fastgpt vs dify

Measured side by side

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 fastgpt if

• Retrieval accuracy is what you are buying; six points separates them and Dify came last of five
• You cannot depend on outbound internet — Dify ships with no model providers and installs them from a marketplace
• You want fewer moving parts in the model path; Dify routes models through a plugin system with its own cache

Pick dify if

• You are building agents and workflows, with search as a supporting feature
• Start-up time matters: 39 seconds against 239
• Memory is tight: 2.6 GB against 3.5 GB, despite Dify running two more containers

They are identical where it is easiest to differ

Both ingested all thirty-six documents. Both retained 100% of the 180 facts planted across the corpus. Both expose their stored chunks, so both figures are exact rather than approximated.

That leaves two dimensions that actually separate them.

Retrieval: FastGPT by six points

74% against 68%. Dify came last of the five platforms measured on this corpus, which is worth stating plainly because it led our previous run.

The chunk counts do not explain it this time. Dify stored 4,263 chunks against FastGPT’s 1,198 — more than three times as many — and retrieved less. On our earlier corpus of large documents that relationship ran the other way and we reported it as a finding; it did not survive this run, and we have retracted it. Finer chunking helps when a document is large enough to bury an answer inside one chunk, and stops helping when documents are the size of an ordinary wiki page.

Operations: Dify

Dify is quicker to start and lighter to run while operating two more containers. If you are creating and destroying instances, six times faster to first request is a real difference.

What Dify costs you that FastGPT does not

No model providers at all. Every one is a plugin fetched from an external marketplace, so a Dify with no outbound internet cannot reach a model of any kind. FastGPT has no such dependency.

A cache that hides a successful install. Installing a provider plugin does not invalidate Dify’s cached provider list: the install task reports success, the database carries the records, the plugin daemon returns the model declarations, and the console keeps returning an empty list. Automating Dify means knowing to clear that cache, because no API does it.

State that survives a reset. docker compose down --volumes does not reset Dify — its data lives in host-mounted directories that named-volume removal leaves alone. A re-run silently inherits the previous database unless you delete those directories yourself.

What FastGPT costs you that Dify does not

A slower start and more memory, as above.

A two-system model setup. Models are configured on a bundled gateway with its own token and its own Postgres instance, then activated separately in FastGPT’s own catalogue. Doing one without the other fails at knowledge-base creation with an error that mentions neither.

An endpoint that lies about success. FastGPT’s model-activation call returns success on a partial update without persisting it. Any client that trusts the response silently misconfigures the instance; ours verifies final state instead.

Licences: neither lets you resell

FastGPT’s licence permits commercial use but prohibits offering the software as a service. Dify’s adds conditions on top of Apache 2.0. If you need to host either for customers, look at RAGFlow, which is Apache 2.0 with no such restriction.