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 comparing | Neurifly Apptivity | Make Make |
|---|---|---|
| Paid for what runs, not per seat | Confirmed A balance you top up, spent on tokens, searches, runs and storage. No seats and no minimum. | In part Per 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 plan | Confirmed European Union by default, on every plan. There is no region to switch on. | In part Make publishes its regions and data handling on its own pages. source |
| Models from more than one vendor on one account | Confirmed Models from several vendors, including open weight ones, picked per conversation and billed on one account. | In part Connect whichever provider you like and pay that provider separately. source |
| Every call recorded with its tokens, latency and cost | Confirmed An append only trail with the model, tokens, latency, cost and the key that made the call. Agent runs are hash chained. | In part Scenario history is kept in the account. source |
| A budget per key, refused before the spend | Confirmed A limit per key and per organization. The call is refused before it goes past the limit rather than invoiced after. | In part The plan caps how many credits a month are included. source |
| Attach files to a conversation | Confirmed Documents and images go into the conversation and land in a knowledge base you control. | Unconfirmed See the vendor's own pages. source |
| Answers can search the web | Confirmed Search is a platform service: the organization picks the engine, results are redacted before the model sees them, and every query is priced and recorded. | Unconfirmed See the vendor's own pages. source |
| Single sign on for the whole company | Confirmed Sign in with the company account, with roles and a permission catalogue enforced by every service rather than by the interface. | Unconfirmed See the vendor's own pages. source |
| Several models answer the same question at once | Confirmed Fusion sends the question to a panel of models in parallel and a separate model synthesises the result. | Unconfirmed See the vendor's own pages. source |
| Every source opens on the passage it came from, with its page and relevance | Confirmed Sources are numbered from what retrieval actually returned, and each opens the passage with its page number and its relevance score. | Unconfirmed See the vendor's own pages. source |
| You choose what a conversation may read: a base, folders, or single documents | Confirmed Retrieval is pointed at a knowledge base, at folders in it, or at named documents, and access is granted per base and per group. | Unconfirmed See the vendor's own pages. source |
| Code the model writes runs in a sandbox with its network cut | Confirmed The 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. | Unconfirmed See the vendor's own pages. source |
Confirmed
In part
Unconfirmed
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.