Recruiting Software Got Easier to Build. Your Renewal Price Should Reflect It.

If you’re buying recruiting technology, reevaluating your stack, or heading into a major renewal, there is one assumption I think you need to challenge before you negotiate anything:

what you paid last year should not automatically be the baseline for what the software is worth this year.

The economics of recruiting technology have changed too much.

A lot of the recruiting software we use today was built during a period when software was significantly harder and more expensive to create. Building a credible enterprise product required substantial capital, large engineering teams, long development cycles, significant infrastructure, and years of accumulated development. Integrations could take months. Moving into an adjacent category could take years. That difficulty created real scarcity, and vendors priced around it.

That scarcity is disappearing much faster than many of the pricing models built on top of it.

The Cost of Building Recruiting Software Has Changed

This is the biggest change, and everything else starts here.

AI-assisted development has materially changed how software gets built. Developers can use these tools to generate and modify code, build interfaces, create APIs, write tests, debug problems, document systems, transform data, and prototype functionality. Combine that with mature cloud infrastructure, open-source libraries, modern development frameworks, APIs, middleware, and iPaaS platforms, and an enormous amount of work that once required custom engineering can now be accomplished with fewer people and less time.

There is real evidence behind that productivity shift, although I would be careful about pretending there is one universal number. GitHub’s research found developers completing a controlled coding task as much as 55% faster with Copilot. In a separate enterprise study with Accenture, GitHub reported improvements in development metrics including higher pull-request merge rates and successful builds.

More recent internal research from Anthropic points in the same direction. Anthropic employees reported an average 50% productivity improvement from using Claude, while the company’s engineering telemetry showed a 67% increase in merged pull requests per engineer per day after adoption of Claude Code. Anthropic itself cautions that productivity is difficult to measure precisely, which is an important caveat.

And the evidence is not uniformly positive. METR found that experienced open-source developers working on mature codebases were actually slower with early-2025 AI tools. But in its February 2026 follow-up, METR said later tools likely produced greater acceleration, while also explaining that widespread AI adoption was making controlled measurement increasingly difficult because developers were reluctant to work without AI.

So I am not arguing that every software engineer is suddenly 50% faster at everything.

The more important point is that the frontier of what a small, capable team can build has moved materially.

And this isn’t just my observation. A May 2026 CIO analysis described essentially the same change in enterprise software economics: historically, companies bought major SaaS platforms because recreating them required enormous capital and long engineering tenures. AI-assisted development is now weakening that assumption, particularly for workflow-oriented enterprise applications and integrations.

That doesn’t mean two developers with an AI coding tool can rebuild Workday over a weekend. That’s not the argument. The more important point is that many of the individual capabilities that created the moat around recruiting technology can increasingly be reproduced much faster than they could when the incumbent products were originally built.

Scheduling. Texting. Candidate matching. CRM functionality. Interview intelligence. Workflow automation. Analytics. Candidate communications. Sourcing capabilities. Integrations. User interfaces. Conversational AI.

None of those capabilities are trivial. But increasingly, simply having them isn’t much of a moat either.

And that’s a major change for buyers because a lot of recruiting technology pricing was established when those capabilities were genuinely expensive to build.

If it took millions of dollars and years of engineering to create something, the company that had already built it could reasonably command a significant premium. If another capable company can now reproduce much of that functionality at a fraction of the historical development effort, the economic value of that original scarcity changes.

Zoho founder Sridhar Vembu has gone even further, arguing that AI could eventually turn large legacy codebases from assets into liabilities because rebuilding software with AI assistance may become more attractive than continually maintaining old architecture. I don’t think we’re universally at that point yet, but the direction of the argument matters.

The legacy vendor may not like that reality. But the buyer shouldn’t ignore it.

Legacy Cost Is Not Market Value

This is where I think a lot of technology buyers get trapped.

Large, established software companies have large, established cost structures. They have older architectures, accumulated technical debt, big engineering organizations, multiple generations of infrastructure, acquired products that still need to be maintained, professional services organizations, sales teams, corporate overhead, and years of technology that cannot simply be switched off.

Those expenses are real.

But they’re the vendor’s expenses. They aren’t automatically the market value of the software.

A newer recruiting technology company can start with a modern architecture, a smaller engineering organization, less technical debt, cloud-native infrastructure, modern APIs, AI-assisted development, and an entirely different cost structure. It may be able to deliver substantially similar business functionality for dramatically less money while still generating attractive margins.

The broader enterprise software market is beginning to have exactly this conversation. CIO’s 2026 analysis argues that the historical SaaS moat was not necessarily the code itself, but the expense of writing and integrating it. As that expense comes down, vendors defending legacy margin structures face increasing pressure from customers with more credible build and replacement alternatives.

In some categories, we may be approaching a point where maintaining the old thing can be more expensive than creating a modern alternative.

That should matter enormously in a technology evaluation.

If an incumbent needs $2 million a year from you because its economic model was built around a much more expensive way of producing and maintaining software, while a credible competitor can profitably deliver the required business outcome for $800,000, the incumbent’s historical cost structure doesn’t somehow make the capability worth $2 million.

The market doesn’t price based on sunk cost.

It prices based on alternatives.

And It’s Not Just Development. The Underlying Economics Changed Too.

Once you get past development cost, the economics underneath the product have changed as well.

Look at AI inference. OpenAI currently lists the older GPT-4 API at **$30 per million input tokens and $60 per million output tokens. **Its current pricing spans dramatically lower levels depending on the model; GPT-5.6 Luna, for example, is priced at $0.20 per million input tokens and $1.20 per million output tokens, with cached input at $0.02 per million tokens.

Those aren’t equivalent models or equivalent workloads, so I would not call that an apples-to-apples performance comparison. But from an economic perspective, the magnitude of the change is impossible to ignore. The commodity cost of putting AI into a workflow can now be dramatically lower than the cost associated with the early frontier-model era.

Think about what many recruiting AI applications actually do: summarize a candidate profile, generate an outreach message, extract structured information from text, classify an interaction, answer a candidate question, produce interview notes, or help identify potentially relevant candidates.

Depending on the model, prompt size, output, and architecture, the raw inference expense behind many individual transactions can be tiny.

That does not mean an AI recruiting product should be priced at the cost of the token. That would be ridiculous. The vendor is also providing workflow, architecture, governance, security, reliability, integration, support, implementation, intellectual property, and ideally measurable business value.

But if you’re paying a substantial premium because something is “AI-powered,” you should understand what the underlying AI actually costs.

Then ask what you’re paying the rest of the premium for.

Storage is another useful example.

AWS currently prices S3 Standard storage starting around $0.023 per gigabyte per month, while S3 Glacier Deep Archive is listed at $0.00099 per gigabyte per month. That means deep archival storage is roughly $1 per terabyte per month for the raw storage component before retrieval, request, transfer, metadata, and surrounding infrastructure costs.

Consider a recruiting platform holding years of interview recordings, transcripts, resumes, documents, and other information that rarely requires immediate access. At that raw Deep Archive rate, 100 terabytes is on the order of $100 per month in underlying archive-storage charges.

Again, I’m not suggesting the vendor’s total cost for managing 100 terabytes is $100. It isn’t. There are retrieval costs, data transfer, databases, backups, redundancy, security, compliance, processing, application infrastructure, operations, and people around it.

But that’s exactly why buyers should understand the difference between the commodity input cost and the value-added price being charged around it.

If a vendor tells me storage is a major reason a product or feature needs to cost hundreds of thousands of dollars, I want to understand precisely what “storage” means in that statement.

Not every infrastructure cost has gone down. GPU-intensive computing remains expensive. Security is expensive. Compliance is expensive. High-performance systems require real infrastructure. Skilled engineers, implementation teams, and customer support still cost money.

That’s why the argument isn’t, “Technology is cheap now, so software should be cheap.”

The argument is that several of the inputs that historically created scarcity in software have changed dramatically, while a lot of software pricing still assumes the old scarcity exists.

That’s what buyers need to challenge.

You Can See It Happening Across Recruiting Tech

Look around the recruiting technology market and you can see the result.

Companies that historically did one thing well—scheduling, texting, sourcing, recruitment marketing, CRM, assessments, interview intelligence, or candidate engagement—are moving into adjacent categories at a speed that would have been extremely difficult several years ago.

A point solution can become a broader platform remarkably quickly because the marginal cost and time required to add another workflow or capability are changing.

The same thing is happening with integrations. There was a time when being integrated with a major ATS or HRIS was a legitimate competitive moat. Building and maintaining those integrations required meaningful engineering effort, and vendors priced accordingly.

That moat is being challenged too. The same 2026 CIO analysis specifically calls out API connections, schema parsing, authentication flows, and data mapping as areas where work that historically required substantial senior engineering effort is increasingly being accelerated by AI-assisted development.

So when a recruiting technology vendor tells you, “We’re already integrated with your environment,” that still has value. But you need to ask a second question: how difficult and expensive would it actually be for somebody else to build that integration today?

Those aren’t the same thing.

An existing capability is not necessarily a defensible capability, and buyers need to stop paying defensibility prices for things that have become relatively easy to reproduce.

Workday, HiredScore and Paradox Are an Interesting Case Study

Workday gives us a particularly interesting example of where this market may be going.

Workday acquired HiredScore in March 2024 for $530 million in cash. In September 2025, it completed the acquisition of Paradox, with Workday’s SEC filing reporting total acquisition-date purchase consideration of approximately $1.1 billion, including $1 billion in cash.

Workday itself now describes Workday Recruiting, HiredScore, and Paradox together as an end-to-end AI-powered talent acquisition suite.

My hypothesis is that Workday is doing two things at the same time.

First, it is buying a foothold while giving itself time to build and integrate what comes next. Buying HiredScore and Paradox immediately gives Workday proven technology, intellectual property, talent, enterprise customers, market position, and capabilities it would otherwise have to spend time building organically. In a market moving this fast, time itself has enormous strategic value.

Second, I think Workday has an opportunity to extract significant value from those existing platforms and installed customer bases while that transition is happening.

And I’m not basing that second point only on a theoretical strategy exercise.

I am hearing directly from multiple large enterprise buyers and people throughout the recruiting technology market that renewal pricing on both Paradox and HiredScore has increased dramatically. I’m hearing it from enough different sources that I don’t believe we’re talking about one unusual negotiation or one unhappy customer.

I don’t have a published dataset that allows me to tell you the average Paradox increase is X% or the average HiredScore increase is Y%, so I’m not going to manufacture a number I can’t support. This is market intelligence coming directly from conversations with buyers and industry participants, and I’m labeling it accordingly.

But the signal is strong enough that buyers need to pay attention.

And economically, it makes sense.

Large enterprise customers can’t casually rip out Paradox or HiredScore. These products can sit deeply inside candidate communications, scheduling, recruiting workflows, integrations, recruiter processes, data flows, and operating models. Replacing one means implementation, testing, integration work, training, change management, internal approvals, and disruption.

This switching-cost dynamic isn’t unique to recruiting technology. The broader enterprise SaaS market is wrestling with the same issue. CIO’s analysis argues that incumbent software pricing has remained resilient partly because replacing enterprise platforms is not simply a coding decision; much of the real cost sits in people, process, data migration, workflow change, and organizational disruption.

That gives an incumbent leverage.

A vendor can increase the price significantly and reasonably calculate that, for at least one more contract cycle, paying the increase will still be easier for the customer than replacing the technology.

That doesn’t necessarily mean the underlying capability suddenly became more valuable.

It may simply mean switching became more painful than paying.

Those are two very different things.

My hypothesis is that Workday can use this period to do both: monetize the acquired and legacy stacks aggressively while it gets the time and foothold it needs to develop the newer, better-integrated products that will compete in the next version of this market.

That’s a rational vendor strategy.

It doesn’t have to be a rational buyer strategy.

Incumbent Leverage Is Not the Same as Market Value

This distinction matters because buyers frequently confuse what a vendor can charge with what a product is worth.

If you’re already deeply embedded in Paradox, HiredScore, or any other major recruiting platform, you may rationally absorb a substantial price increase for another contract cycle. Maybe replacing it will cost $1 million. Maybe you’ll need twelve months to select and implement an alternative. Maybe changing it now would interrupt another transformation. Maybe the internal resources simply aren’t available.

In that situation, paying too much for another year or two may actually be the correct economic decision.

But be clear about what you’re buying.

You’re not necessarily paying the premium because the software is worth that much more. You may be paying a switching-cost premium.

There is nothing wrong with making that decision deliberately. The problem comes when the switching-cost price becomes the benchmark you use to determine the market value of the technology.

Because those are not the same number.

This Is Why New Implementations Should Face a Much Higher Bar

This distinction becomes even more important if you’re selecting new technology.

I can understand why an installed customer accepts a painful renewal. What I have a much harder time understanding is why a company making a new recruiting technology decision today would voluntarily inherit the same economics.

You don’t have the switching-cost problem yet. You aren’t trapped by ten years of configuration. You don’t have hundreds of integrations to unwind. Your recruiters haven’t built their entire operating model around the system. Your organization hasn’t accumulated years of data inside it.

So why would you use the price an incumbent can extract from an installed customer as your benchmark for a new purchase?

If you’re implementing new recruiting technology today, you should be looking very hard at the architecture underneath the product. How old is it? How much technical debt does it carry? How rapidly can the vendor actually develop? How dependent is it on expensive professional services? How easily does it integrate? How much of what you’re buying is truly proprietary? How much has become commodity functionality? What does the vendor’s operating model look like compared with a modern competitor?

Most importantly, ask what somebody building and delivering that capability using today’s technology would need to charge.

That’s the economic benchmark.

I would be extremely reluctant to recommend a new implementation of a legacy or recently repriced recruiting technology product simply because the installed base has historically tolerated the price. If you’re choosing from scratch, the vendor should have to demonstrate why its incremental value justifies that premium against today’s alternatives.

Brand recognition isn’t enough. Historical market share isn’t enough. “We’ve always charged this much” certainly isn’t enough.

Stop Negotiating the Increase. Reprice the Capability.

This is where I think recruiting technology renewal strategy needs to change.

Imagine your current platform costs $2 million annually. The vendor sends the renewal and proposes an 8% increase. The new number is $2.16 million.

What normally happens? The entire negotiation becomes about the $160,000. Procurement pushes back. The vendor makes concessions. Maybe you settle at $2.05 million and everybody walks away saying you negotiated a good deal.

But nobody challenged the most important number in the entire negotiation: why is $2 million still the baseline?

Maybe it should be.

Maybe the platform is deeply embedded in your operation and extraordinarily difficult to replace. Maybe the implementation is exceptional. Maybe the vendor’s data, security, domain expertise, support, workflow, or outcomes are genuinely differentiated. Maybe the switching costs make the $2 million contract economically rational.

All of those things can justify the number.

But “that’s what we paid last year” does not.

If competitors have closed the functionality gap, development cycles have compressed, integrations are easier, infrastructure economics have improved, and alternative vendors can deliver the same outcome with structurally lower costs, then your existing contract price should not automatically anchor your negotiation.

Before negotiating the increase, reprice the capability.

Ask what it would cost if you went to market today. Ask what credible alternatives can actually do now, not what they could do three years ago when you last evaluated them. Ask what it would cost to migrate. Ask which parts of your incumbent’s product remain genuinely difficult to reproduce and which have become commodities. Ask what you’re paying for superior business value and what you’re paying because changing systems is inconvenient.

Then negotiate.

You may still conclude that your incumbent is worth every dollar.

But at least you’ll know why.

Pay for What Is Still Hard

None of this is an argument for buying the cheapest recruiting technology.

There are things in enterprise software that remain genuinely difficult and enormously valuable. Excellent implementation is hard. Clean architecture is hard. Maintaining reliable integrations at enterprise scale is hard. Security and compliance are hard. High-quality data is hard. Deep industry and recruiting expertise are hard. Great support is hard. Driving adoption across a complex organization is hard. Producing measurable business outcomes is hard.

Those are things I am willing to pay for.

Another feature on a comparison sheet that somebody else can reproduce in a relatively short development cycle? Much less so.

That’s the reset I think recruiting technology buyers need to make.

The market doesn’t owe a legacy software company a return on what it cost to build its platform ten years ago. It doesn’t owe an acquirer a return on the premium it chose to pay for another technology company. And buyers don’t owe vendors last year’s price simply because that’s the number printed on the renewal.

If you’re reevaluating your recruiting technology stack or heading into a renewal, don’t start with, **“How much can we get them to take off the increase?”

Start with the harder question:

If we were buying this capability for the first time today, knowing how much easier many components are to build, how much the underlying economics have changed, and what alternatives exist now, what should we actually pay for it?

That’s the baseline that matters.