Should You Build or Buy Your Inference Platform?

In enterprise AI, the first scaling crisis rarely comes from your models — it comes from inference. Costs creep up. Latency spikes. Reliability slips.

Your team needs more control over performance, costs, and security, so the natural instinct is to build your own inference platform. On paper, it promises freedom: tailor every component, run on your own terms, avoid vendor lock-in.

But for inference, the DIY approach rarely delivers the control it promises. Instead, it drains time, budget, and talent, often becoming the very bottleneck it was meant to solve.

The most resilient AI teams have learned this early. They’ve moved to purpose-built inference platforms. These solutions unify deployment, scaling, optimization, and observability into one layer that’s faster to ship models, cheaper to run, and easier to secure.

The Hidden Pitfalls of DIY InferenceOps#

Building your own InferenceOps stack — the practices and tooling required to reliably run, scale, and optimize inference in production — may sound empowering. But what starts as a cost-control play frequently introduces new and more expensive problems.

High complexity and cost#

Standing up a production-ready inference layer is anything but simple. Even a “basic” setup with fast autoscaling and distributed inference can take an experienced team two to three months to design, implement, and stabilize. And that’s before factoring in the continuous tuning required to maximize GPU utilization, right-size resources, and consistently meet SLA targets.

By contrast, Bento Inference Platform’s pre-optimized infrastructure makes it possible to deploy the same setup in less than a day, eliminating months of engineering effort.

Even scaling behavior shows the difference: vanilla Kubernetes can take around 10 minutes to spin up an LLM container, while the Bento Inference Platform does it in less than 30 seconds. That 25× improvement can be the difference between meeting demand and missing it.

Slower time-to-value#

LLM workloads are complex and constantly evolving, and integrating a new model is rarely plug-and-play. When a frontier model drops, DIY teams can spend one to two weeks on integration and testing. This work involves fine-tuning distributed inference strategies, adjusting batching logic, and modifying existing runtime and infrastructure to find the best inference setup for their use case.

By contrast, the Bento Inference Platform offers day-zero access, allowing you to deploy and test in your own environment the same day a model is released. In markets where first-mover advantage is critical, that lost week can mean forfeiting a competitive edge.

Talent drain and delayed innovation#

The expertise required to build and maintain inference platforms with tailored optimization is rare and expensive. Senior infrastructure engineers command 30–50% higher salaries than standard DevOps roles.

When those engineers are pulled into firefighting scaling issues or debugging model servers, they’re not building the AI features that differentiate your product. Over time, that opportunity cost compounds, slowing your ability to innovate.

Compliance and security risk#

For teams in regulated industries, building in-house means taking full responsibility for compliance — from engineering SOC 2, HIPAA, and ISO controls to continuously ensuring they remain valid as the system evolves. 

Every change to your infrastructure can trigger the need for updated documentation, fresh audits, and sometimes full re-certification. The stakes are high: missing even a single control can lead to costly launch delays, regulatory fines, or lasting reputational damage. 

And with inference infrastructure constantly shifting to accommodate new models and optimizations, keeping pace with these requirements quickly becomes an uphill, resource-intensive battle.

Strategic misalignment#

At the end of the day, building inference infrastructure isn’t your competitive advantage. Delivering differentiated AI products is. Every sprint spent on infrastructure is a sprint not spent on the features your customers value.

Leading enterprises recognize this trade-off. Rather than building a large internal infra team, they partner with specialists whose entire focus is inference. This gives them a platform that evolves faster than any in-house team could sustain.

Why Partnering with BentoML Outperforms DIY#

The pattern is clear: what starts as a quest for control becomes a drain on innovation. That's why purpose-built inference platforms are rapidly replacing DIY approaches.

Unlike generic cloud ML platforms that treat inference as an afterthought, the Bento Inference Platform is built specifically for AI inference workloads. It delivers four things DIY inference can’t match: faster deployment, optimized performance and cost, enterprise-grade security and governance, and future-proof flexibility — all without compromising control over your models or data. As a result, your team can move at market speed without sacrificing performance or control.

Faster deployment and time-to-value#

The Bento Inference Platform allows you to serve any open-source or custom models from any framework in minutes with built-in CI/CD pipelines, fast autoscaling, and full versioning support. This means you can move from concept to production in days, not months, without sacrificing reliability or control.

With pre-built Bentos, teams can stand up production-ready services in less than a day, freeing engineering resources to focus on building AI features that deliver customer value.

And the impact shows in the field: when Yext adopted Bento Inference Platform, they cut development cycles by 70%, reduced compute spend by 80%, and scaled to more than 150 models in production. These results demonstrate how faster deployment translates directly into faster time-to-market.

Optimized performance and cost#

The Bento Inference Platform bakes in advanced optimizations such as continuous batching, KV cache offloading, GPU-aware routing, and infrastructure purpose-built for distributed inference. This means you can easily optimize for specific workloads, whether that’s low-latency, high-throughput, or large multi-GPU models, and achieve maximum hardware utilization without endless manual tuning.

Most teams running on the Bento Inference Platform see GPU utilization rates averaging 70% or higher, directly translating into lower costs and more efficient scaling. For example, Rivia reduced infrastructure costs by 6× after moving off their homegrown system, while also improving scaling reliability under heavy load. These optimizations help teams cut infrastructure spend, avoid costly over-provisioning, and ensure mission-critical workloads run at peak efficiency.

Enterprise-grade security and governance#

Purpose-built for enterprise AI teams, Bento Inference Platform meets the most stringent security and compliance standards, including SOC 2 Type II certification. With flexible BYOC (Bring Your Own Cloud) and fully on-prem deployment options, your models and data never leave your controlled environment. Customers in regulated industries such as finance, healthcare, and government can meet strict privacy, audit, and security requirements out of the box.

Beyond deployment flexibility, Bento Inference Platform includes integrated observability and governance tools. It provides the same, or stronger, security posture as an in-house system, without the constant upkeep of engineering and re-certifying compliance on your own.

Future-proof flexibility#

Your inference needs today will not be your inference needs tomorrow. With Bento Inference Platform’s unified compute fabric, you can deploy, scale, and govern inference workloads seamlessly across multi-cloud, hybrid, or on-prem environments, all from a single control plane.

This makes it easy to adapt when requirements shift. Whether it’s GPU shortages, pricing changes, or new compliance rules, Bento Inference Platform lets you re-route traffic and reallocate resources instantly, without re-engineering your stack or locking into a single vendor. The result is infrastructure that stays flexible and ready for whatever tomorrow demands.

Developer velocity#

Day-zero access to new models and one-command deployments mean your developers can experiment, benchmark, and ship without waiting on infrastructure bottlenecks.

And this isn’t just theory. Neurolabs brought products to market nine months faster, saved 70% in costs, and avoided hiring a dedicated infra team. They are shipping new models on a daily basis without any additional infrastructure resources.

Build Innovation, Not Infrastructure#

Infrastructure won’t differentiate you. Innovation will. In an arms race, every delay is ground lost.

Building in-house may feel like control, but it drains your time on endless infrastructure upkeep instead of product innovation. The AI leaders aren’t the ones maintaining bespoke systems; they’re shipping features up to 2× faster by letting purpose-built platforms handle infrastructure complexity.

With Bento Inference Platform, you get the speed, performance, flexibility, and security of an in-house build without the resource drain. Go from concept to production in days, cut GPU spend by up to 80%, and stay fully compliant, all while keeping your models and data under your control.

Book a demo today to evaluate how Bento Inference Platform can help your team scale inference securely, efficiently, and without slowing your product innovation.