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Generative AI does not require one universal data stack. If your application calls a hosted model through an API, you may need only an application runtime, the data stores your product already uses, and suitable monitoring and access controls. Grounding answers in your organization’s information adds a data-preparation and retrieval pipeline. Training or fine-tuning a model adds prepared datasets, model-artifact management, and a training and evaluation environment—and may require accelerated compute.
The practical starting point is the deployment path, not the label “AI.” Add infrastructure to meet concrete requirements for data access, freshness, performance, governance, and recovery. A vector database, GPU cluster, or lakehouse is not automatically part of the minimum setup.
Start by identifying how the model will be used
Three different workloads often get grouped under “generative AI,” but they place different demands on infrastructure:
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|---|---|---|
| Hosted-model API application | Your application sends requests to a pretrained model managed by a provider. | Application compute, its own data handling and storage, identity and permissions, and logs and metrics. Model training infrastructure is generally outside your environment. |
| Application grounded in organizational data (RAG) | The application retrieves relevant source material at response time and supplies it to the model as context. | Source connections, preparation and chunking, embeddings, a searchable index, retrieval and update paths, and security controls that preserve source permissions. |
| Training or fine-tuning | You train a model or adapt one using prepared data. | Training-data preparation and storage, model artifacts, training and evaluation workflows, deployment and lifecycle management; accelerated compute may be appropriate. |
AWS Prescriptive Guidance describes a basic API application pattern using general-purpose application compute, object storage for application data and outputs, and monitoring and logging. It presents event-driven compute as optional. This is an AWS reference pattern, not a requirement for every cloud or application; map the functions to the systems you already operate.
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For a first API-based pilot, the key question is whether the application can safely reach the model endpoint and the data it needs. Do not provision a training cluster merely because the product uses a generative model. Nor does an API pilot need a vector index unless it has a retrieval use case.
What changes when you add RAG?
Retrieval-augmented generation, or RAG, connects a model to information that lives outside its parameters. The system finds relevant material for a particular request, then includes selected passages in the model call. This can make answers more specific to an organization’s content and allow information to be updated without retraining the model. It does not by itself guarantee that an answer is correct: retrieval quality, source quality, permissions, and the model’s handling of context all matter.
The ingestion path: make source material searchable
RAG is a data pipeline as well as a query-time feature. A typical ingestion flow has these stages:
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- Connect to authoritative sources. Identify the systems that contain the information the application is allowed to use, such as approved documents or databases. Decide how each source is accessed and how changes are detected.
- Parse and normalize content. Extract usable text and metadata from the source formats. Depending on the data, preparation can involve deduplication, handling missing values, normalization, enrichment, or document analysis.
- Divide content into retrieval units. Chunking breaks documents into sections small enough to retrieve usefully while retaining necessary context. Chunk size and boundaries affect both what can be found and how much material is indexed.
- Create embeddings and index records. Embeddings represent content for semantic search. The index should retain useful metadata—such as source identity and access attributes—rather than treating each chunk as anonymous text.
- Update, monitor, and rebuild. Establish how additions, edits, and removals reach the index, how failures are detected, and how the index can be restored or rebuilt.
Google Cloud’s RAG reference architecture separates ingestion from serving and describes parsing, chunking, embeddings, indexing, retrieval, and monitoring. Its more self-managed GKE and Cloud SQL example uses object storage, an event-driven embedding subsystem, preprocessing, PostgreSQL with pgvector, and a Kubernetes-based serving subsystem. These are examples of possible designs, not minimum components every RAG application must reproduce. The documents list review dates of March 7, 2025, for the Vector Search architecture and February 24, 2026, for the GKE and Cloud SQL architecture.
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The serving path: retrieve only what the request needs
At response time, the application turns a user’s question into a retrieval request, searches the index, selects relevant results, and adds those results to the model prompt. It then calls the model and returns the response. This path needs to meet the application’s latency and availability needs. It also needs a defined behavior when retrieval fails, returns no useful material, or produces results the user is not authorized to see.
Ingestion and serving have different operational patterns: ingestion processes source changes and can often run asynchronously, while serving is on the user-facing response path. Keeping those responsibilities distinct can make it easier to monitor a delayed index update without confusing it with a model or application outage. Google’s private-connectivity reference illustrates separate ingestion and serving projects, IAM permissions between components, and private network paths. Its review date is December 12, 2025; the topology is an enterprise example, not a universal blueprint.
Do you need a vector database?
Not necessarily. Vector similarity search can find semantically related passages, but RAG may also need full-text search, metadata filters, or other query types. Microsoft’s guidance for AI data platforms notes that some existing database services support vector types and search. Start with the capabilities and operational limits of your current database or search platform before adding a separate service.
| Option | When it may fit | Questions to check |
|---|---|---|
| Existing database or search service with vector capability | The current platform meets the required search, filtering, scale, and operational needs. | Does it support the needed vector and full-text queries, metadata filters, update frequency, reliability, and index-rebuild process? |
| Separate or specialized vector index | Requirements exceed what the existing platform handles well, or a separate service better fits the retrieval workload. | How will it integrate with source permissions and ingestion? How are updates, recovery, monitoring, capacity, and ongoing operations handled? |
Compare real workloads rather than product labels. Measure the data volume, query rate, update frequency, latency target, and expected inference concurrency. Microsoft cautions that chunking can greatly increase the number of indexed records; query rate, throughput, feature use, scaling limits, and overprovisioning can all affect cost. Confirm that a proposed design can update incrementally where needed and recover from an index failure without losing track of source data.
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Choose storage and pipeline services by function
AI workloads can use structured, semi-structured, unstructured, or streaming data. The right platform depends on what the application must ingest and how often it changes—not on a requirement to move everything into a new central store. Some data can remain in its authoritative system; other workloads may benefit from a shared object or lake store, a database, or a combination.
When evaluating a data platform, consider these distinct capabilities:
- Connections and orchestration: Can it connect to the sources and formats you need, schedule or trigger work, and make dependencies visible?
- Preparation: Can it perform the cleanup, transformation, enrichment, document parsing, and chunking the workload requires?
- Storage and traceability: Does it provide suitable capacity and retention, versioning or lineage, lifecycle management, and cataloging or governance?
- Pipeline operations: Can teams monitor jobs, detect failures, and handle the expected volume and frequency of incoming data?
- Retrieval fit: Can the chosen index serve the necessary search, filters, freshness, and rebuild requirements?
Microsoft’s “Data Platform for AI Workloads on Azure” discusses these functions in an Azure-oriented context. Treat its service examples as illustrations of capabilities, not as a stack that must be copied. Apply the same functional checklist to the platform and tools your organization already uses.
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When do you need GPUs or other accelerated compute?
Accelerated compute is not an automatic prerequisite for an application that calls a hosted model API. In that deployment path, the provider runs the model; your own application still needs compute for its runtime and any data processing it performs.
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Training, fine-tuning, self-hosted model inference, or a workload that must meet particular performance and scale requirements can change the compute decision. AWS’s training 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 named cloud instances are vendor examples, not general-purpose hardware specifications. The available guidance does not establish a universal GPU requirement or a current, general performance or savings benchmark; determine compute needs from the model, workload, deployment design, and measured requirements.
Customization also adds a data and lifecycle responsibility that an API pilot may not have: prepare and track training data, manage model artifacts, evaluate the customized model, and operate deployment and subsequent lifecycle workflows. Training data and evaluation data should have clear provenance and quality controls.
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Security is not a layer to add only after the retrieval design is complete. A RAG system can expose information through retrieval even when that information is not directly sent as a training example. The application needs controls over source access, ingestion, indexing, retrieval, model access, and the records produced by operation.
- Preserve authorization: Identify which users and services may read each source, and ensure retrieval does not return content a requester cannot access. Permissions and relevant metadata need to remain usable throughout the ingestion and serving paths.
- Handle sensitive data deliberately: Classify sensitive fields and decide whether masking or other protections are appropriate before content is embedded, indexed, placed in prompts, or written to logs.
- Maintain provenance and quality records: Track where data came from, how it was collected and transformed, and what quality checks apply. Keep model and evaluation information traceable as well.
- Review privacy, regional, and network constraints: Confirm that data handling and service availability satisfy applicable privacy, residency, and regional requirements. Use network segmentation and least-privilege identity where the risk and architecture warrant them.
- Govern logs and lifecycle: Decide who can access prompts, responses, conversation records, and operational logs, how long they are retained, and how source changes or deletions are reflected in downstream stores.
The NIST AI 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 risks and security and resilience. These practices support review and traceability; infrastructure alone does not resolve AI risk. AWS Prescriptive Guidance likewise describes security challenges introduced by RAG and the need to control knowledge sources and data paths.
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Plan operations, reliability, and cost before launch
Infrastructure is useful only if the team can tell whether its data and serving paths are working. Set operational ownership and monitoring for pipeline status, index freshness, request logs and metrics, model access, inference performance, and cost. Define alerts for failures that matter to users—for example, a source not being ingested or an index falling behind its freshness target.
Plan recovery as well as normal operation. Consider how to retry failed ingestion, restore or rebuild an index, make updates safely, and handle unavailable model or retrieval dependencies. Microsoft’s platform guidance calls out monitoring and alerts for processing jobs, along with reliability features such as availability, recovery, and index rebuilds. Google’s RAG references include request logging and monitoring.
Cost is spread across the workflow, not just the model call. Estimate storage, data processing, embedding generation, indexing, model usage, and the capacity needed for search and serving. Include operational effort when comparing managed services with self-managed components. No single price or capacity figure applies across regions, providers, data volumes, and usage patterns; check current terms for the actual deployment.
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A practical decision sequence
- Choose the deployment path. Decide whether the application calls a hosted model API, needs retrieval over organizational sources, or requires training or fine-tuning.
- Define workload requirements. Record source formats and volume, update frequency, query rate, freshness, latency, availability, and access boundaries.
- Map existing capabilities. Identify which current systems can provide application storage, source connections, preparation, search, identity, monitoring, and recovery.
- Add only the missing functions. For RAG, test whether existing search or database services meet retrieval requirements before selecting a separate index. Add accelerated compute only when the chosen training or inference design calls for it.
- Validate the end-to-end path. Test source changes through ingestion and retrieval, permissions for different users, failure and recovery behavior, and performance under expected use.
- Set ownership and review. Assign responsibility for pipeline health, index updates, access review, logs, model evaluation, and cost. Revisit the design when workload requirements change.
AWS Prescriptive Guidance summarizes the foundation for model customization this way: “For developing generative AI applications, particularly if training or fine-tuning a foundation model is necessary, a robust data foundation and compute infrastructure is critical.” That qualification matters: the infrastructure should be robust enough for the workload, but the workload determines what “robust” requires.
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