Neurifly vs Make

Make is visual automation billed in credits, with a broad catalogue of ready applications. Neurifly is an agent platform where the models, the approvals and the audit trail sit on the same account as the run.

Make counts credits, where a single module action consumes one credit, with a free tier and paid tiers that buy larger allowances. It is a mature visual builder with a very large application catalogue, and for classic system to system integration it is an efficient answer. A Make alternative is worth considering when a scenario starts to rely on a language model and someone has to account for the outcome. Neurifly treats that as the primary case. Models are purchased once for the organization instead of a key per team, and their cost appears on the same invoice as the run. Irreversible steps are gated behind human approval. Each run is hash chained and accompanied by an evidence pack recording what it read, wrote and spent. Execution of generated code is contained in a sandbox without network access, and everything is hosted in the European Union.

What you are comparingNeuriflyApptivityMakeMake
A seat that carries a usage limit worth more than it costsConfirmed14 euro per seat a month carries a 20 euro monthly usage limit, about seven times what an ordinary person consumes. Past it work carries on and is charged from a balance you top up, with a budget per API key. One person can stay on usage only for ever.In partPer credit, where one module action is a credit: 1,000 a month free, then 9 dollars a month for 10,000.source
Runs in the European Union without changing planConfirmedEuropean Union by default, on every plan. There is no region to switch on.In partMake publishes its regions and data handling on its own pages.source
Models from more than one vendor on one accountConfirmedModels from several vendors, including open weight ones, picked per conversation and billed on one account.In partConnect whichever provider you like and pay that provider separately.source
Every call recorded with its tokens, latency and costConfirmedAn append only trail with the model, tokens, latency, cost and the key that made the call. Agent runs are hash chained.In partScenario history is kept in the account.source
A budget per key, refused before the spendConfirmedA limit per key and per organization. The call is refused before it goes past the limit rather than invoiced after.In partThe plan caps how many credits a month are included.source
Attach files to a conversationConfirmedDocuments and images go into the conversation and land in a knowledge base you control.UnconfirmedSee the vendor's own pages.source
Answers can search the webConfirmedSearch is a platform service: the organization picks the engine, results are redacted before the model sees them, and every query is priced and recorded.UnconfirmedSee the vendor's own pages.source
Single sign on for the whole companyConfirmedSign in with the company account, with roles and a permission catalogue enforced by every service rather than by the interface.UnconfirmedSee the vendor's own pages.source
Several models answer the same question at onceConfirmedFusion sends the question to a panel of models in parallel and a separate model synthesises the result.UnconfirmedSee the vendor's own pages.source
Every source opens on the passage it came from, with its page and relevanceConfirmedSources are numbered from what retrieval actually returned, and each opens the passage with its page number and its relevance score.UnconfirmedSee the vendor's own pages.source
You choose what a conversation may read: a base, folders, or single documentsConfirmedRetrieval is pointed at a knowledge base, at folders in it, or at named documents, and access is granted per base and per group.UnconfirmedSee the vendor's own pages.source
Code the model writes runs in a sandbox with its network cutConfirmedThe runner installs a filter on itself that cuts the network, files it produces come back to you, and the run is recorded like any other action.UnconfirmedSee the vendor's own pages.source

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Last updated 4 August 2026

Start with a question, not a project.

Create an account, ask something real, and look at what the trail recorded. That is the whole evaluation.