Should You Build or Buy AI RFP Software? Weigh the Tradeoffs

If you’ve used AI tools like ChatGPT or Claude for RFPs, you know the appeal. They’re fast, flexible, and easy to get started with. From generating a working draft to analyzing proposal quality, the possibilities are exciting—at first glance.

These tools are also so good at coding that building an in-house tool is more attainable than ever. But many companies, including Uber and Microsoft, are publicly questioning the hidden costs behind this approach.

It raises an important question: Is it better to build or buy AI RFP software?

In this article, we’ll weigh the tradeoffs of building versus buying, and break down why a hybrid approach often wins.

Why Should You Buy RFP Software in the Age of the AI?

If general-purpose AI were outright replacing the need for dedicated RFP software, you’d expect fewer teams to pay for it. The opposite is happening.

According to the 2026 RFP Trends and Benchmarks Report, 79% of teams now use AI—yet adoption of RFP software has climbed right alongside it, to 69%. The two numbers aren’t competing. They’re rising together.

What’s the reason for this overlap? The answer is potentially related to findings in MIT’s State of AI in Business Report, which found that only 5% of organizations have turned AI pilots into real operational or financial impact.

So, what happens to the other 95%?

Most never make it past the experimentation phase. The likely reason is that a demo only has to work once, but as soon as it’s put to test against day-to-day operations, the reality sets in on what’s required to make it successful.

For RFP teams, that list includes:

• Loading the right source content, keeping it current, and making sure approved language is never rephrased
• Defining who owns each part of the process, and rebuilding those rules into the workflow every time the business changes
• Building context to persist across sessions and teammates, so the system doesn’t start from scratch every time
• Keeping the tool running after the person who built it moves on, or fixing it when it breaks two days before a deadline

This may be part of what the numbers are showing. It’s possible teams anticipate these challenges and decide that solving them isn’t worth owning—not when there are RFPs to win and software built to handle it already exists.

“The hidden tax shows up when you actually try to run Claude across a team, across every RFP, and against real deadlines.”

Andrew Morari

Senior Manager, Solutions Consulting

Loopio

What Does It Really Take To Build Your Own RFP Response Tool?

The upside of building your own RFP response tool is clear: it would be custom-made for your needs. And with a capable team, budget set aside for innovation, and people already in seat, you have a solid starting point.

Your engineering team can put together a prototype in a single sprint that pulls decent answers from a folder of past proposals. Turning that into something governed, collaborative, and safe to run against deadlines across hundreds of RFPs is a different undertaking entirely.

So, what does it really take to build? A few considerations before you commit:

None of this means building an RFP response tool is the wrong call. It means building is a bigger commitment than it first appears.

How Does Buying AI RFP Software Compare to Building It?

Buying AI RFP software delivers what most teams want from a build, without the parts that make building hard. You get speed, support, and a product that’s already solved the biggest challenges, all at a price you can predict.

On the other side, building gives you customization and control, but you also take on everything that comes with it: the time, the risk, and the hidden costs.

Here’s how building vs. buying AI RFP software compare side-by-side:

Factor	Building Your Own Tool	Buying AI RFP Software
Time to value	One to two years before launch, much of it spent on upfront research and development	Live in weeks, with implementation and onboarding support as part of your plan
Ongoing upkeep	Specialized experts own every update, fix, and new feature. If the right person is out when it breaks, a deadline is suddenly at risk	The vendor ships updates and new features continuously, fixes issues as they arise, and gets you back up fast if anything breaks
Keeping pace with AI	Requires in-house expertise to track a fast-moving field	The vendor’s product roadmap is dedicated to staying current
Continuity risk	If a key person leaves, knowledge and sponsorship can leave with them	The product keeps running no matter who leaves, only the workflow around it needs a hand-off
Collaboration across contributors	No native way to assign, review, or version-control answers when multiple SMEs and multiple RFPs are in flight at once	Structured assignments, reviews, and version management built in, so coordination doesn’t live in Slack or email threads
Content governance	Reviewing, updating, and feeding approved content into the system is an ongoing process someone has to own	Built-in ownership, automated review cycles, and freshness controls keep the same vetted answers in circulation, so old or off-message content doesn’t resurface
Audit trails	Tracing which source produced each answer is a manual reconstruction after the fact	Every answer carries citations and a traceable source, so you can show where each claim came from
Integrations	Each connection to your tech stack is a separate build-and-maintain project	Pre-built connectors to your favorite tools, with API access for custom needs
Security	Meeting standards like SOC 2 falls on you—building, documenting, and maintaining the controls, plus carrying the audits	The vendor maintains SOC 2 and similar certifications, so the platform’s security is independently attested without effort from your team

Put together, buying often makes more sense than building. However, the costs are worth a closer look, and where the case becomes more concrete. 

What’s the Cost of Buying vs. Building AI RFP Software?

A build looks cheaper upfront. If your company already pays for an LLM subscription and the talent to build the tool is on payroll, you can start generating RFP responses quickly and at a seemingly low cost.  A software plan, by comparison, is another line item that needs budget approval.

But the savings from a homegrown tool are overshadowed by the hidden costs of making it reliable at scale. These costs hit your bottom line three times:

1. The total cost of ownership: A build is just the start. The maintenance, the updates, and the processes that wrap around the model all demand ongoing work from your engineering team—time that might be better spent on the product you actually sell.
2. The total cost of losing deals: A missing requirement, an inaccurate answer, a fragile process breaking down—what looks like a one-time slip recurs across every RFP and drags down your win rate.
3. The total cost of tokens: Every step in the RFP response process is another API call you pay for. A lengthy proposal can rack up hundreds of them, and as your volume grows, so does the bill—faster than you expect.

Purpose-built software absorbs those costs. You get the speed of AI without the upkeep, the governance that protects (and improves) your win rate, and a predictable price you can actually budget for.

When Should You Build, Buy, or Use a Hybrid Approach?

If you can say yes to the following questions, then a build makes sense:

• Is your process highly-specialized to warrant a custom solution?
• Do you have the engineering talent to spare for the long-term?
• Can you wait one to two years before seeing results?
• Do you have a plan for uptime when there’s no support team behind it?

For most teams, at least one of those is a no—which is why buying is the move. Instead of waiting to see value, you respond faster and win more RFPs within weeks—with the upkeep handled and a support team behind you.

But buying RFP software doesn’t mean abandoning general AI tools altogether. Many teams, 57% in fact, already use ChatGPT or Copilot. So, it doesn’t have to be an either/or–you can have the best of both worlds. 

There are two ways they can work alongside each other:

1. Run them side by side: Use a general AI tool for open-ended tasks, like researching a buyer, brainstorming win themes, or pressure-testing your messaging. Lean on RFP software for the system that provides content governance, SME collaboration, project management, and more. 
2. Connect them for a bigger AI workflow: Your team can build the agentic workflows and custom features it needs, then use API access to feed your governed content into them. You get the custom layer you wanted to build, running on a foundation you can trust.

Not sure which setup fits your team? Here’s a quick way to tell:

If Your Team…	The Move Is…	Why?
Gets value from AI through prompting, but hasn’t built—or doesn’t want to maintain—custom workflows and features	Run them side by side	Your team gets the flexibility and the governance with nothing extra to build or maintain
Has the engineering capacity and appetite to build custom workflows and features	Connect them by API	Your team gets the custom build you want, and the API keeps model drift in check

You don’t have to choose between them—but you do have to decide which one carries the weight. Generalist AI can be part of how you work; the system that reliably supports your RFP response process is the part worth buying.

The Loopio Copilot Agent for Microsoft 365 is a good example of the two working together. It brings your trusted content directly into the Microsoft tools your team already uses—Teams, Outlook, Word, PowerPoint—so you can find, reuse, and tailor approved answers using the power of Copilot’s AI.

Ready to Buy? The Next Step is Choosing the Right RFP Software

If you’ve weighed the tradeoffs and decided that buying is the right move for your team, the next question is which platform.

It’s worth evaluating how the best AI tools for RFP responses compare feature by feature. But the choice ultimately comes down to whether a platform delivers on what made buying over building the right call in the first place.

Here’s what to look for, and why Loopio checks each box.

✓ Reputation and Longevity

The software you rely on shouldn’t be figuring out what good looks like. Loopio has spent 12 years focused on response management and is consistently rated as a category leader on G2, with a perspective informed by more than 1,700 active customers and the best practices that come with that exposure.

✓ Partnership Beyond Support

Running AI responsibly takes trust, transparency, and collaboration. Loopio works in a mindset of shared success, pairing dedicated implementation and adoption teams with strategic support from product, consulting, and executive teams. That partnership is the part a build would have left on your shoulders.

✓ Scalability Across Your Tech Stack

The right platform connects seamlessly to your tech stack. Loopio integrates with the tools your team relies on–from CRM software to communication apps to cloud storage services. Plus, it offers API access so you can build it into a bigger AI workflow. Every one of those connections is something a build would have made you wire up and maintain yourself.

✓ Powerful AI You Can Trust

AI isn’t useful if all it does is get you to the wrong answer faster. Loopio’s proprietary AI is trained on 10+ years of data and pulls directly from your trusted content sources, so every draft starts from approved material. And because each answer comes with source citations and a confidence score, your team can verify your response at a glance instead of taking it on faith.