Building your own AI feedback analytics platform can quickly become expensive. A 2024 IDC study found that companies that shifted workloads to managed cloud services cut five-year operating costs by 48%.

Applied to feedback analytics, internal builds can cost around $1.8 million over five years. In contrast, a SaaS solution, supporting 50 users and moderate volumes of unstructured feedback, might total roughly $900,000 (based on an illustrative 50-seat license with moderate data volumes over a five-year period). These are illustrative figures only; actual costs vary by volume, features, and usage.

Buying spreads costs predictably and delivers insights faster, without the surprise expenses that often follow internal builds.

In this article, we’ll explore the rising costs in 2025, break down what goes into a build, highlight hidden risks, and show where ready-made tools offer a smarter path. You’ll also get a quick checklist to help decide whether to build or buy.

Why TCO Matters in 2025

AI costs keep climbing, so a clear total cost of ownership (TCO) view matters. Many CIOs report nearly 9% price hikes for cloud and other IT services heading into 2025, while global AI‑hardware spend doubled in early 2024 to about $47 billion. Running GPUs, storage, and traffic yourself now burns cash faster every quarter.

People costs climb too. A 2024 study shows 76% of firms lack enough AI‑skilled staff, and labor already makes up roughly 70% of tech operating budgets. Every extra hire pushes the build bill higher, including the costs to recruit, onboard, and manage that talent.

Wild cost swings add more risk. In a 2024 Gartner survey, 90% of CIOs said uncontrolled expenses block AI value, and analysts warn that companies can underestimate project costs by as much as 10x if they misjudge scale.

Put together, volatile cloud bills, pricey hardware, scarce talent, and unpredictable overruns make in‑house AI a fiscal minefield. That’s why 2025 leadership teams start every initiative with a side‑by‑side TCO check: build if the spend is truly strategic and you can stomach the variability; buy when you need results fast and budget certainty.

What Goes Into ‘Build’ Costs?

Building takes far more than writing a clever model. Five cost buckets absorb most of the budget, and they never really switch off.

1. Infrastructure (Cloud & Hardware)

Fast GPUs, storage, and network traffic are the foundation. A single server loaded with top-tier GPUs can cost around $250,000, and cloud fees scale with every query processed. Most teams rent compute power from providers like AWS or Azure, so total spend depends on data volume, how efficiently queries are processed, and how the infrastructure is configured.

Monitoring cloud applications requires observability SaaS tools like Datadog, which help track performance and prevent system failures. However, misconfigurations or excessive data collection can lead to unexpected charges. For example, a company with a typical monthly spend of $100K was hit with an unexpected $160K overage due to an engineer's incorrect setup. (This is an anecdotal example of how misconfiguration—not normal use—can lead to major overages.)

Costs can quickly spiral without expert oversight. To avoid surprises, teams need to budget for observability tools, build guardrails around usage, and regularly review infrastructure configurations to ensure efficiency.

2. MLOps & Tools

Pipelines, model‑training and evaluation frameworks, monitors, and integration APIs must be installed, tuned, and patched. These pipelines often rely on text analytics and backend NLP components to extract actionable insight.

These tools safeguard accuracy and reliability, but add license and engineering hours when you move past a proof of concept.

3. LLM Usage Costs

Whether you deploy your own large language model or access one via providers like OpenAI, Azure, or AWS, LLMs are not free. Costs are volume-dependent and can spike with frequent or complex queries. These services often charge by token or compute time, and if used at scale, they can become a major line item.

4. Model Upkeep & Data Maintenance

New and better models are dropped almost daily which affect costs, speed and accuracy of the final solution. They need to be tested and optimized first. . For this, the data needs cleaning and labeling. For teams handling qualitative input, robust qualitative data analysis workflows are essential.

Analysts put annual maintenance at 15–25 % of the original build cost, a recurring bill that stays for the life of the system.

5. Staffing (Development & Support)

You’ll need data scientists, software engineers, plus DevOps and MLOps talent. Their salaries and benefits often drive about 70 % of tech operating spend. Lose a key engineer, and costs spike again while you recruit a replacement.

In addition, it’s easy to build the wrong thing! So a product manager, a UX specialist, and a designer are critical. They need to specialize in building AI solutions, which is again difficult to find.

6. Compliance & Security

Privacy rules (GDPR, CCPA), audits, and breach prevention land squarely on your team. Expect legal fees, security tooling, and extra work to add explainability or data‑deletion controls.

Hidden & Opportunity Costs of Building

Building drains more than dollars, you can log it in a spreadsheet. Three hidden costs can quietly tilt the build‑vs‑buy balance.

1. Team Retention Risk: AI engineers are scarce, so turnover hits hard. When a senior ML lead walks out, project velocity stalls, and hiring a replacement can take months. Burnout grows when experts spend days patching internal tools instead of shipping new ideas.
2. Delayed Time‑to‑Value: A scratch‑built platform can take a year to reach production. During that wait, you miss insight‑driven decisions and trail faster adopters. IDC found 92 % of firms that buy managed AI reach ROI inside 14 months; home‑grown projects rarely match that pace.
3. Technical Debt & Maintenance: Quick fixes made under deadline pressure pile up. Each patch demands rework, consuming engineering hours that could power customer‑facing features. You also own every bug, audit, and scaling glitch for the life of the system.

Together, retention risk, slow value, and growing tech debt can erase headline savings and leave you with a fragile platform no one loves. Wise leaders budget for these silent drains—or choose a vendor that has already solved them.

Buying Off-the-Shelf: Key Benefits

Now, consider buying an off‑the‑shelf AI feedback analytics platform. The upsides land fast:

1. Predictable bills: Vendors quote a clear license or subscription. One finance team pays about $15 000 a month for 50 seats; no surprise GPU or egress fees. Hardware, cloud, and MLOps live inside that number, so budgets stay locked.
2. Weeks, not months, to insight: After a short onboarding, you plug in data and dashboards light up in days. A recent IDC study shows buyers of managed AI hit positive ROI in ≈14 months, while home‑grown builds often take a year just to go live.
3. Complexity off your plate: Model tuning, drift checks, patches, and security audits move to the vendor’s roadmap. Many modern platforms embed semantic analysis capabilities to refine insights automatically. Your engineers focus on acting on insights, not maintaining pipelines.
4. Built‑in scale and help: SaaS platforms serve thousands of clients, so horizontal scaling and 24 / 7 support are table stakes. When feedback volumes spike, the vendor simply spins up more compute.
5. Future‑proof runway: By 2026, 80 % of software vendors will embed AI. Buying now lets you ride that innovation curve instead of rebuilding every time the stack shifts.

Add up cost certainty, speed, lighter workloads, and continuous upgrades (say, integrations or generative AI tools), and buying becomes the low‑risk, high‑leverage path for teams that need results fast.

Beyond Dollars: Other Factors to Consider

Money matters, yet it isn’t the only lens. Four extra lenses often flip the decision even when budgets look close.

1. Risk & Success Odds: About four in five AI projects miss their goals. When you build, all that risk sits with you. If a key engineer leaves or the model under‑delivers, sunk salaries and hardware costs sting. Buying shifts much of that risk to a vendor whose reputation depends on keeping the system working.
2. ROI & Opportunity Cost: A home‑grown stack might promise unique upside, but it pays off slowly. You could wait a year before users see value. Buying lets you load data and start proving impact in weeks, delivering gains while the DIY path is still in dev. Time saved equals earlier revenue lifts and happier clients.
3. Talent & Focus: Tech bandwidth is finite. Building monopolizes data scientists and DevOps for never‑ending support. A bought platform frees those brains to act on insights or ship customer features, a better use of scarce skill.
4. Agility & Road‑map Velocity: Vendors serving thousands of customers ship new visualizations, connectors, and model upgrades regularly. You get them overnight. Re‑creating the same inside can take quarters and more cash. Buying plugs you into that innovation stream at no extra engineering cost.

Risk transfer, faster ROI, freed talent, and continuous innovation: these “soft” factors often outweigh any spreadsheet that shows building only slightly cheaper on paper.

Quick Feasibility Checklist: Build or Buy?

Answer four direct questions. Any No steers you toward buying (or partnering); four Yes answers suggest building could pay off. A mixed result often points to a hybrid path.

1. Need – Do you collect so much unstructured feedback that simple dashboards can’t keep up? If volumes are low or insights are basic, skip a full AI build and buy a ready‑made tool.
2. Strategic Value – Would owning the tech give you a lasting edge thanks to proprietary data or special workflows? If feedback analytics is support‑level, an external platform is usually plenty.
3. Feasibility – Do you truly have the talent, infrastructure, and budget to design, launch, and maintain a production‑grade stack? Hidden bills (MLOps, audits, 70 % of OpEx tied to people) mount fast. If resources are tight, let a vendor carry that load.
4. Performance – Can you hit target accuracy, uptime, and security at scale, and keep them there? Pilots are easy; year‑three reliability is the hard part. Vendors already tune drift, patch models, and absorb infra spikes.

Interpret Your Score

• 0–1 “No” answers → Building is feasible if the project is truly strategic.
• 2–3 “No” answers → Buying delivers faster ROI with less risk.
• 4 “No” answers → A commercial platform is the obvious choice.
• One “No” tied to skills or infra → Try a hybrid path: buy the analytics engine, build only the bespoke layer.

Closing Thoughts

Today, the business case for buying an AI feedback analytics platform is stronger than ever. Off-the-shelf solutions save time, reduce long-term risk, and ease the burden of maintenance, especially for teams without deep in-house AI capabilities.

Costs vary widely by seat count, data complexity, and use case. What looks like a savings on paper may not hold up when factoring in upkeep, talent retention, or scaling challenges.

That’s why we’ve outlined a realistic comparison of costs, trade-offs, and decision points.

Want to dig deeper? Download our Build vs. Buy Guide for detailed budgets, feasibility tests, and a calculator to help you model your own TCO.