Attention pricing, features and alternatives

The short answer

Buy Attention if reps skip CRM updates after calls and you want the next steps, objections and fields filled in automatically, and if RevOps has a use for structured conversation data beyond the rep's own notes. It is lighter than a full revenue intelligence platform, which is the point: you are buying data capture rather than a management system.

Skip it if you need deal inspection and forecasting built on call data, where Gong is the enterprise benchmark and a much larger commitment. Skip it too if your CRM governance does not allow automated field updates, because the automation is the product, or if basic meeting notes are the whole requirement and Fireflies.ai covers it from $10 per seat per month on annual billing.

Where Attention fits

Attention captures sales conversations and turns them into structured data: notes, next steps, objections and CRM updates. The framing that matters is the destination rather than the capability. Plenty of tools record and summarise a call; Attention's pitch is that the output lands in the systems reps and managers already use instead of sitting in a recording nobody opens. A G2 reviewer said it helps them pull data from sales calls to identify objections and trends, and saves reps time, which is a fair description of both halves of that promise.

It fits teams whose honest diagnosis is a CRM data problem. If your forecast is unreliable because next steps are blank and close dates are stale, and the reason they are blank is that reps finish a call and move straight to the next one, then automated extraction is pointed at the actual cause. If your forecast is unreliable because managers do not inspect deals, a tool that fills in fields will produce better-looking data on top of the same unexamined pipeline, and you will have bought a cosmetic improvement. Work out which of those you have before the demo, using our CRM data audit checklist if the answer is not obvious.

The risk that comes with the benefit is that automated CRM updates write bad data as fast as good data, and at the same scale. An extraction model that misreads a next step on one call in five does not produce a slightly worse pipeline, it produces a pipeline whose errors are evenly distributed and therefore invisible. So the governance decision is not optional: decide which fields the AI may update without review, which require a rep to confirm, and which it may never touch. Our required fields matrix is the place to write that down, and it should exist before the pilot rather than emerging from it.

There is a second audience the purchase usually has to satisfy, and it is the one that justifies the price. Reviewers use Attention to spot objections and trends across calls, which makes the data useful to RevOps, enablement, product and marketing rather than only to the rep who was on the call. That broadens the business case, and it also broadens the questions: ask how trends are reported, whether non-sales teams can reach them, and whether those users need paid seats. A tool bought for rep productivity and justified on cross-functional insight needs to be licensed for both.

Public reviews are fewer than for the older conversation intelligence tools, which changes how you should evaluate rather than whether you should. Reference calls matter more here, so ask for two customers with your call volume and your CRM, and ask them about field accuracy specifically rather than about satisfaction. Attention sits in our revenue intelligence software directory because of where it competes, but the practical shortlist usually also includes the lighter AI meeting assistants for sales, and comparing across those two groups on CRM field accuracy is the only comparison that settles it.

Attention pricing

Attention does not publish prices. In September 2026, attention.com/pricing returned a 404 and the site offered demo requests instead of a plan grid or a free trial. Third-party sites circulate per-user estimates, but we could not verify any of them against anything from the vendor, so we do not repeat them here: an unattributed per-seat figure for a quote-only vendor is a guess with a dollar sign on it. Attention also announced a $30M Series B in June 2026, reported by PR Newswire, which is a reason to expect packaging to keep moving rather than a reason to expect any particular price.

Four things decide what the quote comes to, and the second is the one that quietly inflates it.

1. The per-seat rate on an annual contract. Ask for it as a rate rather than as a total, so the quote survives a change in headcount.
2. Who counts as a seat. Managers, enablement and RevOps users who only view calls and dashboards may still need paid licences, and that is what turns a 10-seat plan into 13 or 14. Settle it before the proposal is built, because it is the single largest swing in a deployment this size.
3. What is in the base price against what is added later: CRM field automation, custom scorecards, API access and onboarding. CRM automation is the reason to buy the product, so confirm in writing that it is not an upgrade.
4. Contract length, and what happens at renewal. A vendor at this stage of funding is a vendor whose packaging may change, so ask what protects your rate if it does.

How to size the budget without a rate

With nothing published, build the budget from both ends. On the cost side, get a per-seat annual quote and multiply it out at 10 x the quoted monthly seat price x 12, then redo the arithmetic with the viewer seats included, because the gap between those two totals is the negotiation. On the value side, the case is rep time saved on notes and CRM updates, so estimate the admin minutes per call your reps spend today, multiply by calls per week and by seats, and price that time at loaded cost. That gives you a number to hold the quote against. The published alternatives in the comparison table are the other anchor: Avoma at $19 to $39 per seat per month on annual billing and Fireflies.ai from $10 are what a lighter answer costs, and any quote well above them has to be justified by the CRM automation specifically.

Because the price is quoted and the packaging is young, get more in writing than you normally would: the per-seat rate, which roles need paid seats, whether CRM field automation and API access are included, the onboarding scope, the term, the renewal uplift, and your export rights on recordings, transcripts and extracted fields if you leave. Then make field accuracy a contractual expectation rather than a hope, since it is the one thing that decides whether the data you are buying improves your pipeline or quietly degrades it. How we review software explains why we leave the circulating per-user estimates off this page.

What Attention gives up

You give up knowing the price, and here you give up the usual workarounds too. There is no plan grid, the pricing URL 404s, and the third-party estimates that circulate cannot be traced to the vendor, so there is no credible benchmark to budget from. The practical answer is to hold a published-rate quote from a lighter tool next to Attention's proposal and make the vendor justify the difference by naming the CRM automation it buys you.

You give up a guarantee that the data is right. Automated updates can write bad data as fast as good, and the failure is quiet: a wrong next step looks exactly like a right one in a pipeline report. Test field accuracy on your own calls during the pilot, count the errors rather than sampling impressions, and decide which fields the AI may update without review. If accuracy on your calls is below what your reps achieve manually, the purchase makes your forecast worse while making it look better.

You give up the depth a revenue intelligence platform brings. Attention is lighter by design, which is a strength in a budget conversation and a limitation in an executive one. If what leadership wants is deal inspection, risk scoring and a forecast built from call data, this is not that product and the gap will surface in the first quarterly review after purchase. Price Gong in parallel if that is a real requirement, and accept it is a different order of commitment.

You give up the comfort of a long review history. There is less public review evidence than for the older conversation intelligence tools, so you are relying on reference calls and your own pilot rather than on the accumulated experience of hundreds of buyers. Ask for references with your call volume and your CRM, and ask them about accuracy and support response rather than about whether they like it.

You give up stability in the packaging. A $30M Series B in June 2026, reported by PR Newswire, funds product expansion and usually reprices it: tiers get renamed, features move between them, and the thing you bought in the base plan becomes an add-on in the next version. Ask what protects your configuration and your rate across a packaging change, and get the answer in the contract rather than in an email.

You give up the option of ignoring pipeline hygiene rules. The automation only helps on top of definitions people agree on, so stage definitions, required fields and what a next step means have to be settled first. A tool that fills fields faster than your team can agree what they mean will industrialise the disagreement.

How Attention compares

This table spans three price orders of magnitude for what sounds like one job, and the reason is that the four alternatives answer different questions. Gong is a management system where the recording is evidence in a deal review. Nektar is pointed at activity and CRM completeness rather than at the conversation itself, which makes it the closest neighbour to Attention's actual pitch. Avoma and Fireflies.ai are good enough at notes and summaries at a published per-seat rate, and they are the right answer for a team that wanted transcripts. Attention's claim is the middle ground: structured CRM data from conversations without buying an enterprise platform. That claim is testable on field accuracy, and the pilot should test nothing else first. Published figures trace to each product's own file and our September 2026 pricing check.

What to test in an Attention pilot

Our sources do not confirm a free trial, so ask about trial options and scope a pilot on your own calls. One measurement decides this purchase, which is field accuracy, so run the pilot against a lighter notetaker and a conversation intelligence platform on the same calls and compare the fields rather than the summaries.

1. Map every CRM field you want Attention to populate, and mark each one as auto-update, confirm-before-write, or never. Agree that list with whoever owns CRM governance before the first call is processed.
2. Run 20 of your own calls through it and check extracted next steps and field values against what a rep or manager would have entered. Count the error rate per field. A field that is wrong 15% of the time belongs in the confirm-before-write column, whatever the demo showed.
3. Compare its summaries with your reps' own notes on the same calls, and ask the reps which they would rather read. The adoption answer is in that preference, and adoption is what the time-saving case rests on.
4. Ask which objection and trend reports exist, pull one for a real segment, and check whether a RevOps or enablement colleague can act on it without a sales rep translating it.
5. Establish exactly which roles need paid seats. Have a manager, an enablement user and a RevOps analyst each try to do their real job with viewer access only, then recalculate the quote with the seats they turned out to need.
6. Measure the admin minutes per call your reps spend on notes and CRM updates before and during the pilot. Multiply the saving by calls per week and by seats to get the annual value, which is the number the quote has to beat.
7. Get the commercial and data answers in writing during the pilot: the per-seat annual rate, which roles are billable, whether CRM field automation, custom scorecards and API access are included, the onboarding scope, the term, the renewal uplift, and your export rights on recordings, transcripts and extracted fields.

Attention questions buyers ask

What is Attention used for?

Capturing sales calls, summarizing them, extracting next steps and details, and updating the CRM automatically.

How much does Attention cost?

Attention does not publish pricing. It sells through demos and custom quotes, as of September 2026. Ask for a per-seat annual price and whether viewer seats for managers are free.

Is there a free trial for Attention?

Our sources don't confirm one. Ask Attention about trial options.

Who are Attention's main competitors?

Gong for enterprise revenue intelligence, and Avoma or Fireflies.ai for lighter meeting assistance.

Is Attention a conversation intelligence tool?

Yes. It also covers CRM automation and AI meeting assistance, which puts it in several categories.

Related

If Attention is on your shortlist, the comparison depends on what you want the call data to do: Gong where deal inspection and forecasting are the job, Nektar where CRM completeness matters more than the conversation, and Avoma or Fireflies.ai where notes and transcripts are the whole requirement. The wider sets sit in our revenue intelligence software and conversation intelligence software directories, and our required fields matrix is the document to settle before any tool gets write access to your pipeline.

Attention sits in our Revenue Intelligence Software directory.