How to Maximize ROI on Inference Infrastructure

AI inference is no longer just a back-end concern; it has become a core business function that drives cost efficiency, faster time-to-market, and your competitive advantage.

As ML and LLM infrastructure accelerates toward a projected $356 billion global market value, ROI expectations from AI deployments across the C-suite and external investors have continued to rise in tandem. Leadership, now more than ever, expects tangible proof that investments in AI are delivering measurable business value.

For AI leaders, the mandate is clear: it’s not enough to simply deploy models. Deployments must show bottom-line impact, keep costs predictable, and scale without introducing new security risks. This guide breaks down how your enterprise can unlock the maximum ROI from its inference infrastructure.

The ROI Gap in Inference Infrastructure#

For many enterprises, inference infrastructure fails to meet its ROI promise. Despite deployment being an initially substantial investment, initiatives frequently stall in “pilot purgatory” or overshoot even generous budgets without delivering measurable outcomes.

The causes are consistent across industries:

• Underestimated total cost of ownership: Beyond GPU pricing, enterprises often overlook compliance, licensing, and integration costs. These hidden expenses inflate budgets and eat into projected ROI.
• Wasted compute from idle or over-provisioned GPUs: Without efficient elastic scaling, enterprises keep GPUs running “just in case” of spikes. This stopgap approach to managing demand ensures availability but drives up spend with little added value.
• Lack of ROI tracking frameworks: Many enterprises launch models without baselines for success. Without clear KPIs, it’s hard to connect deployments to business impact, making it easier for projects to be deprioritized.
• Fragile production operations: Inference infrastructure that lacks robust monitoring and observability can’t reliably detect issues like model drift or latency degradation. The result is unpredictable and unreliable performance that undermines the user experience.
• Scarcity of specialized inference talent: Skilled inference engineers are expensive and difficult to retain. Stretching limited talent across multiple projects delays launches and introduces operational risk.

These issues can transform even well-funded AI programs into expensive experiments rather than scalable business drivers. For executives, the missed ROI isn’t just financial — it’s strategic. Slowed time-to-market and fragile deployments erode competitive advantage at the very moment industry peers are racing ahead.

How the Build vs. Buy Question Widens the ROI Gap#

One of the biggest drivers of missed ROI is the decision to build inference infrastructure in-house. On paper, building gives AI leaders full sovereignty, tighter alignment with internal standards, and freedom from vendor lock-in. In practice, however, it often magnifies the very challenges that make inference infrastructure difficult to operationalize.

The hidden costs show up quickly:

• Delays from manual setup: Standing up infrastructure and pipelines can take months before models are production-ready, slowing down initial time-to-market.
• Specialized talent costs: Infrastructure specialists with deep AI deployment expertise command salaries 30–50% higher than standard DevOps roles, inflating budgets.
• Slowed iteration: Engineers can spend entire cycles maintaining and optimizing systems, rather than experimenting with models. This problem is exacerbated when teams are scaling multiple workloads.
• Operational risks: As workloads expand across dozens of models and regions, misconfigurations, poor observability, or inefficient routing can derail production deployments, even in enterprises with strong engineering teams.

A prime example of these roadblocks manifesting is Revia, a healthcare technology company building phone calling agents that rely on LLM inference. Its team initially built custom pipelines on vanilla Kubernetes, where scaling a workload could take nearly 30 minutes, making true autoscaling impossible. Optimization work quickly consumed weeks of engineering cycles that could have gone to product development. After switching to the Bento Inference Platform, Revia reduced scaling to about one minute (roughly 25× faster) and freed engineers to focus on shipping new features instead of firefighting infrastructure.

How to Deploy High-ROI Inference Infrastructure#

High-ROI inference infrastructure comes from treating model deployment as both a technical and a business initiative. Enterprises that see strong returns approach inference infrastructure with the same rigor they apply to other strategic investments. That means focusing on a set of proven strategies that maximize returns while keeping costs and compliance in check.

Tie every AI project to clear business outcomes#

Defining measurable KPIs won’t guarantee business value, but it makes proving, or disproving, impact possible. Useful examples include:

• Cost per prediction: This KPI quantifies efficiency, highlights waste, and provides a clear lever for finance leaders who want visibility into infrastructure spend.
• Uptime and latency metrics: Consider connecting model performance directly to customer experience and SLA commitments. Even minutes of downtime can translate into lost revenue or churn.
• Customer retention and engagement rates: This indicates whether AI-powered features are creating lasting value rather than short-lived novelty.

But setting metrics is only the first step. To drive real ROI, enterprises must connect those metrics back into workflows instead of isolating them in dashboards. For example, if a fraud detection model shows a spike in false positives, those insights should trigger updates to product workflows and policies, not just sit in logs.

Metrics also need to evolve as priorities shift: what counts as “success” for a customer support chatbot (fast resolution times, higher CSAT) may not align with the priorities of a fraud detection pipeline (low false negatives, strong recall). Both should be revisited regularly as the business grows.

Optimize models and infrastructure for each use case#

Not every workload requires a frontier-scale foundation model. Aligning model size and infrastructure to each specific use case ensures the right balance of cost, performance, and accuracy.

Smaller, fine-tuned models are often better suited for cost-sensitive or latency-critical tasks like classification or summarization. Larger foundation models, by contrast, make sense when accuracy and nuance outweigh compute cost, such as in complex retrieval-augmented generation pipelines or agentic workflows.

The hardware layer is just as important. Choosing the right mix of GPUs, TPUs, CPUs, or even edge devices allows teams to balance memory, throughput, and concurrency. Paired with dynamic autoscaling and scale-to-zero capabilities, this approach prevents overprovisioning and minimizes idle GPU spend.

We’ve seen this play out with Revia, who shifted away from a one-size-fits-all approach and optimized the match between models and infrastructure. The result was a 6x reduction in GPU-related costs along with significant gains in throughput. For enterprises under budget scrutiny, these kinds of optimizations can mean the difference between AI operating as a cost center and becoming a profit driver.

Automate deployment and MLOps#

Manual deployment processes are a silent killer of ROI. Every duplicated configuration file, manual log check, and hand-coded pipeline consumes engineering time that could otherwise go toward innovation. Automating deployment workflows and MLOps reduces this overhead, ensures reproducibility, and accelerates iteration cycles.

The most effective approaches include:

• CI/CD pipelines: Standardized pipelines make every deployment reproducible and auditable, with built-in rollback when issues arise.
• Centralized monitoring: Automated tracking of latency, drift, and utilization provides early warnings before performance issues reach production. Additionally, you need insights into inference-specific metrics for LLMs like TTFT and ITL to optimize for specific use cases.
• Strict version control: Managing data, features, and models under version control creates accountability and prevents the “it worked on my machine” problem.

Let’s look at leading retail tech company, Neurolabs, as an example. By automating deployment and MLOps, Neurolabs accelerated time-to-market by nine months, achieved a 3x increase in deployment speed, and now manages 10 model iterations per week, a cadence that would be impossible through manual workflows.

Build security-first, compliance-ready infrastructure#

For enterprises operating in regulated industries, compliance isn’t optional, but it also shouldn’t stall innovation. The key is securing inference infrastructure from the ground up so models meet regulatory requirements and protect sensitive data without creating friction for developers.

Here are a few steps that can help you achieve this goal:

• Run workloads in private VPCs or on-prem: Keeping data and models within environments you fully control ensures sovereignty and simplifies regulatory approval.
• Automate compliance checks: Continuous auditing and anomaly detection catch policy violations early, reducing the risk of failed certifications or costly fines.
• Enforce strong access controls: Features like RBAC, IAM/KMS, and mandatory audit logs provide accountability across teams and align with standards such as SOC 2, HIPAA, and ISO 27001.

This strategy is already in use at Yext, which deploys models across multiple regions using a cloud-agnostic approach. By keeping workloads close to customers while aligning deployments with local data residency requirements, Yext ensures compliance across global markets without slowing down development.

Implement financial accountability for inference spending#

Strong ROI depends on treating inference costs with the same rigor as other enterprise investments. This means building processes that make costs visible, actionable, and directly tied to business outcomes.

Steps you can take to implement financial accountability include:

• Tracking infrastructure cost in real time: Continuous monitoring ensures teams know exactly what deployments are costing at any given moment, making inefficiencies easier to spot and address.
• Setting anomaly alerts: Automated notifications for unusual traffic spikes, latency changes, or GPU usage trends help teams act before costs escalate.
• Forecasting demand and aligning infrastructure: Usage patterns shift over time. Forecasting enables enterprises to scale resources up or down proactively, avoiding wasted spend during low-demand windows.
• Balancing cost, quality, and speed: Clear visibility into this trade-off helps leaders decide when to optimize for cost efficiency versus performance, and ensures infrastructure spend aligns with business priorities.

The enterprises that succeed treat inference spend as a managed investment, not a black box expense, giving finance leaders confidence and freeing AI leaders to innovate responsibly.

Empower cross-functional collaboration and iteration#

Driving ROI from inference infrastructure requires more than technical excellence alone. Breaking down silos between teams accelerates innovation, aligns technical work with business goals, and ensures models deliver sustained value.

The most effective approaches include:

• Aligning data science, infrastructure, and business stakeholders: When these groups plan deployments together, models are designed not just for accuracy but also for customer experience, cost efficiency, and revenue impact.
• Refreshing and testing models frequently: Regular iteration ensures systems adapt quickly to shifting data, compliance changes, or emerging drift. Enterprises that make iteration part of the process see fewer surprises and more reliable ROI.
• Using shared dashboards and KPIs: Unified visibility across teams promotes transparency and accountability. With cost, performance, and adoption metrics in one place, leadership can quickly spot gaps and engineers can avoid firefighting.

Neurolabs applied this approach by restructuring collaboration between its data scientists and business teams. Instead of being bogged down in infrastructure tasks, data scientists were able to focus on building compound AI systems that directly supported customer-facing initiatives.

The Bento Advantage: Turning Strategy into Execution#

Enterprises often find it difficult to operationalize these strategies consistently at scale. That’s where Bento comes in. The Bento Inference Platform combines enterprise-grade governance with optimized infrastructure, enabling AI leaders to move from high-level strategy to measurable business results, without the risks of building in-house.

Rapid deployment and flexibility#

With one-command deployments and CI/CD integration, Bento’s Inference Platform cuts time-to-production from weeks to just 1–2 days. Flexibility is built in: enterprises can serve any open-source or custom model from any major framework, chain multiple models together, and integrate custom logic without rewriting infrastructure. This agility ensures teams don’t just deploy faster; they innovate faster.

Performance optimization for LLMs#

Bento’s Inference Platform provides tailored performance tuning, including speculative decoding, KV cache offloading, and distributed inference. These capabilities maximize throughput and minimize latency, ensuring LLM-powered applications deliver responsive, cost-efficient user experiences.

Dynamic scaling with cost control#

Concurrency-based autoscaling, scale-to-zero, and fast cold starts prevent wasted compute while ensuring capacity during spikes. By aligning infrastructure to demand in real time, enterprises avoid the cost drain of idle GPUs and maintain predictable ROI even as workloads scale.

Unified observability and accountability#

Enterprises gain full visibility into inference operations with real-time dashboards tracking cost, GPU utilization, and performance metrics, including those specific to LLMs. This unified observability layer provides the accountability executives require, while giving engineering teams the insight to optimize deployments proactively.

Developer-friendly, open-source foundation#

BentoML originated as an open-source project, and that DNA remains. Developers benefit from an intuitive experience with open APIs and pre-optimized models, shortening time-to-market and reducing the friction of adoption. By combining a developer-focused UX with enterprise readiness, BentoML bridges the gap between experimentation and production.

Security-first, compliance-ready architecture#

With BYOC, enterprises can keep workloads inside their own VPC or on-prem environments, maintaining sovereignty and compliance while still benefiting from BentoML’s automation. Security features like RBAC, audit logs, and secrets management are built in. Governance isn’t bolted on as an afterthought; it’s part of the platform’s foundation.

Support for complex inference pipelines#

AI leaders rarely deploy models in isolation. The Bento Inference Platform’s custom serving architecture natively supports async tasks, batch jobs, and multi-model pipelines. These capabilities map directly to the tactical strategies discussed earlier, from frequent iteration to cost-efficient scaling, and help enterprises deliver real business outcomes at scale.

Are You Ready to Deploy ROI-Positive Inference Infrastructure?#

Maximizing ROI doesn’t come from pouring more money into GPUs; it comes from making inference a repeatable, strategic capability that accelerates business impact.

Bento’s Inference Platform makes that shift both achievable and measurable. With one-command deployments, tailored LLM performance optimizations, and fast autoscaling, enterprises cut costs while accelerating time-to-market.

Need help with inference infrastructure? Contact us to streamline deployment, prove ROI, and scale AI securely.