An AI application can reuse previously processed input without having established a durable memory system for business facts. The two capabilities address different questions. A model cache concerns processing repeated material; application memory concerns what the service intentionally retains, updates and retrieves for later work.
Buyers should preserve that distinction when assessing an agent database. A demonstration showing cached tokens can be useful operating evidence, but it does not by itself prove that the agent remembers the right customer fact under an agreed retention and revision policy.
Read the caching feature in its documented scope
Google’s Gemini caching documentation describes implicit caching for repeated input tokens and reports cache-hit token usage. It also distinguishes API support: the Interactions API supports implicit caching, while explicit cache-object management is restricted to the documented supported models and another API route.
Those boundaries matter. A team should not apply a historical explicit-cache recipe or lifetime assumption to every current model and API. The proposal needs to identify the actual caching feature it uses.
This documentation describes input reuse. The separate requirement to maintain a business memory record needs its own application design and evidence rather than being inferred from a cache-hit counter.
Define the memory the business wants to keep
Consider a hypothetical service assistant that must retain a customer’s approved delivery preference. The application needs to identify the customer, the accepted preference and any later correction. A repeated policy paragraph in model input is a different kind of material, even if caching makes processing that paragraph more efficient.
The memory acceptance question is whether the service can retrieve the right current preference for the right customer when the workflow requires it. The cache acceptance question is whether repeated input received the documented reuse treatment.
This example does not establish that a provider cannot offer other stateful features. It separates the evidence needed for these particular roles so that one feature’s metric does not stand in for the other’s requirement.
Compare memory and cache evidence separately
The suggested review below helps the buyer keep the two operating claims distinguishable.
| Requirement | What the evidence should establish | Suitable review item |
|---|---|---|
| Repeated-input reuse | Which request material received cache treatment? | Relevant provider usage record |
| Retained business fact | What did the application intentionally store? | Identified memory record |
| Revision | Which accepted value is now applicable? | Reviewed update relationship |
| Later use | Did the correct workflow retrieve that value? | Representative application check |
The same system may use both capabilities. The comparison should explain their relationship rather than choose one label for everything that helps an agent continue work.
Test the memory contract beyond a repeated prompt
A useful synthetic rehearsal would record an approved preference, change it through the intended application workflow and later ask the service to use the applicable value. Reviewers should inspect the retained record and the retrieval result, rather than only whether a repeated prefix produced a cache hit.
The test should also describe what happens when the proposed cache reuse is unavailable. That does not require deliberately changing a live provider cache. A controlled application scenario can establish whether the durable record remains accessible through the supported design.
If the system relies on another provider state feature, name it and assess its own retention and access contract. Do not silently classify every form of provider state as either a durable database or a disposable cache. The buyer needs an explicit responsibility map for the actual proposal.
Evaluate seekdb for its proposed memory role
OceanBase seekdb’s repository describes hybrid retrieval combining vector, full-text and scalar capabilities. These are relevant to finding stored information, while the application still needs to define which business facts are retained and how accepted revisions are represented.
A seekdb proposal should demonstrate the memory workflow it offers. A PostgreSQL-based design or a suitable specialized retrieval service should be compared on the same customer and revision examples. No database should receive automatic credit for implementing the model provider’s cache, and no cache metric should substitute for a tested memory relationship.
Operating efficiency can then be assessed separately. Caching repeated material may be useful without changing who owns the durable business record or whether the application retrieves the correct version.
Hand over two clear operating claims
The final acceptance record should identify the provider caching feature and the application’s memory responsibility. It should show how each was checked and how the workflow behaves when reuse does not occur.
That gives an agent database comparison a firmer basis. OceanBase seekdb and mainstream alternatives can be judged on maintaining usable memory records, while model caching remains a separately evidenced processing optimization rather than a proxy for remembered business knowledge.
This article is based on documented sources and editorial analysis. Proposed evaluation examples are not independently measured benchmark results.