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Build a production-ready AI application by designing its data flows, security controls, delivery process, and failure behavior—not just choosing a model endpoint. Start with the workload and the data it may use; then select storage, retrieval, model access, and deployment patterns that meet its latency, governance, portability, and operating-cost needs. There is no single architecture that fits every AI workload.
This topic is also the focus of a Linux Foundation on-demand webinar recorded October 29, 2025, and presented by Yugabyte. Its stated subjects include open standards, data consolidation, RAG, multimodal databases, elastic scale, cost management, security, and reliability. Because it is a vendor-presented session, that framing is not independent evidence that one database or architecture is best.
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Start with the workload and the data
Before choosing components, define what the application must do and what information it is allowed to use. A RAG assistant, a model-backed service that summarizes events, and a batch prediction pipeline can have very different freshness, query, latency, and availability needs. “AI application” is not a useful enough workload description to make architecture decisions.
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- Data: Inventory source systems, formats, quality, ownership, sensitivity, and permitted uses. Separate data used for training or evaluation from data retrieved to provide context at inference time.
- Service expectations: Specify freshness, response time, throughput, availability, and acceptable degraded behavior. Clarify which paths need interactive responses and which can run asynchronously.
- Constraints: Record residency, retention, access, audit, and provider requirements, plus the skills and operational capacity of the team responsible for the system.
These answers become the criteria for comparing designs. They also expose the trade-offs that a diagram can hide: duplicated data, access rules that differ between systems, transfer costs, and responsibility for keeping derived data current.
#1 Best Overall
Choose how data moves between stores
Many AI systems use more than one kind of data store. A lake, warehouse, operational database, and purpose-built retrieval service may serve different access patterns, but every transfer introduces work: data must be selected, transformed, governed, refreshed, and traced back to its source. AWS architecture guidance groups common movement patterns into three broad shapes:
| Pattern | What moves | Design question |
|---|---|---|
| Inside-out | Data is aggregated into a lake, then subsets move to specialized stores. | Which derived copies need to exist, how quickly must they refresh, and how will their lineage and access policies be maintained? |
| Outside-in | Data from application stores moves into a lake for broader analysis. | Can the source data be shared for this purpose, and how will ingestion avoid creating stale or unauthorized copies? |
| Perimeter movement | Data moves between specialized stores rather than following one central route. | Which teams own each handoff, and how will they manage consistency, transfer costs, and end-to-end lineage? |
AWS’s Modern Data Architecture Accelerator is one AWS-specific reference implementation: it describes an S3-based lake with governance and DataOps capabilities around it, moving data to purpose-built analytics services. AWS presents the architecture as adaptable to data lake, lake house, data mesh, and generative AI environments; it is an example, not a universal blueprint.
For any design, document the source, transformation, destination, refresh expectation, owner, and allowed use for each important data flow. Include data transfer and duplication in cost estimates. If a retrieval index or feature store falls behind its source, the application can return outdated context even while its model endpoint is healthy.
Build the retrieval and model path deliberately
For retrieval-augmented generation (RAG), the model should receive relevant, permitted context rather than an unexamined dump of enterprise data. A useful design separates the preparation path from the request path:
- Ingest and normalize: Collect approved source material and convert it into representations the retrieval process can use. Preserve identifiers and provenance so retrieved material can be traced back to its source.
- Attach metadata and access rules: Record information such as ownership, timestamps, sensitivity, and applicable permissions. Decide how user identity and document-level access affect what retrieval may return.
- Prepare retrieval representations: Create and refresh the indexes or other representations required by the chosen retrieval method. Define how edits, deletions, and permission changes propagate.
- Retrieve for each request: Apply the relevant query and access constraints, and select context that fits the application’s quality and latency requirements.
- Call the model and return a response: Pass only the necessary context to the model, then apply the application’s response, logging, and review policies.
Multimodal workloads add more than a file-format question: images, audio, text, and other inputs can require different preparation, retrieval, and governance decisions. The Linux Foundation webinar page names multimodal databases and RAG as subjects, but does not establish a best implementation. Evaluate stores and retrieval methods against your actual data types, query patterns, consistency needs, latency, access controls, and portability requirements.
Keep the model-access layer distinct enough that a provider or model change does not automatically force changes throughout ingestion and retrieval. That boundary can reduce coupling, but it does not make a system portable by itself: APIs, prompts, model behavior, data formats, and operational tooling can still differ.
Make components modular for a reason
A production generative AI application may include ingestion and processing, retrieval, model access, orchestration, persistence, and a user-facing interface. AWS guidance describes decomposing these into independently deployable services, and also describes event-driven designs that separate interface events, orchestration, processing, service logic, and persistence.
Those patterns can support independent deployment, monitoring, scaling, and updates. They also add service boundaries to secure, observe, coordinate, and operate. Use modularity where separate ownership, distinct scaling needs, resilience, or the likelihood of changing a component justify that overhead. A small, stable workload may not benefit from splitting every function into a separate service.
Rank #3
Kubernetes can be part of the deployment platform for these services, but using Kubernetes does not settle data architecture, model access, governance, or delivery choices. Decide which components need container orchestration and which are better handled by other managed or existing platform capabilities. The right boundary depends on workload demands and the expertise available to operate it.
Make development and deployment repeatable
AI behavior can change when data transformations, source data, model versions, prompts, configuration, or evaluation methods change. Treat those inputs as a connected lifecycle so teams can review changes and reproduce important results as far as the system permits.
Google Cloud’s enterprise AI/ML blueprint distinguishes an interactive development environment from operational non-production and production environments. Its workflow combines exploration and experimentation with repeatable build-and-test pipelines and controlled deployment. The concepts apply beyond Google Cloud; the blueprint itself is an ecosystem-specific example.
- Track the inputs: Version code, transformations, model identifiers, prompts, configuration, and evaluation records. Record which data snapshots or sources were used where practicable.
- Test before promotion: Run repeatable checks on data quality, retrieval behavior, access controls, model behavior, and application-level outcomes. Define how a failed check blocks or rolls back a release.
- Separate environments: Keep experimentation distinct from operational non-production and production. Set appropriate rules for copying or accessing sensitive data in each environment.
- Automate controlled release: Store build artifacts and use a reviewable promotion process. Google’s blueprint identifies CI/CD automation, artifact storage, feature data, and model registry functions among possible lifecycle capabilities.
- Monitor outcomes after release: Relate operational measures to business results and establish owners and response procedures for changes in data, model behavior, or service health.
The goal is not to promise that every model result can be exactly reproduced. It is to make the material inputs and changes visible enough to investigate unexpected behavior, compare releases, and support review.
Protect data across the whole path
Data protection must follow information as it moves between services, not stop at the user interface or database. NIST IR 8505, finalized September 30, 2024, addresses protecting data in cloud-native, multi-cloud, service-mesh, and hybrid environments, including sensitive information in transit on both east-west (service-to-service) and north-south (external-to-service) paths.
Apply controls to ingestion, storage, retrieval, model calls, and logs. The specific controls depend on the data, jurisdictions, threat model, and organizational obligations, but the design should make these questions answerable:
- How is data classified, and which uses are allowed for each class?
- Which identities and services may read, transform, or pass data onward, and how is least privilege enforced?
- Where is data encrypted at rest and in transit, including internal service-to-service traffic?
- Which information may enter prompts, model-provider requests, traces, or application logs, and how are retention and access controlled?
- Can the organization audit access and transfers across system boundaries?
Google Cloud’s blueprint also names identity, networking, logging, monitoring, data access control, and infrastructure encryption at rest and in transit as platform capabilities. NIST discusses service meshes and WebAssembly in the context of its approach; their presence alone does not guarantee compliance or adequate protection.
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Plan for scale, reliability, observability, and cost together
Elasticity is useful only when the system can scale the stages that need it without creating unacceptable latency, failure behavior, or cost. AWS guidance frames cloud-native architecture decisions around cost, reliability, resiliency, and performance. Its serverless AI guidance describes loosely coupled, event-driven systems that can scale and recover independently, while emphasizing observability and cost/performance optimization. These are design options, not guarantees of lower cost or higher availability.
Best Value
For each stage—from ingestion and retrieval to model calls—decide what happens under load or failure:
- Burst handling: Identify which workloads are variable and whether queues or asynchronous processing can absorb bursts without violating freshness or response-time requirements.
- Dependency failure: Define behavior when a model, retrieval store, or upstream data source is slow or unavailable. Decide where retries are safe, how they are bounded, and when to return a degraded result or fail clearly.
- Request tracing: Connect logs and metrics across service boundaries so an operator can follow a request through retrieval, orchestration, and model access. Protect sensitive data in that telemetry.
- Capacity and limits: Check service limits, throughput requirements, and scaling behavior under expected demand. Do not assume that independently scalable components remove bottlenecks elsewhere.
- Cost visibility: Measure the costs of compute, storage, model use, data movement, and operational tooling against the workload. A design that duplicates data or invokes expensive services more often can change its economics as traffic grows.
Serverless and microservices can be appropriate when their operational model fits the workload and team. Outcomes depend on traffic shape, service limits, data movement, implementation, and operational maturity; no cited guidance establishes a universal savings or reliability advantage.
Compare candidate architectures on the same criteria
Once the workload is defined, use a consistent scorecard rather than comparing architectures by a single feature or vendor claim. Record evidence and unresolved questions for each candidate.
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|---|---|
| Data fit | Which formats, volumes, query patterns, freshness, and quality requirements must be supported? |
| Movement and lineage | How many copies and transfers are needed? What are the latency, ownership, lineage, and transfer-cost implications? |
| Portability | Which open standards, formats, and APIs are used, and what would changing cloud or model providers require? |
| Governance and security | Can identity, classification, residency, access, in-transit protection, and audit needs be enforced end to end? |
| Operations | Can the team automate deployment, monitor the request path, reproduce changes, recover from failures, and support the chosen components? |
| Scale and economics | How do variability, latency, throughput, service limits, model usage, and data movement affect total operating cost? |
| Team fit | Does the organization need a managed platform for broad access, or infrastructure-level control for an expert team? |
Use this comparison to expose trade-offs, not to produce a universal winner. AWS and Google Cloud guidance are official examples for their respective ecosystems, while the webinar’s stated themes identify concerns rather than proving a product advantage. The evidence available here does not establish benchmark results, universal cost savings, or one best architecture.
Turn the design into a reviewable plan
A useful architecture decision is specific enough for implementation and review. Capture the workload, data flows, chosen component boundaries, security controls, delivery lifecycle, and failure behavior in one design record. For each significant choice, note the alternatives considered, the reason for selection, and what change in workload or organizational constraint would trigger a reassessment.
That record keeps decisions grounded in the application’s actual data and operating needs. It also gives platform, data, security, and application teams a common basis for discussing portability, reliability, scale, and cost before those concerns become production incidents.
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