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GLEAN AI · GLEAN ALTERNATIVE

The Glean AI Alternative That Runs Models, Agents, and Data Tooling in Your Infrastructure

Glean AI is an enterprise assistant: it indexes the SaaS and document corpus you already have and answers questions over it. Shakudo is the runtime underneath that layer. It hosts models, agents, and data tooling inside your own infrastructure, with portability across AWS, Azure, GCP, and on-premises. Calls to a hosted model still go to that provider, under your own contract, with zero retention and no training on your data.

300+
Pre-Built Components
2-4
Weeks to Deploy
5,000+
Connectors

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Quick overview of pricing, platform features, and deployment options.

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THE CHALLENGE

An Assistant Answers Questions. The Runtime Decides What Is Possible.

Glean AI does its job well: it makes a scattered corpus searchable and askable, and it saves people from hunting through tools to find an answer. What it does not decide is where your models run, which models your teams may reach, how data tooling and serving are operated, or what happens to that stack when the next model generation lands. Those stay separate decisions, and they sit underneath the assistant rather than inside it.

A Runtime, Not an Answer Layer

Shakudo hosts the models, agents, and data tooling that answers ultimately depend on: ingestion, transformation, orchestration, training, and serving in one platform you operate. A search and answer layer can sit on top of it, but the runtime underneath is not borrowed from it.

The Platform Runs in Your Environment

Deploy on Kubernetes you already run: your cloud account, another cloud, your data center, or an air-gapped network. Data the platform stores stays in your environment. A call to a hosted model goes to that provider under your own contract, with zero retention and no training on your inputs.

Identity Stays Yours

Authorization is bound to the identity provider your systems already trust, rather than a permission copy held in a vendor index. Every agent inherits its operator's access, and platform activity lands in one audit trail inside your boundary.

CAPABILITIES

The Runtime Underneath the Assistant

Integration, deployment, and identity are platform defaults, so the corpus an assistant reads is assembled and governed in the same place the models run.

ONE UNIFIED ECOSYSTEM

One Runtime for the Models and the Data Behind Them

Ingestion, transformation, orchestration, ML training, model serving, and BI managed through a single platform. The pipelines that assemble a corpus live next to the models that reason over it, rather than in a set of services assembled across vendors.

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One Runtime for the Models and the Data Behind Them
IN YOUR PERIMETER

Isolation Without Giving Up Hosted Models

Run fully air-gapped with self-hosted models, or run in your own cloud account and enable hosted models deliberately. Platform data, credentials, and logs stay inside your boundary either way, and each hosted call is made on your own provider contract.

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Isolation Without Giving Up Hosted Models
IDENTITY-BOUND ACCESS

Every Agent Inherits Its Operator's Access

Role-based permissions, policy enforcement, and a record for every request, applied across the models and data tooling in the runtime. Authorization follows the identity your systems already trust, so access review stays one conversation instead of one per tool.

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Every Agent Inherits Its Operator's Access

Shakudo vs. Glean AI

Shakudo

A runtime you operate, for models, agents, and data

  • Hosts models, agents, and data tooling in one runtime you operate
  • Runs in your infrastructure: AWS, Azure, GCP, on-premises, or air-gapped
  • Your own cloud rates and model contracts, with no per-seat platform license
  • A swappable model catalog: hosted providers or open-weight models you host
  • Operational in 2-4 weeks on Kubernetes you already run
Other platforms

An enterprise assistant over the corpus it indexes

  • Answers natural-language questions over the SaaS and document corpus it connects to and indexes
  • The product surface is the search and answer experience, not the runtime underneath it
  • The model layer is configured inside the product rather than served in your environment
  • Permissions mirror the connected sources into the product's own permission model
  • Data engineering, training, and serving around the corpus sit in tools assembled separately

Build what differentiates you. Buy what accelerates you. The employees focus on what differentiates us, and the agents help them do it faster and better.

Sagar Chikkala
Chief Information Officer
FlexiVan
120,000+
Intermodal Chassis Operated
1955
Operating Since

Frequently Asked Questions

How is Shakudo different from Glean AI?
Glean AI is an enterprise search and AI assistant product: it connects to the SaaS and document sources an organization already has, indexes them, and answers questions over that corpus. Shakudo is the runtime those answers ultimately depend on. It hosts models, agents, and data tooling in your own infrastructure, so ingestion, transformation, orchestration, training, and serving all run in an environment you operate. The two are different layers rather than substitutes: a search assistant can sit on top of a runtime like Shakudo, and a hosted model can still be reached through the runtime on the provider agreement you already hold, alongside open-weight models you serve yourself.
What stays in our environment, and what leaves it?
Platform data stays in your environment: prompts, embeddings, outputs, credentials, and logs are stored in your own databases and object storage, inside infrastructure you run. There is no Shakudo-hosted data plane holding a copy. The one deliberate exception is a hosted model call: when a workload calls a hosted provider, that request goes to that provider under your own contract, with zero retention and no training on your inputs. Self-hosted open-weight models stay entirely inside your boundary, including in air-gapped deployments.
Does Shakudo replace an enterprise search assistant?
No, and it is not built to. A search and answer layer over SaaS content is a product decision, and Glean AI is one option there. Shakudo is the layer underneath it: it runs the models, agents, and data tooling, and it gives an organization one place to govern identity, policy, and audit across them. Where a team wants retrieval and answering over its corpus, the runtime hosts the retrieval stack and the models behind it, and a search product can be added on top of that.
How long does implementation take?
Most enterprise customers are operational within 2-4 weeks. Shakudo deploys on Kubernetes you already run and connects to your identity provider, so the work is integration rather than migration: existing pipelines keep running while workloads move over.
What does pricing look like?
Pricing is based on your deployment scale and requirements. We work with enterprises of all sizes. The best way to understand pricing for your use case is to request a pricing overview through the form above.
What security attestations does Shakudo hold?
Shakudo holds SOC 2 Type II. A copy of the report is available under NDA on request. Because the platform runs in your infrastructure, its controls sit inside your own audit boundary rather than behind a vendor tenancy, and platform data residency is a deployment property you configure rather than a vendor commitment you rely on.

Own the Runtime Behind the Answers, Not Just the Answers.

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