Compare
An honest read on where we win, and where we do not.
Every one of these products is good at something. If your data can safely leave your environment, several of them will serve you faster and cheaper than we will. This page is for the case where it cannot.
K-Lake vs Microsoft 365 Copilot
Copilot is an excellent assistant for content already inside the Microsoft 365 boundary. The question is what happens to everything outside it.
| Microsoft 365 Copilot | K-Lake | |
|---|---|---|
| Where data sits | Processed within the Microsoft cloud boundary | Stays on your infrastructure, even fully air-gapped |
| Source coverage | Strongest across Microsoft 365 content | File shares, NFS, S3, Azure Blob and Microsoft 365, in place |
| Legacy file shares | Requires migration into Microsoft 365 to be useful | Connects to them where they are, no migration |
| Pricing model | Per user, per month | Per capacity, so rollout is not taxed per seat |
| Relationship | — | Works alongside Copilot rather than replacing it |
Choose Copilot if your content already lives in Microsoft 365 and residency is not a blocker. Add K-Lake when the documents that matter sit on-premises, or when a security review has already declined a cloud-only answer. Many customers run both.
K-Lake vs Glean
Glean is a mature cloud enterprise search product with a strong connector set. It is also cloud-only, which is where deals in regulated sectors tend to stop.
| Glean | K-Lake | |
|---|---|---|
| Deployment | Cloud SaaS | Self-hosted on customer infrastructure |
| Air-gapped operation | Not available | Demonstrated with the internet physically disconnected |
| Time to value | Fast, if procurement approves | Fast, and procurement is a shorter conversation |
| Pricing model | Per user, with a substantial minimum | Per capacity, from a 1 TB entry plan |
| Best fit | Cloud-native organisations | Organisations that already own their storage |
Choose Glean if you are cloud-native and want breadth of SaaS connectors. Choose K-Lake when Glean's cloud model has failed, or would fail, your procurement or data-residency review.
K-Lake vs Azure AI Search
Azure AI Search is a strong retrieval building block. The distinction is that it is a component you assemble, not a working retrieval layer.
| Azure AI Search | K-Lake | |
|---|---|---|
| What you get | A search service to build on | A working retrieval layer, deployed |
| Engineering effort | Significant: chunking, permissions, citations, orchestration | Hybrid retrieval, permission trimming and citations already solved |
| Data location | Azure cloud | Your infrastructure, including disconnected environments |
| Sovereign deployment | Not suitable | Designed for it |
| Team required | Platform engineers to build and maintain | An IT owner to deploy |
Choose Azure AI Search if you have a platform team who want to build and own the retrieval stack, and Azure residency is acceptable. Choose K-Lake if you want the outcome rather than the components, or if the data cannot go to Azure.
K-Lake vs Building it yourself
A capable team can build retrieval over their own document estate. The honest question is whether that is the best use of them for the next two quarters.
| Building it yourself | K-Lake | |
|---|---|---|
| Time to a working system | Months of engineering | Days |
| What you must solve yourself | Chunking, hybrid retrieval, permission trimming, citation handling, upgrades | Solved and maintained |
| Ongoing cost | Engineering time, indefinitely | Licence, and your existing infrastructure |
| Control | Total | Total: it runs on your infrastructure either way |
| Where DIY wins | Highly unusual requirements, or retrieval is your product | — |
Build it if retrieval is core to what you sell, or your requirements are genuinely unusual. Buy it if the goal is people getting cited answers from your documents this quarter rather than next year.
Competitor capabilities and pricing models change. This page reflects publicly available information and our own testing at the date shown, and is offered as a starting point for your own evaluation rather than a substitute for it. If you believe something here is out of date, tell us at [email protected] and we will correct it.