Glean recently raised $150 million in Series F financing to reach a $7.2 billion valuation. The company reports it surpassed $100 million in annual recurring revenue in less than three years and aims to support 1 billion agent actions by the end of 2025. While Glean positions its platform as a central layer for enterprise knowledge, the architecture requires users to trust Glean with their data. Glean uses a proprietary "Enterprise Graph" to encode organizational context, which remains non-portable. You cannot export your indexed data or choose a different vector database if you leave the platform. This creates a dependency because leaving Glean means rebuilding your entire knowledge layer from scratch.
Deployment constraints also separate Glean from competitors like Agora. Glean remains a cloud-native platform with no true on-premises or air-gapped option for regulated industries like defense or healthcare. You must maintain a relationship with a cloud provider to use the service. In contrast, Agora allows users to deploy with a single Helm command for full on-premises isolation. Glean also limits model choice to a curated set including OpenAI GPT, Anthropic Claude, and Google Gemini. You cannot bring your own fine-tuned models or use open-source models like Llama or Mistral.
| Feature | Glean | Microsoft Azure AI | Google Vertex AI |
|---|---|---|---|
| Deployment | Cloud-only | Hybrid/Cloud | Cloud |
| Model Choice | Curated (GPT, Claude, Gemini) | Custom/Open | Custom/Open |
| Pricing Model | Per-user subscription | Consumption/Capacity | Usage-based |
| Minimum Seats | Approximately 100 | No minimum | No minimum |
Deployment costs and agent economics
Glean does not publish a list price for its Core Suite. Procurement requires sales engagement, and median contracts sit around $98,100 per year. Industry reports suggest per-user costs start above $50 to $75 per month. You also face potential implementation fees, custom integration costs, and 3% to 7% annual price escalations. Glean Enterprise Flex includes a pooled allowance of FlexCredits, but premium-model queries and every agent run consume these credits.
Comparing Glean to Google Gemini reveals different quota structures. Google Gemini Enterprise Standard provides 1,120 queries per seat per week, which covers every type of query. Glean Enterprise Flex provides unlimited Fast Mode queries for simple lookups, but it limits reasoning-mode queries on standard models to 100 per user per week. If your employees primarily perform reasoning tasks, Google gives eleven times more headroom per seat before it meters usage.
The ROI of AI agents varies by how organizations deploy them. A Google Cloud study of 3,466 senior leaders found that 88% of agentic AI early adopters report ROI on at least one use case. These early adopters dedicate at least 50% of their future AI budget to agents. In the customer service sector, these leaders report 43% ROI compared to a 34% average for all organizations. Google Cloud also reported that 74% of executives achieve ROI within the first year of generative AI deployment.
Integration and infrastructure tradeoffs
Google Cloud and Microsoft Azure leverage existing productivity suite dominance to drive adoption. Google Cloud’s Gemini 3 launch aims to embed AI within existing workflows, and the company offers Gemini 3 Pro at $2 per million tokens. This price is 60% lower than OpenAI’s pricing. Google also offers the Agent Developer Kit, which allows users to build agents using Gemini or third-party models like Meta’s Llama 4 or Anthropic’s Claude 3.7.
Microsoft Azure AI functions differently because it lacks a single platform fee. You pay for underlying compute, storage, and networking services. When you add Azure Logic Apps or API Management, you face additional consumption charges per workflow action or capacity meter. Users often combine these services to create an integrated estate. This makes the total cost of ownership dependent on specific meter volumes rather than a simple seat count.
Glean provides a advantage with its library of over 100 pre-built connectors for tools like Salesforce, Slack, and Jira. It inherits existing permissions by pulling access-control lists from these source systems. If you lack permission for a file in Google Drive, Glean will not surface that file in your results. This prevents the tool from bypassing the security walls your organization already established.
Do companies prioritize the breadth of pre-built connectors or the ability to host their own models on-premises?
Microsoft users often report high satisfaction with deep integration into Azure data and identity services. However, users of Azure AI point to the complexity of managing distributed pipelines. Google Cloud Vertex AI users face a steep learning curve and must manage costs that can spiral as usage increases. Glean remains a polished solution for those who prefer a managed service over building their own infrastructure.
