Every organization deploying machine learning models eventually faces the same question: should we build our own AI observability infrastructure or buy an existing solution?
The question seems simple. In practice, it involves tradeoffs that are easy to underestimate. Organizations that make this decision poorly waste resources, either building what they could have bought or buying what does not meet their needs.
The Appeal of Building
Building observability infrastructure in-house offers genuine advantages.
Complete Customization
Custom solutions match organizational needs exactly. Integration with existing data infrastructure follows internal standards. Dashboards reflect how teams actually think about their models. Alerts trigger based on metrics that matter for specific use cases.
Vendor solutions, by contrast, serve many customers with varying needs. They necessarily make compromises that may not align with any single organization's priorities.
No Vendor Dependency
Built solutions do not create external dependencies. Pricing does not change based on vendor decisions. Features do not disappear because a vendor pivots strategy. Migration costs do not accumulate as vendor lock-in deepens.
This independence has strategic value. Organizations that depend on vendors for critical infrastructure accept risks they cannot fully control.
Deep Integration
Custom observability can integrate deeply with proprietary systems. Internal data formats, training pipelines, and deployment infrastructure may not map cleanly to vendor APIs.
Building allows solutions that span boundaries that vendor products cannot cross. Monitoring can begin in training environments and extend through production without integration gaps.
The Hidden Costs of Building
These advantages come with costs that teams consistently underestimate.
Initial Development
Building observability infrastructure requires significant engineering effort. Data pipelines must capture relevant signals. Storage systems must handle monitoring data at scale. Visualization tools must present information usefully. Alerting systems must route notifications appropriately.
Each component seems tractable in isolation. Together, they constitute a substantial project. Teams that estimate "a few weeks" often discover that months pass before basic functionality works.
Ongoing Maintenance
Initial development is only the beginning. Observability infrastructure requires continuous investment.
New model types need new monitoring capabilities. Performance requirements increase as model counts grow. Security patches require ongoing attention. Team members who built the system eventually leave, and others must learn to maintain their work.
This maintenance burden is easy to ignore during initial planning. It becomes impossible to ignore once the system is running and absorbing engineering time that could go elsewhere.
Opportunity Cost
Engineers building observability infrastructure are not building models or features. This tradeoff is straightforward to calculate but often ignored.
If your organization's competitive advantage comes from better models, every engineer-month spent on infrastructure is an engineer-month not spent on modeling. The infrastructure may be necessary, but it is not what creates differentiated value.
Catching Up
The observability space evolves rapidly. Vendor products improve continuously as dedicated teams add capabilities, incorporate customer feedback, and address emerging challenges.
Internal teams cannot match this pace. They have other priorities. They lack the breadth of customer feedback that vendors receive. Features that vendors deliver routinely require significant effort to replicate internally.
Over time, built solutions often fall behind vendor products. The initial customization advantage erodes as vendors add the features that mattered.
The Case for Buying
Buying observability infrastructure offers different advantages.
Immediate Capability
Vendor solutions work immediately. Implementation takes days or weeks, not months. Teams begin monitoring models while their custom-build counterparts are still designing systems.
This speed matters when models need to reach production. Every week of delay is a week without the value that monitoring provides.
Accumulated Expertise
Vendor products incorporate lessons learned across many deployments. They handle edge cases that internal teams would discover painfully. They include features that address problems customers did not anticipate.
This accumulated expertise is difficult to replicate. It emerges from diverse experience that no single organization can match.
Resource Efficiency
Vendor solutions let organizations focus engineering resources on differentiated work. Infrastructure that anyone can buy should not consume resources that could create unique value.
This efficiency argument is particularly strong for organizations where ML is one capability among many. Dedicating engineering talent to commodity infrastructure when specialized solutions exist is difficult to justify.
Continuous Improvement
Vendors improve their products continuously. Features appear without internal effort. Performance improves without internal optimization. Security patches arrive without internal security reviews.
This continuous improvement means that buyer organizations benefit from investments they did not make. Their observability capabilities advance even while their teams focus elsewhere.
Making the Decision
Neither building nor buying is universally correct. The right choice depends on organizational context.
Consider Your Scale
Small deployments often do not justify custom infrastructure. The fixed cost of building spreads over too few models. Vendor pricing at low volumes is typically reasonable.
Large deployments change the calculation. Vendor pricing often scales with data volume or model count. At sufficient scale, building may cost less even accounting for maintenance burden.
The crossover point varies by vendor and use case. Organizations should model costs realistically for their expected scale, not just current state.
Consider Your Requirements
Standard monitoring needs are well-served by vendor products. Tracking drift, measuring accuracy, and alerting on anomalies are common requirements that vendors address comprehensively.
Unusual requirements may demand custom solutions. Proprietary data formats, unique regulatory constraints, or specialized model types may not fit vendor capabilities.
Be honest about whether requirements are genuinely unusual. Many teams believe their needs are unique when they are actually standard. This belief leads to building what could have been bought.
Consider Your Team
Building requires specific skills that may or may not exist internally. Data engineering, distributed systems, and frontend development are all necessary. Teams lacking these skills must either hire or accept suboptimal results.
Organizations with strong infrastructure engineering teams are better positioned to build. Those whose strength is in modeling or domain expertise should consider buying.
Consider Your Timeline
Building takes longer than buying. If monitoring is urgently needed, vendor solutions provide faster time to value.
If time permits a longer development cycle, building becomes more feasible. But timelines tend to expand. What seems like ample time at project start often compresses as other priorities emerge.
The Hybrid Approach
Many organizations adopt hybrid approaches. They use vendor solutions for baseline capabilities while building custom components for specific needs.
This approach can capture benefits of both options. Standard monitoring comes from vendors with their accumulated expertise. Specialized monitoring addresses unique requirements that vendors cannot meet.
The risk is complexity. Managing multiple systems, integrating vendor and custom components, and maintaining clear boundaries between responsibilities all require ongoing attention.
Hybrid approaches work best when the boundaries are clear. Vendor solutions handle specific, well-defined scope. Custom solutions handle remaining needs without duplicating vendor capabilities.
Evaluating Vendors
Organizations that decide to buy face their own decision: which vendor?
Capability Fit
Does the vendor support your model types and deployment patterns? Can their solution integrate with your infrastructure? Do their metrics and visualizations match how your teams think?
Proof of concept deployments answer these questions better than sales conversations. Invest time in actual integration before committing.
Pricing Model
How does pricing scale with your expected growth? Are there volume discounts? What happens if you need to reduce usage temporarily?
Understand total cost of ownership, not just initial pricing. Implementation effort, ongoing configuration, and integration maintenance all contribute to true cost.
Vendor Viability
Will the vendor exist in three years? Is their product their main focus or a side project? Do they have sufficient funding and customer traction?
Betting on a vendor that fails or pivots creates painful migration projects. Established vendors with clear business models present lower risk.
Support and Documentation
How responsive is support when problems arise? Is documentation sufficient for self-service? Are there community resources for common questions?
Support quality varies widely. References from similar organizations provide better signal than vendor claims.
The Strategic Frame
Beyond immediate practicality, the build vs buy decision has strategic implications.
Core vs Context
Some capabilities are core to competitive advantage. Others are context that enables core activities but does not differentiate.
For most organizations, observability is context. It enables effective ML deployment but does not itself create differentiated value. Context capabilities are generally better to buy than build.
Organizations whose core business is ML infrastructure face a different calculation. For them, observability may be core. Building makes more sense when capability directly creates competitive advantage.
Option Value
Vendor solutions preserve options. They can be replaced if better alternatives emerge. Internal resources remain available for other uses.
Custom solutions consume options. Engineering talent is committed. Migration costs accumulate. Flexibility decreases as investment deepens.
AI governance frameworks should consider this option value. The ability to adapt as the landscape evolves has strategic importance beyond immediate capability needs.
Moving Forward
The build vs buy decision deserves careful analysis, not instinctive reactions. "We can build it ourselves" may be true but irrelevant if building is not the best use of resources. "We need to buy" may be false if requirements genuinely exceed vendor capabilities.
Honest assessment of capabilities, resources, and requirements leads to better decisions. Teams that make this assessment carefully avoid both unnecessary building and inappropriate buying.
The observability landscape continues to evolve. Decisions made today should account for that evolution. Whatever choice you make, build in the flexibility to reconsider as circumstances change.
