dify vs ragflow
Short answer
RAGFlow if the knowledge base is the product or you need to resell it — it retrieved 73% against Dify's 68% and is Apache 2.0. Dify if it is one part of an agent platform and you cannot spare ten gigabytes of memory for Elasticsearch.
Measured side by side
| Measurement | dify | ragflow | Better |
|---|---|---|---|
| Time to first served request | 39 s | 511 s | dify |
| Containers | 15 | 5 | ragflow |
| Peak memory during ingestion | 2,619.7 MB | 9,959.6 MB | dify |
| Parsing fidelity | 100% | 99% | dify |
| Retrieval accuracy | 68% | 73% | ragflow |
| Retrieval latency (p50) | 298.6 ms | 414.7 ms | dify |
Both were run on the same host against the same corpus with the same embedding model, so these figures are directly comparable. Everything behind them is in the raw data.
Pick dify if
- Memory is constrained: 2,620 MB against 9,960 MB for the same corpus
- Instances start and stop often — 39 seconds to first request against 511
- You are building agents and workflows, with retrieval as one component
Pick ragflow if
- You need to redistribute or resell the deployment; RAGFlow is Apache 2.0 and Dify's licence is not
- Retrieval accuracy is what you are buying — five points, and Dify came last of five platforms
- You want fewer moving parts: five containers against fifteen, and no plugin marketplace in the model path
Accuracy: RAGFlow, narrowly
| Dify | RAGFlow | |
|---|---|---|
| Documents ingested | 36/36 | 36/36 |
| Parsing fidelity | 100% | 99% |
| Retrieval accuracy | 68% | 73% |
Parsing is a tie in practice. Retrieval is five points apart, and worth noting because Dify came last of the five platforms on this corpus — a reversal from our previous run, where it came first on a corpus of much larger documents.
Operations: Dify, by a wide margin
| Dify | RAGFlow | |
|---|---|---|
| Peak memory | 2,620 MB | 9,960 MB |
| Ready to serve | 39 s | 511 s |
| Corpus ingestion | 21 min | 22 min |
| Containers | 15 | 5 |
Nearly four times the memory and thirteen times the start-up, for five points of retrieval. RAGFlow bundles Elasticsearch, which accounts for most of that.
Note that the direction of the container count runs opposite to the memory: RAGFlow runs a third as many containers and needs four times the RAM. Neither number predicts the other.
The licence decides it for one group of buyers
RAGFlow is Apache 2.0 with no restriction on offering it as a service. Dify’s licence adds conditions that prohibit exactly that.
If you intend to host this for customers, that settles the comparison regardless of the memory. If you are running it internally, it costs you nothing.
Dependencies each brings
Dify ships with no model providers. They are plugins fetched from an external marketplace, so an air-gapped Dify cannot reach a model at all. Installing one does not invalidate its cached provider list, so the console keeps reporting an empty list after a successful install until the cache is dropped — and no API drops it.
RAGFlow validates a model endpoint at registration and refuses if it cannot reach it, which surfaces a misconfiguration immediately rather than at first use. Against that, it answers HTTP 200 with a non-zero status code in the body on failure, and uploading a document does not start parsing.