it incredibly flexible for use cases beyond just chat, like research assistants, report generators, or automated scrapers. 

In short, where Glean helps you find answers, VectorShift helps you create solutions.

2. Search Interface Capabilities

2.1 Glean: Search-First by Design

As we saw in the platform overview, Glean’s core strength lies in its search interface. 

Everything, from its AI assistant to app integrations, is built on top of a powerful enterprise search layer. It connects with tools like Slack, Google Drive, Confluence, Jira, and more to provide a unified search experience. 

The results are personalized, permissions-aware, and deeply integrated into the user’s role and team context. For large organizations where “where something is stored” is a daily question, Glean’s search experience can solve a lot of headaches.

However, it’s not a customizable search engine in the traditional sense. 

You can’t build branded portals, adjust scoring behavior, or deploy it outside the core app. It’s optimized for internal document discovery and controlled access, not for public interfaces or tailored workflows. 

It works best when used as-is, within Glean’s broader assistant interface.

2.2 VectorShift: Fully Customizable Search on Your Own Data

VectorShift includes a search interface too, one that’s highly configurable. Search can run over any pipeline, knowledge base, or document collection, allowing teams to create domain-specific assistants or standalone discovery tools with full styling control.

More importantly, you can control almost everything. Branding, knowledge base, LLMs, response style, deployment, and much more.

To create a custom search interface in VectorShift, follow these seven steps:

(Step 0: Add the data you want your search to work on in “Knowledge Base” from sidebar)

1. Navigate to the "Search" section in the sidebar under the "Interfaces" tab

2. Click “New” to open the setup dialog and name your search

3. Select a knowledge base or pipeline as your search source

4. Customize configurations like AI summaries, preview text, relevancy scores, and assistant responses

5. Style the interface. Add branding, change fonts, welcome images, colors, and more

6. Enable deployment with options like public link sharing, SSO, or password protection

7. Embed the search widget into your site using iFrame or share via direct link

8. This flexibility turns VectorShift’s search into not just a query tool, but a deployable interface for internal teams, customers, or even partners.

So, yes, Glean excels in giving you one perfect internal search. But VectorShift gives you the power to build as many as needed, for as many contexts as required.

3. Approach to AI Integration

3.1 Glean: Reliable Answers Through Retrieval, Not Imagination

Glean’s AI integration is designed with one priority: trust. Rather than letting large language models guess answers from memory, Glean feeds them real, permission-aware data at the moment of the query. 

This structure, called Retrieval-Augmented Generation (RAG), means Glean doesn’t rely on memory or guesswork. Every answer is traceable to a source, which is essential in high-stakes environments like legal, finance, or compliance. 

However, that same structure also makes it rigid. 

The AI behaves predictably, but it’s limited to searching and summarizing within existing content. You can’t use it to trigger workflows, manipulate data, or interact with external systems. It’s optimized for consumption, not execution. That’s a strength in some orgs, but a ceiling in others.

3.2 VectorShift: AI as a Building Block, Not Just an Assistant

VectorShift treats LLMs very differently. Instead of limiting models to a search-and-answer role, it allows builders to place them anywhere inside a larger workflow. 

You can use LLMs to summarize documents, generate structured outputs, process forms, analyze tables, or chain decisions across multiple steps. 

Each LLM call is fully customizable, from the prompt and system behavior to the AI model and response format. For example, look at this example below,

And since it's built around a pipeline architecture, AI is just one of many interchangeable nodes. This makes it incredibly powerful for operational use cases where reasoning, action, and multi-step logic are all required. 

4. Customization & Use Case Flexibility

4.1 Glean: Fixed Functionality with Selective Customization

Glean gives teams just some room to tailor the experience, but within a well-defined box. The platform is designed to keep things predictable, which means customization focuses on how the assistant responds, not how it operates. 

Instead of building workflows, you’re just configuring assistants to match specific roles or datasets.

Here’s how Glean handles customization:

• Tone and Voice Control: Teams can adjust how formal, casual, or concise the assistant sounds.

• Source Restriction: You can limit responses to specific files, folders, or tools (like Jira, Notion, or Drive).

• Glean Apps: Pre-configured assistants can be created for departments (e.g., HR bot or Sales bot) with distinct behaviors.

• No Workflow Logic: You can’t chain logic, run conditions, or pass data between steps. Everything is a one-shot answer.

• Permission-Aware Responses: Customizations respect enterprise access levels, so users only see what they’re allowed to see.

Glean is a powerful tool when the problem is “people can’t find things,” but it’s not designed for open-ended problem-solving or task automation. 

If your use case starts resembling a workflow, Glean’s boundaries start showing fast.

4.2 VectorShift: Full Creative Control Without the Code

VectorShift is designed for people who want to build (and not just configure). Every interaction is customizable, every step is programmable, and there’s no ceiling on complexity. 

You’re not locked into predefined actions or linear question-answer formats. Instead, you construct full pipelines, each node handling a specific piece of logic, from reading files to triggering AI, calling APIs, or formatting structured outputs.

Here’s what VectorShift enables:

• Modular Workflow Building: Use drag-and-drop nodes to build pipelines with branching logic, loops, or conditional paths.

• Multi-Channel Deployment: Export to Slack, WhatsApp, web forms, voicebots, APIs, or embed as a widget.

• Prompt + System Control: Set system instructions, select among various AI model providers (OpenAI, Anthropic, Meta), their various AI models (GPT-4o, GPT-4o mini, Claude, etc), token limits, and more.

• Connect to Any Data: Upload files, scrape websites, call APIs, or integrate live with Google Drive, Notion, Airtable, and more.

• Advanced Logic Tools: Use nodes for filters, merges, SQL generation, and even inject Python code for edge cases.

VectorShift is perfect for teams that need flexibility, autonomy, and the ability to adapt fast, whether they’re building customer-facing tools or streamlining internal ops. 

In essence, VectorShift turns AI into infrastructure.

5. Deployment and Scale Strategy

5.1 Glean: Built for Organization-Wide Rollouts

Glean is designed with enterprise-scale deployment in mind. It assumes a formal IT environment and a need to support hundreds or thousands of users with minimal friction. 

Everything from indexing to permissions to Slack integration is built to mirror how large organizations function. 

Because of this, the deployment process is more structured and deliberate, but it results in a highly secure, compliant, and consistent experience across the org.

Deploying from Glean typically follows these steps:

• Step 1 – Connect Core Data Sources: Integrate tools like Google Workspace, Slack, Jira, Notion, Salesforce, and more.

• Step 2 – Enforce Permissions & Governance: Set up user access policies, SSO, role-based visibility, and data retention rules.

• Step 3 – Enable LLM Provider & Hosting Options: Choose between Glean’s hosted OpenAI key, Azure OpenAI, or bring-your-own-model.

• Step 4 – Roll Out via Slack, Chat, or API: Deploy Glean Assistant inside Slack channels, internal chat hubs, or make it available as a Chat API.

• Step 5 – Monitor with Admin Tools: Track adoption, query trends, app usage, and performance via built-in dashboards.

Because Glean operates as a centralized assistant with deeply integrated knowledge of your systems, scale doesn’t degrade performance. In fact, the more data it has, the more valuable it becomes. 

But that comes at a cost: deployment can be heavy upfront. Implementation often requires IT involvement, onboarding sessions, and ongoing admin management.

5.2 VectorShift: Deploy Fast, Scale on Your Terms

VectorShift lets anyone, from operators to growth teams, build and ship functional AI tools without needing to involve IT or wait on procurement. 

Deploying through VectorShift typically looks like this:

• Step 1 – Build a Pipeline: Use no-code nodes to define the logic (input, LLM prompts, conditions, APIs, output).

• Step 2 – Test Directly in Builder: Run the pipeline inside the editor, adjust prompts, logic, or tokens as needed.

• Step 3 – Export the Output: Choose deployment mode like chatbot, form, voicebot, or API.

• Step 4 – Embed or Share: Generate a public URL, iFrame snippet, or Slack integration using OAuth or API key.

• Step 5 – Secure as Needed: Add password protection, SSO auth, or restrict access via deployment settings.

This makes it perfect for bottoms-up adoption. Whether you’re automating a personal workflow or launching a lightweight internal tool, you don’t need permission from IT to get started.

VectorShift’s composability makes it scale-friendly. 

You can clone, fork, or chain workflows. You can run multiple projects in parallel, each with its own logic, deployment method, and target audience. 

It scales horizontally, by empowering many builders, not vertically through centralized architecture. That flexibility is a double-edged sword, though: without thoughtful governance, it’s easy to create redundancy, inconsistency, or sprawl.

In short, Glean is built to scale from the top down. VectorShift is built to scale from the bottom up. One secures alignment across an entire company. The other enables rapid execution across many moving parts.

6. Pricing and Predictability

6.1 Glean: High Stakes, High Value, Low Transparency

Glean does not publicly disclose its pricing. Instead, access is gated behind demos, sales calls, and enterprise contracts. There is no free tier, no trial, and no way to explore the product without talking to a sales rep. This makes early experimentation difficult, especially for smaller teams or departments testing AI adoption.

That said, Vendr, a credible software marketplace provides some visibility into typical pricing. Based on data from 85 purchases, the median contract value for Glean is $62,400/year, with actual deals ranging from $21,740 to $144,977 depending on company size, seat count, and features.

While that may reflect strong value at scale, it also means:

❌ No way to estimate cost without a sales conversation

❌ No self-serve tier to test or prototype workflows

❌ No published information on usage-based pricing, per-user charges, or overages

❌ No visibility into what’s included in different contract bands

Glean is designed for companies ready to commit, not explore.

6.2 VectorShift: Transparent, Modular Pricing Designed for Builders

VectorShift takes the opposite approach. Pricing is public, modular, and usage-aligned, allowing teams to start small and scale as they grow. 

There’s no ambiguity. There are three clear tiers and incremental overages that map directly to product usage.

The three available pricing tiers are:

1. Starter (Free): 1 pipeline, 1 chatbot, 1 knowledge base, 1 integration, 1 GB storage, 1,000 non-AI actions/month

2. Premium ($25/month): 5 pipelines, 5 chatbots, 5 integrations, 3 GB storage, 10,000 non-AI actions/month, 1 shared user

3. Pro ($125/month + usage-based add-ons): 100 pipelines, 100 chatbots, 10 integrations, 10 GB storage, 50,000 actions/month, 100 shared users, advanced support

Usage beyond tier limits is charged incrementally, $0.01 per extra pipeline, $0.00001 per stored vector, $0.04 per additional GB, etc.

This structure makes VectorShift ideal for iterative teams. You can test ideas for free, scale projects when they show value, and never wonder what the next billing cycle will look like. 

Glean vs. VectorShift: What’s Best For You?

Choosing the right tool isn’t about which one is better overall. Think about what’s better aligned with your team’s workflow, control needs, and rollout maturity.

If your primary challenge is navigating fragmented internal knowledge, and you need answers grounded in enterprise permissions, and org-wide governance, Glean is unmatched. It’s purpose-built for companies where accuracy and access control matter more than customization.

What sets VectorShift apart is that it doesn’t abstract away the AI. It lets you define exactly how it fits into your workflows. You choose the model, define the inputs, control the outputs, and configure every logical step in between. 

VectorShift also avoids the common trade-off between power and accessibility. You don’t need to write code, but you’re not locked out of customization. Whether it’s a chatbot powered by 10 steps of logic or a data-enrichment agent that scrapes, analyzes, and posts results, VectorShift handles it. 

That makes it especially useful for teams who need AI to work like software, not just talk like a human. To know more about how VectorShift can help you, book your no-cost demo now.