adlass vs RAG: an honest comparison
Quick verdict
If you only need an agent to retrieve passages from a static document corpus, RAG is fine and you may not need anything more. If you need your whole team and their agents to read and write the same live files, data, and state, you need a shared data layer, not a retrieval pipeline. adlass is the layer; retrieval is one thing you can still do inside it.
RAG and adlass solve different problems, and they are often confused because both put your data near an LLM. RAG is a retrieval technique: you embed a corpus and pull matching chunks into the prompt at query time. adlass is a shared data layer: you, your team, and your agents work over the same live files, datasets, and state, connected over MCP.
Feature comparison
| RAG pipeline | adlass shared data layer | |
|---|---|---|
| Primary job | Retrieve passages from a corpus | Read and write live shared data |
| Data freshness | Snapshot at indexing time | Live, current files and state |
| Write access | Read-only retrieval | Agents and people read and write |
| Shared across team | Usually per-app, not shared | Shared Spaces by design |
| Multiple agents | Each app builds its own | One layer, many agents |
| Access control | Build it yourself | Per-Space permissions built in |
| Setup | Build and maintain a pipeline | Connect over MCP |
| Best for | Q&A over static documents | Teams and agents working on the same data |
When RAG is the right choice
If your use case is question answering over a large, mostly static corpus, such as a documentation set or a knowledge base, RAG is a good fit and adlass would be more than you need. Retrieval over embeddings is exactly the right tool there.
When you need a shared data layer instead
The moment more than one person or more than one agent needs to work on the same data, and especially when they need to write, not just read, retrieval is not enough. You need shared state, write access, and access control. That is what adlass provides, and you can still run retrieval over the data inside it.
Can you use both?
Yes. They are not mutually exclusive. Many teams keep retrieval for static reference material and use adlass as the live layer their agents and people actually work in. The honest framing is: RAG is a technique, adlass is where the work happens.
Related guides
Frequently asked questions
- Is adlass a RAG tool?
- No. adlass is a shared data layer for teams and their agents. You can run retrieval over data inside it, but its job is shared, live read and write access, not just fetching passages.
- When is plain RAG actually better?
- When you only need question answering over a static corpus and no one needs to write back or share live state. In that narrow case, a retrieval pipeline is simpler.
- Do I have to throw away my RAG setup to use adlass?
- No. Keep retrieval for static reference material and use adlass as the live layer your team and agents work in. They complement each other.
Try adlass
The shared data layer for teams and their agents.