HyperLake vs Shakudo: which deployment fits your AI platform?

How should you evaluate the operating model?

An infrastructure platform is not the same thing as a finished business application. It may provide a way to run models and data services, but your team still needs to identify the workload, define acceptance criteria and decide which systems connect to it. A platform demonstration should show the workflow that matters to you, not only a component catalog.

Use a representative but bounded evaluation: approved data enters, a model processes it, a user receives a result, and an operator reviews the evidence. Include a denied request, a failed dependency and a restore. These checks expose operational boundaries that an ideal-path demonstration may omit.

Ask who can change a model version and how the previous version is restored. Check whether a service can bypass the intended access gateway. Inspect where traces, prompts, backups and support bundles go. Confirm how temporary administrative privileges are granted and revoked. The answers should be specific to the proposed deployment.

Ask each supplier to diagram every service that handles workload data, including identity, monitoring and support paths. Then walk through a normal deployment and one recovery scenario. For disconnected sites, ask how signed updates arrive, how the team knows they are complete, and what happens if an update fails halfway through. For connected environments, establish which services require outbound access and who can approve it.

Clarify which part of the system your operators will own. A platform can package common operations, but responsibility for identity configuration, model approval, incident response and application quality still needs an owner. Put those responsibilities in the implementation plan rather than assuming they follow from a product label.

Which option fits which team?

If local or disconnected operation is mandatory today, Shakudo's published deployment scope makes it the more relevant of these two to evaluate. Ask it to demonstrate the exact topology, update method, recovery process and support path you need. Vendor descriptions are a starting point for that evaluation, not proof of a particular installation's behavior.

HyperLake is relevant when the customer's AWS or Google Cloud account meets the required boundary and the team wants to assess a managed foundation for open-source data and AI services. Confirm which services are included, how identity and access are configured, and what remains the customer's responsibility. Do not treat future Azure, OVH, private-cloud or on-premises availability as current capability.

Avoid treating the number of available tools as a measure of fit. A short list of evaluated components can be more useful than a large catalog when the workload depends on a particular model, identity provider and database. An established component integration may save work if it meets a real requirement. Test which case applies.

Some buyers may need to compare a platform with a managed application or a direct model service instead. If the workload is narrow and the supplier's application already meets the data boundary, support and acceptance criteria, operating a broader infrastructure layer may add work without changing the result. Conversely, a reusable foundation may be worth evaluating when several workloads share identity, data services and deployment controls. Compare the full operating scope, not just the first demonstration.

Where does each platform fit better?

• Shakudo: Evaluate it when a local, disconnected or air-gapped deployment is a current requirement. Its public materials describe those deployment modes; validate them with a demonstration for your topology and support needs.
• HyperLake: Evaluate it when customer-account AWS or Google Cloud operation meets the requirement and you want to assess a managed foundation for open-source data and AI services.

What should a practical evaluation measure?

Measure the outcomes that matter to your application: accepted task quality, response time under expected concurrency, recovery behavior and effort to operate the environment. Do not substitute an unrelated vendor benchmark for your acceptance criteria. Keep a record of test inputs, versions and configuration so the results can be repeated.

For procurement, request a written scope covering infrastructure, storage, networking, software, support and engineering time. Distinguish platform charges from the cloud bill and from the cost of the domain application. Ask whether capacity can actually scale down, including underlying compute, rather than only whether a serving endpoint can stop accepting work.

The LLM API vs self-hosted GPU cost calculator can help compare a token-based API with dedicated capacity using your inputs. It does not compare platform license offers or prove equivalent answer quality.

Ask for the proposed service boundary in writing: what is included in the platform, what is billed by the cloud provider, which third-party tools have separate licenses, and what ongoing engineering work remains. Use the same workload assumptions in both proposals. If a supplier cannot provide a comparable estimate, record the unknowns rather than treating an absent price as zero.

For either evaluation, test the evidence and governance path as well as the successful inference. Confirm that an authorized user can see which model and configuration produced a result, that a rejected access request is recorded, and that a previous version can be restored. The exact checks depend on the application, but writing them down before the demonstration makes competing proposals easier to assess.

What should a migration plan include?

1. Inventory the workloads, datasets, models, licenses and external services in scope.
2. Separate mandatory location and connectivity requirements from preferences.
3. Map current identity groups and service accounts to proposed permissions.
4. Export a small representative dataset and verify formats, lineage and retention rules.
5. Run the same evaluation against the current and proposed environments.
6. Test rollback, restoration and revoked access before moving production traffic.
7. Agree who owns updates, incidents, capacity planning and support escalation.

Migrate in a reversible slice. Running both environments briefly can simplify reconciliation, but also creates extra copies and access paths. Document temporary arrangements and remove them when the transition is accepted.

When is HyperLake the wrong choice?

Do not select HyperLake on the assumption that Kubernetes portability means every deployment location is available today. Current availability, a future roadmap and a technically possible architecture are separate things. If procurement requires on-premises delivery now, treat that as a pass-or-fail criterion and assess currently available offers.

It is also reasonable to avoid either infrastructure platform when the actual requirement is a finished domain application. An operating environment does not automatically provide the application's workflow, training data, acceptance tests or business ownership. Keep those costs and responsibilities visible in the proposal.

Frequently asked questions

Sources

Facts about other products come from the pages below, last checked October 31, 2026. Products change; check the source before you decide.

1. Hyperlake FAQ checked October 31, 2026
2. Hyperlake Get started checked October 31, 2026
3. Hyperlake Use cases checked October 31, 2026
4. Shakudo overview checked October 31, 2026
5. Shakudo Platform checked October 31, 2026
6. Shakudo FAQ checked October 31, 2026

Related guides

• Physical AI deployment guideWhat a Physical AI stack needs, why sovereignty matters for robot data and models, and how Hyperlake deploys and governs the stack in your environment.
• LLM API vs self-hosted GPU cost calculatorCompare monthly LLM API and self-hosted GPU costs using measured tokens, throughput and prices. Model, GPU and region labels identify each scenario.