First-time evaluator: How do I judge if LlamaIndex is 'good'?

Alright, so I'm coming at this from the infrastructure side. I've got my RAG apps running in k3s clusters, usually with a simple FastAPI frontend and maybe some Istio for routing. I've been hand-rolling a lot of the chunking and query logic.

Now I'm looking at LlamaIndex to see if it saves me time. But "good" is vague. For me, "good" means it doesn't fight my stack and actually simplifies things.

My main evaluation points:
* **Integration:** Does it play nice with my existing vector DB (usually Weaviate or PGVector in-cluster)? Can I easily containerize it?
* **Operational Overhead:** Is the abstraction leaky? If I need to debug a query, can I see what's happening without pulling my hair out?
* **Flexibility:** When I need to step outside the happy path for a custom retrieval strategy, does it get in the way?

Here's a snippet of what I'd test first—seeing if I can easily swap out a component:

```python
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.weaviate import WeaviateVectorStore
import weaviate

# Can I cleanly plug in my already-running Weaviate instance?
client = weaviate.Client("http://weaviate-svc.my-namespace.svc.cluster.local:8080")
vector_store = WeaviateVectorStore(weaviate_client=client, index_name="MyDocs")
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Build index from existing docs in the store
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
```

If this is straightforward and the performance is predictable, that's a big plus. Curious what others are using as their main criteria beyond just accuracy numbers.