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Design a production-ready AI application around its data flows and operational needs—not around a model endpoint alone. Start with the data the system may use, how fresh it must be, who can access it, and what latency and reliability the workload requires. Then choose data stores, retrieval, model access, orchestration, and deployment patterns that meet those requirements without adding complexity the team cannot operate.
What makes an AI application data-centric and cloud-native?
A cloud-native AI application is a set of connected services and data flows. It may include source systems, ingestion and transformation, storage, retrieval, model access, orchestration, and a user-facing interface. The model is one component; data quality, permissions, service boundaries, and failure handling also shape the result.
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“Data-centric” means treating the data needed for training, validation, retrieval, or inference context as a core design concern. “Cloud-native” describes an approach to building and operating the system using capabilities such as automated deployment, independently managed services, and elastic infrastructure. Neither term requires a specific database, cloud, or Kubernetes deployment.
A Linux Foundation webinar recorded on October 29, 2025, presented by Yugabyte, frames the subject around open standards and portability, data consolidation, scale and cost, compliance, security, reliability, RAG, multimodal databases, and the CNCF ecosystem. That vendor-presented session identifies relevant design questions, but does not establish a universally best product or architecture.
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Start with the workload and its data
Before choosing components, define what the application must do and what data it is allowed to use. These decisions determine whether a system needs a low-latency transactional store, analytical storage, retrieval over documents, or several stores connected by governed data flows.
- Use case: Is the system supporting inference, retrieval-augmented generation (RAG), training, validation, or a combination?
- Data sources and formats: Which databases, files, events, and media types are involved? Who owns each source?
- Freshness: How quickly must a change in a source become available to the application?
- Access and permitted use: Which users, services, and models may access which data, and under what policy?
- Query and performance needs: What requests must the application serve, at what latency and throughput?
- Operating constraints: What are the requirements for recovery, data residency, auditability, and cost?
Agree on these requirements across data, application, platform, security, and business teams. A technically workable pipeline can still fail as a product if its data is stale, its access rules are unclear, or its service levels do not match how people use it.
Choose how data moves between stores
Modern AI systems often combine data lakes, warehouses, application databases, and purpose-built stores. AWS describes three broad movement patterns. They are useful ways to reason about architecture, not mutually exclusive designs or prescriptions for every workload.
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| Pattern | How data moves | What to consider |
|---|---|---|
| Inside-out | Aggregate data into a lake, then move subsets to specialized stores. | Decide which data needs a specialized store, how subsets are refreshed, and how lineage and access controls follow the copies. |
| Outside-in | Move data from application stores into a lake for broader analysis. | Account for the impact of extraction on source systems, freshness requirements, and the ownership of downstream copies. |
| Perimeter movement | Move data between specialized stores around the wider data environment. | Track the added integration paths, transfer costs, permissions, and consistency expectations across stores. |
For every path, establish who owns the source and destination, what transformations occur, how freshness is measured, and how users can trace a result back to its inputs. Data duplication may be justified for performance or workload fit, but it creates additional governance and synchronization work.
AWS’s Modern Data Architecture Accelerator describes one implementation: an S3-based lake surrounded by unified governance and DataOps capabilities that move data to purpose-built analytics services. AWS presents it as adaptable to data lake, lake house, data mesh, and generative AI environments. Treat it as an AWS reference implementation rather than a platform-neutral blueprint.
Build the application as a data path, including RAG
For a RAG application, the central question is not simply which database to use. It is how authorized, relevant, current context gets from source data to a model request—and how the application can explain or control that path.
- Ingest and normalize: Bring in permitted source content and transform it into representations suitable for retrieval.
- Preserve metadata and access: Retain source identity, ownership, timestamps, and the access information needed to enforce policy.
- Prepare retrieval representations: Organize the content and any required indexes or other retrieval structures for the expected query patterns.
- Retrieve for a request: Identify relevant context while applying the applicable access rules and freshness expectations.
- Pass context to the model: Send only the information needed for the request through an approved model-access path.
- Return and observe the result: Present the response through the application interface and retain appropriate operational records without exposing sensitive data unnecessarily.
Compare retrieval and database options using the same criteria: supported data types, query patterns, consistency needs, latency, governance, and portability. A multimodal workload may need to handle more than text, but the webinar’s mention of multimodal databases does not establish one implementation as best.
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Generative AI systems can separate ingestion and processing, retrieval, model access, orchestration, and user-facing functions into independently deployable services. AWS recommends decomposition for production applications; its event-driven guidance also separates interface events, orchestration, processing, service logic, and persistence.
Separate components where independent ownership, scaling, monitoring, resilience, or the ability to change a model or provider matters. A service boundary can let a team update one part without coordinating a release across the entire application. It also adds interfaces, deployment and monitoring work, and more failure paths to coordinate.
Keep components together when the separation does not support a real workload or team need. Microservices and event-driven designs are options, not guarantees of lower cost or greater reliability. Their outcomes depend on traffic, service limits, data movement, implementation, and the team’s operational capability.
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Make delivery reproducible and auditable
Separate experimentation from operational deployment. Google Cloud’s enterprise AI/ML blueprint distinguishes an interactive development environment from non-production and production environments, with a workflow for exploration, repeatable build-and-test pipelines, and controlled deployment. Its named products are examples of capabilities; the lifecycle principles apply beyond that ecosystem.
Track the parts needed to understand and reproduce a release: data transformations, application code, model versions, configuration, and evaluation records. Use automated build, test, and deployment steps where appropriate, with controlled promotion between environments. Artifact storage, model registry functions, access controls, and monitoring can support that process.
Evaluate the system against its intended business outcome as well as model-level measures. AWS guidance cautions against treating hard machine-learning metrics as the whole definition of success. A model metric can help diagnose behavior, but it does not by itself show whether the application is useful, safe to operate, or meeting its service objectives.
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Security and governance apply wherever data is stored, transformed, retrieved, sent to a model, or recorded in logs. In a distributed system, internal service-to-service traffic matters alongside traffic crossing the application boundary.
NIST IR 8505, finalized September 30, 2024, addresses protection of sensitive information in cloud-native, multi-cloud, service-mesh, and hybrid environments, including east-west and north-south data flows. It explores service meshes and WebAssembly as part of an approach; neither technology alone guarantees compliance.
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- Classify data: Identify sensitive information and define which uses are allowed.
- Limit access: Apply least-privilege identity and authorization to services, people, and data.
- Protect transfers and storage: Apply encryption in transit and at rest where required by policy and risk.
- Control model access: Govern what information may be sent to which model endpoint and under what conditions.
- Monitor and audit: Record access and relevant system activity in ways that support investigation without creating unnecessary sensitive-data copies.
Google Cloud’s blueprint likewise names identity, networking, encryption at rest and in transit, logging, monitoring, and data access control as platform capabilities. The right control set depends on the organization’s data, jurisdictions, threat model, and obligations; a platform diagram is not a substitute for that assessment.
Plan for scale, resilience, observability, and cost together
Elastic scale is not automatically economical, and a distributed design is not automatically resilient. AWS guidance treats cost, reliability, resiliency, and performance as connected architecture considerations. Its serverless AI guidance describes loosely coupled, event-driven systems that can scale and recover independently, while emphasizing observability and cost/performance optimization.
For each workload stage, ask:
- Which stages see bursty demand, and which need steady low-latency capacity?
- Where can a queue or retry absorb temporary overload, and where would it make a response too slow?
- What happens when retrieval, a model endpoint, or a downstream service is unavailable?
- Can teams trace a request across service boundaries and distinguish application, data, and model failures?
- How will teams measure resource use and the cost of data movement as demand changes?
Set the expected behavior for timeouts, retries, and partial failures rather than assuming dependencies will always respond. Observability should help operators connect an application request to its relevant service activity, while limiting sensitive information in telemetry.
Compare alternatives on the same criteria
Use one evaluation framework for platforms, data stores, deployment patterns, and model-access options. That keeps a comparison grounded in the application rather than in a vendor’s preferred feature list.
- Data fit: Supported formats, quality requirements, query patterns, freshness, and volume.
- Data movement: Latency, duplication, lineage, transfer or egress costs, and ownership across stores.
- Portability: Open standards, API and format compatibility, and the effort required to change cloud or model providers.
- Governance and security: Identity, classification, residency, in-transit controls, auditability, and access policy.
- Operations: Deployment automation, observability, reproducibility, recovery, required skills, and support responsibilities.
- Scale and economics: Demand variability, latency, throughput, service limits, and total operating cost.
- Team fit: Whether teams need a managed platform for broad access or infrastructure-level control for specialist workloads.
Open standards can reduce some switching friction, but portability is not a yes-or-no property. Assess which APIs, data formats, and operational practices are portable, and what work a change of provider would still require. No single design is established as best for all workloads; the useful choice is the one that meets the requirements the team can state and operate.
Quick Recap
Use a decision sequence before implementation
- Define the use case and its success criteria. Identify the user, the task, and business outcomes alongside model measures.
- Map data and permissions. Record sources, owners, allowed uses, sensitivity, freshness, and expected data movement.
- Set operating requirements. Specify latency, throughput, availability, recovery, audit, and cost constraints that matter for the workload.
- Choose data and retrieval patterns. Select stores and movement paths based on query and governance needs, not because a technology is fashionable.
- Set service boundaries. Separate components where independent change, scaling, or ownership justifies the coordination overhead.
- Design delivery and controls. Plan environment separation, repeatable pipelines, access controls, monitoring, and auditability before production promotion.
- Validate failure and change scenarios. Check how the system behaves when inputs are stale, a dependency fails, demand spikes, or a model or provider changes.
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