Introduction
Getting quality GenAI products requires solid evaluation, there's no way around it. Teams know this, which is why they spend weeks building evaluation pipelines, writing custom scripts, and creating dashboards to measure model performance. But here's the challenge: while your engineers are busy wiring up these testing systems, they're not working on what matters most, improving your models and shipping new features that users actually want.
A basic prototype of an in-house evaluation system can cost anywhere from $14,200 to $28,100 (284 hours at $50 to $99 per hour). After the initial build, you will continue to pay for infrastructure scaling, metrics updates, and edge case adaptation, all of which subtly increase your budget. You’ll find developers and data scientists pulled off core model work to fix pipelines and scoring inconsistencies, slowing down release cycles.
As your models and datasets expand, in-house build systems can buckle under pressure, triggering unforeseen delays and rework. Custom tools often don't have built-in collaboration tools, which makes it hard for PMs, engineers, and other stakeholders to share ideas easily. And when you finally need enterprise-grade audit logs or compliance support, surprise licensing and support cost can put you into budget overruns and customers less confident.
In this guide, we’ll break down a real-world ROI analysis with concrete numbers and timelines to help you decide when it makes sense to build your own evaluation platform and when buying is the smarter choice.
Executive Summary
Total Cost of Ownership comparison over 3 years
A recent analysis by OpenLedger on building in-house real-time reporting layers provides a clear financial breakdown for the build vs. buy decision:
Building in-house: A recent ROI calculator for internal reporting layers puts three-year TCO between $850,000 and $1.65 million; its midpoint ($1.25 million) maps to roughly $300 k upfront development plus about $200 k yearly for maintenance and compliance.
Buying a SaaS: The same study shows a 3-year range of $87,000–$420,000 for subscription-based solutions, even the high end is 60 – 80% cheaper than an internal build.
Time-to-value analysis: 6 months vs 2 weeks
Building an in-house evaluation pipeline typically requires 3-4 months of iterative development. Your engineering team goes through multiple cycles of design, implementation, testing, and refinement, each iteration requiring specialized expertise in ML evaluation frameworks, data pipeline architecture, and performance optimization.
Vendor platforms can spin up in as little as two weeks, many SaaS offerings report full deployment within 1–2 weeks letting teams begin model testing almost immediately.
Team productivity impact: 40% efficiency gain
Offloading evaluation tooling allows AI teams to shift around 40% more of their time back to model research and feature work, based on industry case studies.
Ready-made dashboards, built-in collaboration, and automated reporting minimize context switching, so engineers and data scientists stay focused and aligned.
Risk mitigation factors
SaaS systems include service-level agreements, security certifications, and audit logs. They assist the business in achieving compliance criteria without imposing additional labor.
As data volume increases, suppliers manage scalability, version upgrades, and infrastructure health. This reduces the likelihood of incurring costs for unexpected maintenance or interruptions.
True Cost of Building AI Evals In-House
At first glance, building an AI evaluation system in-house may seem like a good idea, but when you factor in the costs of development salaries, cloud bills, monitoring fees, and maintenance, the costs add up quickly.
3.1 Development Costs Breakdown
When you look at the costs of developing AI evaluation, salaries take up most of the budget:
Senior ML engineer, US: typical range $130,000 to $200,000 base per year, with recent averages near $160,000 and upper bounds around $200,000.
DevOps engineer, US: averages cluster around $125,000 to $150,000 base per year across major trackers
UI or UX designer, US: averages commonly fall near $110,000 to $125,000 base per year.
3.2 Infrastructure costs
Compute and GPUs: An AWS p4d.24xlarge with 8 A100 GPUs is about $32.77 per hour on demand, which is roughly $23,900 for a 24x7 month, so one training-grade GPU node can add a five-figure monthly line item on its own.
Storage: S3 Standard is $0.023 per GB per month for the first 50 TB, so 10 TB is about $230 per month and 50 TB is about $1,150 per month.
Example sizing note: If usage spikes, each additional p4d.24xlarge adds about $23,900 per month at on-demand rates, while storage scales linearly with data volume at $0.023 per GB in the common S3 Standard tier.
3.3 Monitoring and security tools
Observability: Datadog Infrastructure Monitoring starts around $15 per host per month on annual Pro plans and about $23 per host per month on annual Enterprise plans, with APM and logs billed separately.
Compliance stack: A typical SOC 2 effort spans readiness, risk assessment, pentest, tooling, and the formal audit, with a total budget often falling between $30,000 and $50,000 depending on scope and size.
Penetration testing: Market rates commonly range from $5,000 to $50,000 per engagement, with complex scopes exceeding $50,000 in many cases.
Figure 1: Hidden Expenses of In-House AI Evaluation
Future AGI Platform: Investment and ROI Analysis
4.1 Future AGI pricing
Future AGI operates on a flexible pay-as-you-go model, so you only pay for what you use. This approach provides transparency and ensures that costs scale predictably with your team's activity.
Free plan: This plan is $0/month for up to 3 seats and includes all core features for building, observing, and improving your models. It operates on a usage-based model with a generous free tier for services like traces and data storage. You only pay if you exceed these free limits, making it a true pay-for-what-you-use starting point.
Pro plan: $50/month for up to 5 seats, adds alerting, dashboards, error localizer, and eval feedback; billed monthly as per usage with two months free on annual commitment.
Enterprise Plan: Custom pricing with volume discounts, SLAs, on-prem/self-hosted options, SSO, and compliance certifications.
Free trial: custom access for a pre-determined period so you can test every capability before committing.
4.2 Same‑day implementation
Account and keys: Create the organization, add API keys, and invite the team in minutes.
Connect SDKs: Link OpenAI, Anthropic, or Hugging Face through Future AGI’s lightweight clients to begin capture immediately.
Enable tracing: Turn on end‑to‑end tracing to see cost, latency, and anomalies without custom pipeline work.
Turn on evaluators and guardrails: Start collecting metrics and apply safety filters to production endpoints out of the box.
Go live: Dashboards, alerts, and incident views are ready the same day, backed by vendor‑managed infra and enterprise options when needed.
Total implementation cost: Minimal—subscription fees start at $0–$50/month plus standard onboarding time, versus six months of full-time engineer salaries for in-house builds.
3-Year TCO Comparison
5.1 In-House Solution (Total Cost of Ownership ) TCO for 3 Years
Costs for Year 1 in-house
$70,500 for development (salaries for ML engineers, DevOps, and UI/UX)
Infrastructure: $36,000 (about $3,000 a month for networking, storage, and cloud servers)
Maintenance: $25,000 (about 20–30% of the development budget for bug fixes and new features)
The total for the first year is $131,500
Costs for Years 2 and 3 in the House
Ongoing Development: $40,000 (small changes and improvements to features every year)
Scaling the infrastructure: $60,000 (more servers, autoscaling, and backup)
Maintenance and Support: $35,000 (security patches, compliance audits)
The total for years 2 and 3 is $270,000.
Total for 3 Years in the House: $401,500
5.2 Future AGI Platform TCO for 3 Years
The Future AGI Pro Plan: $50 a month for 36 months = $1,800
Implementation: $0 (included onboarding, training, and consulting support).
Total for 3 years: Approximately $1,800 (base) + variable usage, typically lower than fixed alternatives. This is typically lower than fixed alternatives due to the pay-as-you-go structure and the use of proprietary Future AGI models, which provide evaluation inference at a very low cost.
5.3 ROI Calculation and Savings
Total savings: $401,500 (in-house) – $1,800 (Future AGI base, excluding low variable usage) = $399,700.
ROI: = ($399,700 / $1,800) × 100 = 22,206%, reflecting the low entry point and scalable costs.
Payback Period: Less than a month (in-house averages $11,153 per month vs. Future AGI's ~$50 base + custom minimal usage per month).
I know a lot to digest, let’s see it in table form.
Category | In-House (36 Months) | Future AGI Pro (36 Months) |
Development & Staffing | $70 500 (Year 1) $40 000/year (Y2–Y3) | n/a |
Infrastructure & Cloud | $36 000/year | Included |
Maintenance & Support | $25 000 (Y1) $35 000/year (Y2–Y3) | Included |
Scaling & Upgrades | $60 000/year (Y2–Y3) | Included |
Implementation & Onboarding | n/a | Included ($0) |
Subscription Fee | n/a | $50/month × 36 = $1,800, with usage‑based metering |
3-Year TCO Total | $401 500 | $1,800 base + usage‑based metering |
Net Savings (In-House – Future AGI) | n/a | $399,700 using base subscription only; actual savings vary with usage |
Table 1: 3-Year Total Cost of Ownership (TCO) Comparison
Build vs Buy Comparison
6.1 In-House Development Risks
Technical risks: Roughly 70–85% of AI initiatives miss their timelines or fail to hit key objectives, and around 67% of bespoke infrastructure projects run past their original schedule.
Talent risks: Most internal evaluation stacks are built on top of open-source frameworks (LangChain, Hugging Face, FastAPI, etc.). But, losing ML or DevOps engineer mid-project can bring development to a halt for weeks; in fact, 89% of open-source projects lose a core contributor at least once and 70% do so within their first three years.
Scaling risks: As data volumes grow, performance bottlenecks emerge: over 40% of custom AI stacks require urgent re-engineering before they can reliably operate at production scale.
6.2 Future AGI Risk Mitigation
Proven platform: Trusted by over manyAI teams in finance, healthcare, and e-commerce for real-world deployments.
Compliance built-in: GDPR, SOC 2, and enterprise-grade security are standard—no extra engineering work needed.
24/7 support: Engineers keep an eye on your infrastructure and tackle incidents around the clock.
SLA guarantees: 99.9% uptime guaranteed.
Building Accurate Evaluation
Building an AI evaluation system is tough, but building one that produces accurate and reliable metrics is even harder. Here’s why in-house solutions often fall short and how Future AGI gives you a decisive advantage.
7.1 The In-House Challenge
When teams build their own evaluation tools, they quickly discover that accuracy is a moving target:
Time-Consuming Frameworks: Creating a truly accurate evaluation framework from scratch takes months. You have to define metrics, write complex scoring logic, and handle countless edge cases. This is time your engineers could be spending on your core product.
Generic Metrics Don’t Work: Simple metrics like pass/fail or keyword matching aren't enough for today's AI models. You need to measure things like semantic similarity, relevance, and factual correctness, which require advanced models and algorithms to get right.
The “Eval as a Product” Trap: To achieve reliable accuracy, your evaluation system needs to be treated like its own product. It requires a dedicated roadmap, constant updates, and a team to maintain it. This adds significant overhead and distracts from your main business goals.
7.2 The Future AGI Advantage: Best-in-Class Accuracy from Day One
Future AGI’s platform is built to deliver highly accurate evaluations out of the box, saving you months of effort and giving you results you can trust.
Proprietary Evaluation Models: Future AGI uses its own state-of-the-art models specifically trained for evaluation tasks. These models provide best-in-class accuracy for measuring everything from basic correctness to complex nuances like tone and style.
Pre-Built and Custom Evals: You get access to a library of pre-built evaluators for common tasks like summarization, question-answering, and sentiment analysis. You can also create custom evaluators tailored to your specific needs, all while leveraging Future AGI’s powerful underlying models for scoring.
Focus on What Matters: By using Future AGI, you offload the entire burden of building and maintaining an accurate evaluation system. Your team can focus on interpreting the results and improving your models, not on debugging evaluation scripts.
Team Productivity and Efficiency Gains
8.1 Developer Productivity Metrics
Switching to Future AGI delivers immediate and measurable productivity benefits by automating evaluation work, accelerating feedback, and common engineering task.
Setting up for evaluation: It takes teams about two weeks to get a full testing pipeline running, but only two hours.
Model testing cycles: What used to take a week now happens every day, which speeds up feedback on new tests.
Incident response time: With automated alerts and error localization, the average time to fix AI problems goes from hours to minutes.
Onboarding new team members: Instead of four weeks of hand-holding and paperwork, new hires can get up to speed in about three days with guided in-app tutorials and templates.
8.2 Quantified Productivity Benefits
Time saved for engineers: Each ML engineer gets back about 15 hours a week that they used to spend setting up, maintaining, and fixing bugs.
Faster iteration cycles: Teams can test models three times faster, which means they can turn ideas into confirmed results three times faster.
Reduced context switching: Instead of switching between dashboards, scripts, and deployment tasks, engineers can focus on working on models.
The value of every engineer: An ML engineer earning $75 per hour can work an additional $58,500 annually if they save 15 hours per week.
Real-World Case Studies
Case Study: Mid-Size AI Startup
Challenge: An AI startup was building a cutting-edge meeting summarization product, but their engineering team was stuck in a six-month loop of building and fine-tuning custom evaluation pipelines. The product launch was delayed twice, and their best engineers working with maintenance instead of innovation.
Solution with Future AGI: Instead of working with scripts, the team switched to Future AGI and was live in under 48 hours. They plugged the SDK into their existing prompt library, attached preset "summary" and "faithfulness" evaluators, and set up automated alerts using guardrails.
Results:
Launched 4 Months Ahead of Schedule: By eliminating the evaluation bottleneck, they beat competitors to market and captured a critical window of opportunity.
Saved $180,000 in Engineering Costs: They reallocated the equivalent of two senior engineers' salaries for six months directly back into core product development.
Achieved 3x Faster Iteration: Model testing cycles shrank from weekly to daily, allowing them to rapidly improve summary quality based on near-instant feedback.
Case Study: Enterprise AI Team
Challenge: A large enterprise AI division was drowning in complexity. Their internal evaluation system couldn’t handle the scale of over 50 different language and vision models. Each new model required manual pipeline adjustments and expensive server upgrades, leading to inconsistent metrics and frustrated data scientists.
Solution: The company adopted Future AGI's Enterprise Plan. Within three weeks, their IT and data science teams connected every model endpoint to a single, unified observability layer using the Model Context Protocol (MCP). This gave them a centralized "single source of truth" for all evaluation data.
Results:
70% Reduction in Infrastructure Costs: They replaced their sprawling, self-hosted servers with a single SaaS plan, slashing their cloud bill.
5x Faster Model Deployment: With a standardized evaluation pipeline, new models and updates were released every two days instead of every two weeks.
Drastic Uptime Improvement: System reliability jumped from a shaky 94% to a solid 99.9% backed by SLAs, virtually eliminating production incidents.
Effortless Compliance: Enterprise-grade security features like GDPR and SOC 2 were built-in, freeing the team from the constant pressure of internal audits and compliance checks.
When to Build vs Buy
10.1 Build In-House When
Unlimited engineering resources and long timelines: If you can give a full team 6 to 12 months of work without taking them away from their main AI work, it makes sense to do it in-house.
Core competitive advantage lies in evaluation infrastructure: The evaluation infrastructure is the main source of competitive advantage: If you use custom metrics or proprietary scoring, it's important to own every line of code.
Strict data locality requirements that can’t be met: If regulations force you to keep all data on-premises with no vendor access, only your own solution can comply.
10.2 Choose Future AGI When:
When to buy AI evaluation platform: In 90 % of cases, teams need speed over building from scratch.
Time-to-market is critical: Future AGI spins up in under two weeks versus six months for custom builds .
Want to focus engineering on core AI innovation: Offload dashboards, alerts, and maintenance so your ML experts stay on modeling.
Extremely unique evaluation requirements (rare edge cases): If you have one-off metrics no vendor supports, custom code may win otherwise, buy.
Need proven, scalable infrastructure: Future AGI serves 100+ teams, scales to millions of requests, and avoids your re-architecture headaches.
Require enterprise-grade security and compliance: Built-in GDPR, SOC 2, and SSO come out of the box, no audit-delay risk.
Conclusion
Over 3 years, choosing Future AGI over an in-house build delivers a $394,700 savings and dropping your TCO from $401,500 to just $6,800 . You also shift from a six-month development delay to live pipelines in under two weeks.
With Future AGI’s platform, you gain 99.9% uptime backed by SLAs and battle-tested performance across 100+ teams , while in-house efforts carry a 85% project-failure risk. That reliability slashes emergency fixes and cuts compliance headaches, so your team avoids surprise audits and security gaps.
By offloading evaluation infrastructure, your ML engineers reclaim around 15 hours per week for core model work instead of maintenance. Faster feedback loops and fewer incidents mean you keep innovation front and centre, not debugging dashboards.
Next Step
Calculate Your ROI with Future AGI to see your personalized savings and break even point .
Then Start Your Free Schedule an ROI Consultation for a guided cost analysis . These steps ensure you move from uncertainty to impact fast, secure, and budget-friendly.
Frequently Asked Questions (FAQs)
