When an AI company serves customers across multiple continents, every millisecond matters. Enquire AI, a digital expert network that powers AI inference by integrating artificial and human intelligence, discovered this reality as they scaled their knowledge platform globally. Their platform leverages advanced AI to help clients access diverse insights for decision-making, but their international customer base created a perfect storm of operational challenges.
The company faced three simultaneous demands: ultra-low latency for real-time AI inference, high availability to ensure continuous service, and efficient data distribution for international customers. Traditional database architectures forced them to choose between these requirements, but Enquire AI needed all three to deliver their AI-powered insights effectively across continents.
The Traditional Approach Falls Short
Modern AI applications demand more than traditional databases can deliver. Your machine learning models need instant access to training data, your vector embeddings must stay current across continents, and your inference engines require real-time access to local datasets—all while maintaining consistent performance regardless of user location.
Most enterprises tackle this challenge with the same playbook: deploy regional database replicas, implement load balancing, and hope for the best. But this approach creates as many problems as it solves.
Traditional primary-replica database architectures might look good on paper, but they create operational nightmares in practice. Write operations must still flow to a single primary node, often located thousands of miles from where users need the data. Read replicas provide local access to data, but they’re always behind the primary, creating consistency issues that compound in AI workloads.
When you add AI processing to this mix, the problems multiply. Vector embeddings generated from user data might need to be processed by AI services in distant regions, creating massive latency penalties. Real-time inference requires immediate access to the most current data, but replica lag means your AI models are making decisions based on outdated information.
The result is a system that’s neither fast enough nor reliable enough—the worst of both worlds.
The Multi-Master Revolution
The breakthrough comes from fundamentally rethinking database architecture. Instead of accepting the limitations of primary-replica systems, companies like Enquire AI are embracing multi-master active-active architectures that eliminate single points of failure and minimize latency.
In a multi-master setup, every database node can handle both reads and writes, with changes automatically replicated across the network. This isn’t just a technical improvement—it’s a complete paradigm shift that puts data processing closer to users while maintaining global consistency.
After evaluating AWS Aurora, Enquire AI made the strategic decision to transition from AWS RDS to pgEdge Cloud’s distributed PostgreSQL solution. They deployed a two-node cluster with nodes in US East and Mumbai regions, ensuring that data processing could happen locally while maintaining global consistency.
The results were immediate and dramatic: significant reduction in data latency, improved response times for international customers, enhanced platform availability through geographic distribution, and elimination of single points of failure.
Beyond Uptime: The Performance Advantage
What makes this approach particularly powerful is how it solves multiple problems simultaneously. Geographic distribution stops being a constraint on performance and starts becoming a performance enabler.
When data lives locally, AI operations can happen locally too. Vector generation, similarity searches, and inference operations all occur close to users, eliminating the need for long-distance data transfers that create performance bottlenecks.
“pgEdge Distributed Postgres combined with the pgvector extension is a powerful combination that puts inference and similarity search requests closer to the users, giving them faster search results regardless of location,” said Cemil Kor, Head of Product at Enquire AI.
This proximity advantage becomes particularly valuable for AI workloads that require constant updates to embeddings. In traditional architectures, new data in one region might need to be sent to a centralized AI service thousands of miles away for vectorization, then stored back locally. With distributed AI compute, the entire process happens locally, dramatically reducing latency.
The Economics of Distributed Performance
The financial benefits extend beyond improved user experience. Centralized AI architectures generate massive bandwidth costs as data shuttles between regions for processing. These costs scale linearly with AI usage, creating an expensive bottleneck as organizations expand their AI capabilities.
Distributed architectures flip this economic model. Instead of paying for data movement, organizations invest in local compute capacity that serves their regional user base indefinitely. The initial infrastructure investment pays dividends through reduced bandwidth costs and improved user experience that drives retention and growth.
For organizations operating globally, this can be the difference between viable AI operations and abandoning AI capabilities entirely in distant markets due to poor performance.
Implementation Without Disruption
The transition to distributed AI doesn’t require rebuilding existing systems from scratch. pgEdge’s approach maintains 100% PostgreSQL compatibility, meaning existing applications can migrate without code changes while gaining distributed capabilities.
This compatibility extends to the AI stack as well. The 100% open source PostgreSQL extension pgvector provides efficient storage and querying of vector embeddings directly within the distributed database, enabling seamless integration with existing machine learning workflows.
Organizations can start with their most latency-sensitive regions and gradually expand the distributed architecture to other markets. Each new region becomes self-sufficient while maintaining data consistency with the broader network.
The Integration Challenge Solved
One of the biggest hurdles in distributed AI implementation is maintaining consistency across AI models and embeddings. Traditional approaches require complex synchronization mechanisms that introduce their own failure points and performance penalties.
pgEdge’s approach using multi-master (active-active) replication solves this by ensuring that updates to AI models or vector embeddings propagate across the entire cluster in near real-time. When a model is updated in one region, all other regions receive the update automatically, ensuring consistent AI behavior across the global platform.
This automatic synchronization means organizations can deploy global AI capabilities without sacrificing local performance or creating operational complexity for their teams.
Beyond Today’s Requirements
Performance requirements continue to evolve, with users expecting faster responses and higher availability from AI applications. Organizations that have solved the distributed AI puzzle are positioning themselves to meet these escalating demands rather than struggling with centralized bottlenecks.
As AI capabilities become more sophisticated and requirements regarding low latency and high availability become more stringent, the advantages of distributed architectures will only increase. Companies that make this transition now are building competitive moats that will be difficult for centralized competitors to cross.
The Strategic Imperative
Performance challenges surrounding AI applications aren’t going away—they’re becoming more complex and more demanding. Organizations that treat latency and availability requirements as constraints to be minimized will find themselves at an increasing disadvantage to competitors who’ve learned to leverage them as a source of architectural strength.
The companies leading this transition aren’t just solving today’s problems—they’re building infrastructure that turns geographic complexity into a competitive advantage. When distributed deployment becomes a performance enabler rather than a performance inhibitor, scalability stops being a cost center and starts being a revenue driver.
For global enterprises, the question isn’t whether to adopt distributed AI architectures—it’s whether to lead the transition or follow it. The organizations that figure this out first won’t just have better performance; they’ll have fundamentally superior AI capabilities that scale with user demands rather than despite them.
The future belongs to companies that can deliver AI capabilities that are simultaneously global and local, consistent and fast, reliable and responsive. Distributed AI architecture isn’t just a solution to today’s latency challenges—it’s the foundation for tomorrow’s AI-powered enterprises.
Phillip Merrick Bio:
Phillip is a technologist, entrepreneur and CEO with over three decades of experience in the enterprise software and Internet industries. Companies he has co-founded and/or run include webMethods, EnterpriseDB (EDB), SparkPost and Fugue, all of whom achieved successful nine or ten figure exits. At webMethods Phillip co-invented web services and web APIs; under his leadership the company had a highly successful IPO and became the fastest growing US software company. He is currently the CEO and co-founder of pgEdge, a distributed Postgres database startup launched in 2023.
