The Best Database is No Database

Over the past decade, software companies have optimized their tech stacks for digital experiences—handling web traffic spikes, A/B testing user interfaces, and refining ad targeting algorithms. These architectures were designed for transactional efficiency of SaaS applications, not the raw complexity of physical systems.

Building modern machines presents an entirely different challenge: managing torrents of high-frequency sensor data generated by complex, interdependent systems. Unlike web applications, where failures can often be mitigated with a quick rollback, real-world hardware demands precision, reliability, and real-time decision-making. The databases and infrastructure that power consumer applications weren’t built for this level of complexity.

The market is shifting. Legacy database architectures—where compute and storage are tightly coupled—are no longer viable for modern hardware systems. These constraints become bottlenecks in industries where milliseconds matter, from autonomous systems reacting to sensor inputs to spacecraft processing terabytes of real-time telemetry. True separation—not just within a vendor’s ecosystem, but as a fully modular architecture—is now a requirement.

The implications for hardware companies are profound, particularly in fields where high-frequency telemetry, real-time AI workloads, and the ability to scale dynamically are essential.

The Technical Challenges of Tightly Coupled Architectures

Tightly coupled architectures create rigid dependencies between compute and storage, making it difficult to scale modern aerospace, robotics, and autonomous systems efficiently. Monolithic databases like Postgres, Timescale, and InfluxDB require compute and storage to scale together, meaning that as data volumes grow, organizations must provision more compute even when additional processing power isn’t needed. This leads to unnecessary infrastructure costs and limits the ability to scale workloads dynamically.

Scaling monolithic databases is inherently more expensive and challenging because there are fewer levers to pull. 

Read and write operations share the same compute resources, creating performance bottlenecks as demand increases. In aerospace applications processing high-frequency telemetry or robotics systems performing real-time data analysis, this limitation forces organizations to over-provision compute simply to accommodate storage growth. In contrast, decoupled architectures allow storage to scale independently while compute resources are allocated based on actual read and write demands. This flexibility ensures that systems remain responsive and cost-efficient even as data ingestion rates surge.

Scaling monolithic databases is inherently more expensive and challenging because there are fewer levers to pull.

Beyond performance constraints, tightly coupled architectures increase operational complexity. Many time-series databases, such as TimescaleDB and InfluxDB, struggle with write-heavy workloads at scale. Schema changes and version updates require synchronized deployments across dependent services, increasing regression testing and deployment risks. In mission-critical environments, this slows iteration cycles and makes it harder to adapt to evolving workloads. By separating compute from storage, organizations can scale resources as needed, optimize system efficiency, and eliminate the constraints of monolithic designs—key requirements for building reliable, high-performance machines.

Real-World Impacts of Coupled Architectures

Tightly coupled architectures don’t just create inefficiencies—they create failure points in mission-critical applications.

• Aerospace: High-fidelity spacecraft simulations require synchronized processing across structural, thermal, and aerodynamic models. Many existing databases struggle to ingest and process terabytes of telemetry data in real-time, leading to bottlenecks in mission-critical operations. NASA’s struggles with integration bottlenecks in Mars lander simulations illustrate how data processing inefficiencies translate into costly delays.
• Software-Defined Vehicles: Centralized architectures mean a minor software issue—like a failed OTA update—can disrupt essential vehicle functions. Tesla’s autopilot disengagement issues and Volkswagen’s ID.3 software delays highlight the risks of interdependent systems that can’t be debugged, validated, or updated in isolation.
• Manufacturing: Industrial inspections generate petabytes of sensor data daily. Processing these at scale requires real-time anomaly detection, but coupled architectures lead to idle compute resources, underutilized storage, and inflated costs. Boeing’s transition to digital twin-based quality control reflects the need for a decoupled, scalable infrastructure.
• Autonomous Systems: Sensor fusion in robotics depends on rapid perception and decision-making. When compute and storage are coupled, lag creeps in, leading to slow response times. Failures in Boston Dynamics’ robots navigating complex environments and Amazon’s fulfillment center automation breakdowns underscore how traditional architectures hinder real-time adaptability.

These failures aren’t theoretical—they’re real-world limitations that slow progress, increase operational risks, and drive up costs.

Breaking Free from the Monolith

Historically, database vendors sold tightly coupled solutions: compute engines, storage systems, and query tools packaged into a single product. While many touted “separation of compute and storage,” these offerings rarely escaped the confines of proprietary ecosystems. Today, organizations need more flexibility—architectures built on open standards with interoperability at their core.

A truly decoupled approach brings several advantages:

1. Interoperability: Open formats make it easier to integrate analytics engines, machine learning frameworks, and other data tools. This reduces vendor lock-in, cuts costly egress fees, and allows teams to choose the best-fit solutions without restrictions.
2. Scale What You Need, When You Need It: Compute and storage can scale independently, accommodating massive real-time data streams during peak demand while minimizing costs when workloads decrease. Storage remains stable while compute resources flex dynamically.
3. Process at the Edge: Decoupling storage from compute enables data processing closer to the source. Robotic arms, autonomous vehicles, and industrial sensors can make decisions locally, reducing latency and improving diagnostics without relying on a central database.‍
4. Data Sovereignty: Organizations retain full control over sensitive data, ensuring compliance with regulatory and intellectual property requirements. With decoupled architectures, companies decide where and how their data is stored and accessed—eliminating dependencies on external vendors.

Monolithic systems weren’t designed for AI-driven workflows, forcing organizations to make trade-offs that limit efficiency and flexibility.

AI and the Unbundling Imperative

AI isn’t just another workload—it fundamentally reshapes how data needs to be structured, processed, and retrieved. Traditional architectures, built for static queries and predefined analytics, struggle to support AI pipelines that demand real-time adaptability, scalable data processing, and explainability. Monolithic systems weren’t designed for AI-driven workflows, forcing organizations to make trade-offs that limit efficiency and flexibility.

AI workloads introduce distinct challenges that tightly coupled architectures fail to address:

• Training vs. Inference Optimization: AAI training requires large-scale parallel computation, while inference depends on low-latency execution. Monolithic architectures force these workloads to compete for the same resources, creating inefficiencies. A decoupled approach allows storage and compute to scale independently, optimizing both without unnecessary overhead.
• Real-Time & Historical Analysis: AI-driven decision-making depends on streaming telemetry for real-time analysis and historical datasets for continuous model refinement. In monolithic systems, these workloads compete for the same compute resources, creating bottlenecks. Decoupled architectures remove these constraints, enabling AI to operate on structured, context-rich data without delays.
• Adaptability to Evolving AI Tools: AI tooling evolves rapidly. A rigid, monolithic stack limits flexibility, making it difficult to adopt emerging frameworks or integrate new machine learning pipelines. Modular architectures allow organizations to integrate the latest AI tools without costly overhauls.
• Explainability in Mission-Critical Systems: In aerospace, transportation, and robotics, AI decisions must be AI tooling evolves rapidly. A rigid, monolithic stack limits flexibility, making it difficult to adopt emerging frameworks or integrate new machine learning pipelines. Modular architectures allow organizations to integrate the latest AI tools without costly overhauls.

The shift is already happening. Snowflake reports that over 10% of its revenue now comes from storage-only use cases—a clear sign that businesses are demanding AI-ready, flexible data architectures. AI’s potential is gated by how well data is structured, contextualized, and retrievable. Unbundling compute and storage isn’t just a technical shift—it’s the key to making AI work in real-world, high-complexity environments.

Making the Transition

This shift isn't just about technology — it's about control. Before committing to a data architecture, hardware teams should evaluate their operational goals and constraints:

• Who controls your data? If telemetry is stored in proprietary formats, how does that affect your ability to integrate with new tools, evolving AI models, or future mission requirements? Can you query, analyze, and migrate your data without restrictions?
• Is your architecture built for iteration? Modern machines aren’t static; they evolve. Can your system support continuous testing, real-time updates, and the flexibility to adapt without disruptive overhauls? Or are you constrained by rigid, monolithic infrastructure that slows development cycles?
• What kind of analysis do you need? Real-time anomaly detection? Historical trend analysis? Edge computing? Different tasks demand different tools, and modular architectures support this diversity. Are you constrained by a one-size-fits-all approach?
• What are your data movement costs? Moving data between incompatible systems can become a significant expense. Decoupled systems eliminate these inefficiencies by centralizing data in open formats.‍
• Can your system scale with complexity? Can your architecture scale storage independently from compute, or will growth force costly migrations and infrastructure rework? How easily can you onboard new hardware, integrate new telemetry sources, or scale AI workloads?

Building modern machines demands more than just incremental improvements—organizations can’t afford to wait for legacy database vendors to catch up.

The Path Forward: Data Control as a Competitive Advantage

Building modern machines demands more than just incremental improvements—organizations can’t afford to wait for legacy database vendors to catch up. The shift to modular, decoupled architectures is already underway—and the organizations that make the shift will gain:

• Real-time insights at scale—Eliminating delays in observability and anomaly detection.
• Operational efficiency—Scaling compute and storage independently to optimize costs.
• AI-ready infrastructure—Seamlessly integration of evolving ML and analytics frameworks.‍
• Control over mission-critical data—Ensuring security, compliance, and flexibility.

This isn’t about following trends—it’s about building an infrastructure that matches the scale, complexity, and speed of modern hardware systems. Legacy architectures are holding back progress. The companies that embrace decoupled architectures now will define the future of real-time observability, predictive maintenance, and AI-driven autonomy.