AI Stack Consolidator
Paste what you pay for each month and this sorts it into three buckets: absorbed by a general AI model, reducible to a cheaper tier, and keep. The rule underneath it is one line: a model absorbs generation. It does not absorb proprietary data, distribution, or execution runtime.
That distinction is the whole tool. A model can write the article; it cannot produce a backlink index, own an IP's sending reputation, or keep a workflow running at 3am. The most common finding isn't that AI replaces your stack — it's that you're paying for the same model three times through three different wrappers.
Add the price if you want real numbers (Jasper $49). Without one, a typical price is used and flagged as an estimate. Annual figures are converted to monthly.
Every verdict in the catalogue, and the reason for it
44 tools are catalogued — 11 absorbed, 11 reducible, 22 keep. These are judgements, not measurements, which is exactly why every one is published with its reasoning attached instead of hidden inside the calculator. If you think a verdict is wrong you can see it and disagree with it.
Two constraints are built in deliberately. Reducible tools never count toward the cancellable figure, because a downgrade is not a cancellation. And any tool the catalogue doesn't recognise is reported as unrecognised rather than assumed harmless — a calculator that silently drops what it can't parse always returns a more impressive number than the truth.
Absorbed
A general model does this job. These are the cancellations — the only line where the saving is real.
A wrapper over the same class of model you already pay for, with templates on top. The templates are prompts.
Same shape as Jasper. The output is model output; the subscription buys presets.
Generation with a UI. Nothing here requires a second model subscription.
Short-form generation only. Fully covered by a general model.
Predictive copy scoring is a claim about performance, not a data moat. The writing itself is absorbed.
Paraphrasing is the single easiest thing to ask a general model for.
Rewriting and tone adjustment — squarely generation.
A per-seat surcharge for model access you are already buying elsewhere.
Document Q&A. Upload the file to the model you already pay for.
For a straightforward FAQ bot this is a prompt and a few hours. It stops being absorbed once you need routing and handoff.
Deck generation is generation. Presenting and sharing them isn't a paid problem.
Reducible
Part of the job is absorbed and part isn't. Usually a downgrade rather than a cancellation, so it is never counted as savings here.
The suggestions are absorbed. The always-on inline layer across every app isn't — that's an integration, not a model.
The brief and the draft are absorbed. The live SERP scrape underneath them is proprietary data you can't generate.
Same split as Surfer at a lower price — the content side goes, the SERP data stays.
Brief generation absorbed; the competitor corpus it reads is not.
Transcription and summarising are absorbed. Sitting in the call and capturing the audio is the part you're actually paying for.
Transcript-based editing is absorbed in principle. The render pipeline and the timeline are not.
One-off graphics are increasingly absorbed. Brand kits, templates and a team that can edit without you are not.
Writing the posts is absorbed. Holding the platform tokens and publishing on time is infrastructure.
It is a model, but a specialised one — a general chat layer doesn't match it. Consolidate down to one image model, don't expect to cut it entirely.
The logic is absorbed; you can write it. The hosted runtime, retries and the connector library are what the fee actually buys.
Heavily overlapping with an agentic coding tool. Usually one of these survives, rarely both — but which one is a workflow decision, not a cost one.
Keep
Proprietary data, distribution or runtime. An AI layer runs on top of these — it does not replace them.
A crawled index of the web's link graph. No model generates this — it either has the data or it's guessing, and guessed backlink data is worse than none.
Geo-grid rank data collected from real locations. Measurement, not generation.
Access to a proprietary graph. The moat is the data and the permission to query it.
Contact data with verification. A model will happily invent an email address, which is precisely the failure mode you're paying to avoid.
The system of record for who owes you money and who you owe a call. State, not text.
Money movement and compliance. Nothing about this is a generation problem.
Runtime. Something has to serve the bytes.
A server is a server. The AI layer runs on it, it doesn't replace it.
Persistent state with access control. A model has no memory of your rows.
Names and routing. Structurally not a model problem.
Identity, mail delivery and the calendar everything else hangs off.
Where the humans are. Consolidating this consolidates your team, not your stack.
Deliverability is reputation held by an IP over time. Writing the email was never the hard part.
Availability arbitration against a live calendar. Cheap to replace, but not by a model.
A specialised model with no general-chat equivalent. It is AI spend, but it isn't duplicated AI spend.
Rendering and a timeline. The script is absorbed; the edit isn't.
An execution runtime with credentials and scheduling. This is the thing that runs your AI, not a thing AI runs.
The database and the shared surface stay. Only the AI add-on is duplicated spend.
Shared state about work in flight. A model can file the issue; it can't be the tracker.
Measurement of things that already happened. Generation has nothing to offer here.
Bytes at rest. Not a model problem.
Secrets custody. You want this boring and you want it nowhere near a model.
Cutting the bill is the small half
Cancelling duplicated subscriptions is worth doing once and then it's done. The larger number is almost always hours rather than dollars — which is the question the Leverage Audit starts from instead.