CIOs must rearchitect observability to keep pace with AI

AI engineering is pushing past the limits of traditional software development and exposing a gaping hole in enterprise infrastructure: observability. CIOs pushing to operationalize AI across lines of business must now rethink how their infrastructure evolves to support AI-first development.

In response to this fundamental shift, Bay Area-based startup Braintrust—an end-to-end platform for AI-app building—has built Brainstore, a database optimized for large-scale AI workloads.

Early-adopting enterprise leaders have called out their inability to explain and govern AI as the biggest barrier to deploying generative AI (GenAI), as highlighted in our foundational report: Your Generative Enterprise™ playbook for the future.

The scale of the challenge is only growing as workloads explode.

Exhibit 1: Observability is essential to tackle enterprise leaders’ explainability, governance, and trust concerns

Sample: N=104 early-adopting enterprise leaders; percentages=the number of leaders ranking each barrier as either 1 or 2 for impact
Source: HFS Research, 2025

Legacy observability tools were never designed for AI—and the cracks are showing

AI development introduces massive, unstructured, and fast-changing data that overwhelms traditional observability stacks.

• Log sizes have ballooned, with p95 logs growing from 500KB to nearly 3MB within months. Token-heavy LLM traces often span megabytes and thousands of steps.
• Dynamic query needs, such as full-text search or filtering on semi-structured fields, break schema-bound query engines.
• Security and privacy constraints demand self-hosting, but most tools built for SaaS delivery are complex and costly to run on-prem.

A typical full-text search takes 240 milliseconds in Brainstore, compared to 78,963 milliseconds in a general-purpose warehouse (that’s 329x slower). Write latency and read visibility also show similar multi-second gaps. That’s the difference between a two-minute debug cycle and a much longer and more frustrating slog.

The reality is that the observability layer built for microservices is choking under the weight of AI workloads.

Brainstore rethinks observability from the ground up for the AI era

Brainstore isn’t a bolt-on or a schema tweak. It’s a vertically integrated, object-storage-native database built for AI-scale logs—and it’s what Braintrust runs on at customers such as Replit, Instacart, Stripe, and Zapier.

What sets it apart:

• Built for scale: A stateless, Rust-based binary that delivers sub-second queries across terabytes of semi-structured data. Rust is a modern systems programming language designed for performance, reliability, and memory safety.
• Full-text first: With a Tantivy-based inverted index (a lighter, faster, and more modern architecture) and real-time write-ahead log, Brainstore handles complex searches in milliseconds.
• Easy to own: Set it up in under a day, reusing existing infra such as S3, Postgres, and Redis. No steep ops curve. No exotic dependencies.

This design enables AI engineers to iterate faster, fix issues faster, and scale products faster without hitting infrastructure bottlenecks. In a space where agility drives accuracy and customer experience, that edge compounds quickly.

The developer experience is now a business differentiator

Enterprise CIOs must recognize that AI engineering moves at a fundamentally different speed. Braintrust’s broader developer platform accelerates the journey from prototype to production, with claims of going from sub-50% to over 90% accuracy in 2–3 months. Local testing, automated evals, real-time feedback, and seamless LLM swapping make the development lifecycle potentially radically faster. But none of this works unless the observability layer can keep up.

Traditional observability tools don’t just slow engineers down but also the entire business. Triage cycles stretch from minutes to hours. Root cause analysis becomes guesswork. Privacy and security risks go unmanaged. In contrast, Braintrust enables AI teams to triage 30 issues per day—10 times more than legacy approaches—and cut deployment cycles from a month to a single day.

Observability must evolve into a strategic AI enabler

Observability, long treated as IT plumbing, is now a strategic enabler of AI agility. Enterprises that treat it as an afterthought will fall behind—unable to iterate, assure safety, and compete.

AI-native infrastructure breaks from the general-purpose mold. Just as GPUs have mostly displaced CPUs for model training, AI-shaped observability will displace cloud-native generalism for AI operations.

CIOs must act now to deliver faster, safe, and more scalable AI systems

Braintrust’s Brainstore shows what’s possible when observability is purpose-built for the AI era—lightweight, high-performance, and built for the way AI engineers work today.

CIOs must act now: audit where observability is limiting AI delivery, pilot AI-native alternatives such as Braintrust, and invest in the infrastructure that drives velocity, safety, and scale. Modernizing observability is now a strategic imperative for delivering faster, safer, more scalable AI systems.

The Bottom Line: Rearchitect your observability stack to match the demands of the rise of AI-first development.

Enterprise CIOs must rearchitect their observability stack to match the demands of AI-first development. Generic tools built for microservices are choking under the weight of AI-scale logs, dynamic data structures, and real-time iteration needs.

Observability isn’t just about uptime anymore—it’s the foundation of your AI development advantage.