Generative AI does not require one universal data stack. If your application calls a hosted model API, you may need only an application runtime, access to the model, suitable data storage, and monitoring. Add retrieval-augmented generation (RAG) when the model must use your organization’s information at response time; add training infrastructure when you intend to fine-tune or train a model. The right design follows the workload, data risks, performance targets, and operational capacity—not the label “AI.”
Start with the model deployment path
The first infrastructure decision is what your application will do with the model. Calling a pretrained model through an API, grounding it in private or frequently changing information, and training or fine-tuning a model are different workloads. They call for different data flows and levels of compute responsibility.
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A hosted model API can keep model hosting and its associated compute outside your own environment. Your application still needs a place to run, a secure way to call the model, and a plan for the data it reads, produces, and logs. RAG adds a preparation and retrieval system. Training or fine-tuning adds managed datasets, model artifacts, evaluation, and lifecycle processes.
AWS Prescriptive Guidance describes a basic API-based application pattern using general-purpose application compute, object storage for application data and outputs, and monitoring and logging. Event-driven compute can be added where useful. This is an AWS reference pattern, not a mandatory stack: it does not mean every pilot needs a data lake, vector index, or GPU server.
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Three common paths at a glance
| Workload | Data and compute needs | What changes operationally |
|---|---|---|
| Hosted model API | Application runtime, model endpoint access, application data storage as needed, identity controls, and observability. | Model infrastructure is largely managed outside your environment; you remain responsible for the application and its data handling. |
| RAG over organizational data | Source connections, parsing and preparation, embeddings, a searchable index, query-time retrieval, and model serving. | You must keep content and permissions current, monitor ingestion and retrieval, and secure the path from source to response. |
| Training or fine-tuning | Prepared training data, storage for datasets and model artifacts, training and evaluation workflows, deployment, and lifecycle management. | You take on more of the model-development workflow; accelerated compute may be appropriate as the workload scales. |
These are choices rather than a maturity ladder. A team can start with an API and later add RAG if it needs answers grounded in internal sources. It need not move to custom training unless that addresses a real requirement.
What a basic API application needs
For an application that sends a prompt to a hosted model and returns the result, keep the initial architecture proportionate. The application runtime handles user requests, business logic, and API calls. Identity and permissions determine which users and services can access application data and the model endpoint. Storage holds only the data the application actually needs to retain, such as user-provided inputs or generated outputs, subject to your retention and privacy decisions.
Logging and monitoring make it possible to detect failures, latency changes, unusual usage, and application errors. They also introduce a data-governance question: prompts and outputs can contain sensitive information. Decide what to log, who may inspect it, and how long it is retained before enabling detailed request logging. Do not treat observability as permission to keep every prompt indefinitely.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAt this stage, a dedicated vector database or local GPU is not implied. Add persistent storage, event-driven processing, or specialized compute only when the application’s data volume, reliability needs, or response-time targets justify them. The model provider’s own endpoint availability, regional options, and data-handling terms must also fit the application, but those details vary by service and are not a single infrastructure specification.
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What changes when you add RAG
Retrieval-augmented generation supplies selected external or organizational information to a model when a request arrives. It is a data pipeline plus a serving-time retrieval path: source content must be made searchable, and the application must retrieve relevant passages and provide them as context for a model response.
Build the ingestion and update path
- Connect authoritative sources. Identify the repositories, databases, files, or other systems that contain the information the application is allowed to use. Define ownership and permissions at the source boundary.
- Parse and prepare content. Convert source material into usable text or structured records. Depending on the data, preparation may include deduplication, handling missing values, normalization, enrichment, document analysis, and retaining useful metadata.
- Divide content into retrievable units. Chunking breaks long documents into smaller pieces for indexing and retrieval. Choose boundaries that preserve meaning and context; the appropriate unit depends on the source and the kinds of questions users ask.
- Generate embeddings and index the material. An embedding represents content in a form that supports semantic similarity search. Store the embedding with the text or a reference to it, plus metadata needed for filtering, provenance, and access control.
- Update for changed sources. Establish how new, edited, and deleted content propagates through preparation and indexing. The update strategy should meet the freshness requirement and provide a way to detect and recover from failed jobs.
Run the serving path
At query time, the application receives a user request, applies the relevant identity and access checks, and searches for suitable content. It selects context, constructs an augmented prompt, calls the model, and returns the response. The application should preserve enough source information to support citations or other provenance features when those are part of the product’s requirements.
In its RAG reference design, Google Cloud separates ingestion from serving: ingestion prepares content and builds or updates an index, while serving retrieves relevant material and supplies it to the model. A separate Google Cloud design, reviewed on 2026-02-24, illustrates one self-managed arrangement using cloud object storage, event-driven embedding and preprocessing, PostgreSQL with pgvector, and a serving system on Kubernetes. These are examples of possible architectures, not minimum requirements.
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Ingestion and serving may have different scaling and reliability needs. A large backfill can consume substantial processing capacity while user queries still need predictable response times. Separating the flows can help teams monitor and control them independently, although the appropriate deployment boundary depends on the application.
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Do you need a vector database?
No—not automatically. Vector similarity search can find content that is semantically related to a query, but retrieval systems may also need full-text search, metadata filters, permissions, and other query types. Microsoft’s guidance for AI data platforms notes that some existing database services provide vector types and search. Evaluate those capabilities before introducing a separate system.
A dedicated vector store or search service becomes more compelling when the existing platform cannot meet requirements for retrieval behavior, scale, freshness, filtering, reliability, or index management. The decision is about fit, not whether RAG is present.
Compare retrieval options on workload evidence
| Question | Why it matters |
|---|---|
| Which query types are necessary? | Semantic similarity alone may not handle exact terms, structured filters, or combinations of vector and full-text search. |
| How quickly must changes appear? | Freshness requirements shape ingestion frequency, incremental updates, and the cost and complexity of rebuilding an index. |
| How much data and query traffic are expected? | Record count, query rate, concurrency, and throughput affect capacity and latency planning. |
| Can the index be recovered or rebuilt? | Index rebuild and recovery paths affect service continuity and the ability to correct bad or stale data. |
| What does the platform require to operate? | Managed services can reduce routine operational work; self-managed options can offer control but require staff and processes to maintain them. |
Chunking has a direct capacity consequence: one source document may become many indexed records. Estimate the likely record count after preparation, not just the number of source files. Account for storage and indexing as well as embedding and processing, model calls, service capacity, and operational effort. Costs vary by provider, region, service configuration, and date, so a generic price estimate would not be reliable.
Data preparation and storage choices
Plan around the data you have and how it changes. Inputs may be structured, semi-structured, unstructured, or streaming. A pipeline may need source connectors, transformation steps, orchestration, job monitoring, and capacity for the expected ingestion volume and frequency. Data quality problems—duplicates, missing values, inconsistent formats, or weak metadata—can degrade retrieval even when the model and index are functioning normally.
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Not every source needs to be copied into a new central repository. Data may remain in its system of record, be copied to shared object or lake storage, or be held in an existing database, depending on access, governance, and processing needs. Microsoft’s Azure-oriented data-platform guidance discusses distinct storage functions such as multi-format support, lifecycle management, versioning and lineage, governance and cataloging, and high-volume storage. Those functions can be mapped to an organization’s existing platform rather than copied as a vendor-specific stack.
For each dataset, determine whether the application needs a transformed copy, a searchable representation, or only a secure connection to the source. Keep enough metadata to identify the source and support updates, auditing, and access decisions. For multimodal information, the preparation path may differ from a text-only workflow; AWS’s data lifecycle guidance includes unstructured and multimodal data alongside preparation and governance considerations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When are GPUs or other accelerators needed?
Accelerated compute is not an automatic prerequisite for an API-based application. When a hosted model performs inference, the provider operates the model-serving infrastructure. Your application still needs compute to handle its own traffic and data flow, but that does not by itself require a GPU.
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AWS Prescriptive Guidance describes object storage and data integration for preparing training data, a managed machine-learning environment, and accelerated compute as an option as projects scale. Its cloud instance examples are vendor-specific; they are not universal physical-hardware recommendations. Do not select a GPU server merely because an application uses generative AI.
Security, governance, and reliability belong across the data flow
RAG can provide current information without placing source documents into model parameters, but it creates security obligations around ingestion, retrieval, prompts, outputs, and logs. Permissions must follow information into the index: a user should not retrieve content they are not authorized to see. Enforce access controls in the retrieval path, not only in the original repository.
- Identity and least privilege: Limit which users, services, and pipeline components can read sources, update indexes, call models, or inspect logs.
- Sensitive information: Classify data and decide whether fields should be masked, excluded, or otherwise protected before indexing or sending context to a model.
- Provenance and quality: Track where data came from, how it was prepared, and when it was updated so that questionable or stale results can be investigated.
- Privacy and retention: Review what prompts, retrieved passages, outputs, and feedback are stored, for what purpose, and for how long.
- Regional requirements: Confirm that data residency, model service availability, and processing locations satisfy applicable organizational and legal requirements.
- Auditability: Govern source access, ingestion changes, index updates, model access, and relevant logs.
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, 2024) recommends documenting data collection methods, provenance, quality, model information, and evaluation data, and examining privacy, security, and resilience risks. These practices support traceability and review; infrastructure alone cannot establish that an AI system is safe or appropriate.
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Operationally, monitor ingestion job status, index freshness, errors, latency, throughput, model behavior, and cost. Define alerts and recovery procedures for failed processing and stale or damaged indexes. Microsoft’s guidance calls out pipeline monitoring and alerts, availability and recovery, and index rebuilds; Google’s RAG references include request logging and monitoring. Choose metrics that indicate whether the service is working for users, not merely whether its infrastructure is running.
Choose the smallest architecture that meets the requirements
Before selecting services or building a platform, write down the workload constraints that change the design. A practical comparison should cover:
- Deployment ownership: Which components are managed, and which require your team to maintain, customize, or move between environments?
- Data integration: Are the required sources and formats supported? Can the pipeline transform and orchestrate them at the necessary volume and frequency?
- Retrieval: Are vector similarity, full-text search, filters, metadata, and freshness all supported as needed?
- Performance: What data volume, update rate, query rate, latency, throughput, and inference concurrency must the system handle?
- Security and governance: Can identity boundaries, sensitive-data controls, provenance, auditability, and regional requirements be met?
- Reliability: What happens when ingestion fails, the index is stale, a service is unavailable, or a rebuild is needed?
- Total operating cost: Consider storage, processing, embeddings, indexing, model calls, service capacity, and the staff effort needed to run the system.
Use those answers to avoid overbuilding. A proof of concept can begin with a hosted model API and modest application services. Add a RAG pipeline when the application needs information outside the model’s built-in knowledge or needs source-specific context at response time. Introduce dedicated search, more complex orchestration, or accelerators only when a measured requirement or a clear security and reliability need calls for them.
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