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A RAG system has two connected flows: an ingestion path that prepares and indexes content when it is added or changed, and a serving path that runs again for each user request. Each arrow carries data and triggers work—such as parsing, embedding, search, or generation—with its own cadence, cost drivers, and latency. The diagram below shows a common pattern, not a mandatory product blueprint.
The two flows in a RAG architecture diagram
Retrieval-augmented generation (RAG) gives a language model relevant material from a separate collection of documents or records. The architecture is easiest to understand when content preparation is separated from answering questions: ingestion creates searchable material; serving finds relevant material and uses it to generate a response.
Ingestion and indexing
Source systems → connector or landing zone → parse, clean, and chunk → document embedding model → vector index or store
This path runs when content is first loaded and again when it changes. A large initial backfill can create a short-lived burst of processing and embedding work; ongoing cost depends on how much content is added or updated.
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Serving a request
User → UI or API → orchestrator → query embedding → retrieval → optional hybrid merge or rerank → prompt assembly → LLM inference → optional safety checks → answer with supporting sources
Serving repeats for each request. Monitoring and evaluation sit alongside this path: logs and selected outputs can be used to investigate operations and assess answer quality. Not every box has to be a separate service. A single application can perform several roles, and some managed platforms combine steps internally.
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What each ingestion arrow carries and costs
These are cost categories and latency considerations, not prices. Actual billing depends on the chosen provider and configuration.
| Arrow | Payload and operation | Cadence | Likely cost and latency dimensions |
|---|---|---|---|
| Source → connector or landing zone | Files, records, or change events are collected from source systems and delivered to a place the pipeline can access. | Initial load, then on a schedule or when changes occur. | Connector development and operation, source licensing where applicable, data transfer, and landing storage. Large backfills can create bursts of transfer and storage activity. Supported formats and connectors vary by source. |
| Landing zone → parser and chunker | Raw content is fetched, extracted, cleaned, normalized, and split into retrievable chunks. Scanned files may require OCR or layout extraction. | For each new or changed item that needs processing. | Processing time or compute, OCR and extraction, retries, and temporary storage. Larger or scanned documents can take more work. Parsing time and queues can delay newly added content from becoming searchable. |
| Chunks → document embedding model | Each chunk is converted into a vector representation. | On initial indexing and for chunks created or changed by later updates. | Embedding inference or compute, plus the time to process the chunks. Chunk size and overlap affect how many vectors are produced and therefore the work. Google Cloud’s reference design uses the same embedding model and parameters for indexed content and runtime queries. |
| Vectors and associated text or metadata → index or store | Vectors and the text or metadata needed for retrieval are written, indexed, and kept available for search. | On initial indexing and subsequent writes; storage and serving capacity continue while the data is retained and available. | Index build and update work, storage, and search-serving capacity. The operational and billing model depends on whether this is a managed vector service or a database with vector support. Indexing and capacity can affect when new content is available and how quickly queries return. |
What each serving arrow carries and costs
| Arrow | Payload and operation | Cadence | Likely cost and latency dimensions |
|---|---|---|---|
| User → UI, API, or orchestrator | The natural-language request enters the application, often with conversation context, identity, and request settings. | Every user request. | Application compute, authentication, networking, request logging, and any session or state storage. This is often smaller than model inference, but it still needs to be measured. The division of work between frontend, backend, and orchestrator varies. |
| Query → query embedding | The request is encoded as a vector suitable for retrieval against the indexed content. | Usually once per request, unless the application reuses or transforms a query. | Embedding inference or compute and a network or API hop. Because retrieval typically waits for this vector, its response time contributes to the serial request path. Google’s reference design calls for matching the model and parameters used to index the content. |
| Query vector → retriever or index | The system searches for candidate chunks using similarity, filters, or a combination of retrieval methods. | Every request that needs retrieved context. | Search requests, index or database serving capacity, filtering, and transfer of results. Returning more candidates can improve the chance of finding useful evidence, but increases the data and work passed to later stages. |
| Candidates → optional hybrid merge or reranker | Lexical and vector result lists may be combined; a reranker may score candidates against the query and reorder them. | Only when the retrieval configuration uses these steps. | Additional search or model compute and result handling. A reranker adds a serial stage: Microsoft Learn’s Azure Architecture Center says it adds more latency than standard, vector, or hybrid search. Its relevance benefit should be weighed against added cost and response time on representative queries. |
| Retrieved context → prompt assembly | The application selects and formats retrieved evidence alongside the user’s question and instructions. | Every generated answer using RAG context. | Orchestration compute and, importantly, the input tokens sent to the generator. More or redundant context increases input work and can distract the model. Retrieval settings and context size should therefore be evaluated together. |
| Prompt → generator or LLM | The model receives instructions, the question, and selected context, then generates answer tokens. | Every generated response. | Input and output inference or compute, model-serving capacity, and time to first token and completion time. Context length affects input work; answer length affects output work. There is no portable per-arrow price without a specified provider, model, region, capacity mode, and workload. |
| Model → optional safety or response processing → user | Generated output may be screened, formatted with citations or supporting sources, and returned to the client. | For each response, if the design includes these steps. | Safety-service calls or compute, citation formatting, and response transport. Safety may be a distinct hop or part of a model platform; Google’s managed reference architecture includes configured safety filters. |
| Requests and answers → logs, metrics, and evaluation | Operational events and selected prompts or responses feed monitoring, troubleshooting, and quality assessment. | Logging may run continuously; evaluation can be scheduled or triggered by a change. | Log volume, retention, analytics, evaluation compute or model calls, and data governance. Logging and evaluation are operational work even when a simple architecture drawing leaves them out. |
Optional boxes belong only where they solve a measured problem
- Query rewrite, augmentation, or decomposition: A system can rewrite an unclear request, add useful terms, or split a multi-part question into smaller searches. These steps may improve retrieval for particular query types, but add compute and can add a serial model call. Microsoft’s Azure retrieval guidance discusses rewriting, augmentation, decomposition, and HyDE; evaluate them on the requests they are intended to help.
- Hybrid search: Combining lexical and vector retrieval can help when exact names, identifiers, or terms matter alongside semantic similarity. The resulting rankings may need to be merged before context is selected.
- Reranking: This can reorder a broader set of retrieved candidates before prompt assembly. It may help with mixed content or when relevance is especially important, but it is not a free default; benchmark its quality and latency effect.
- Graph traversal: Add graph retrieval when entity relationships and multi-hop connections are central to the questions. It is an advanced retrieval choice, not a required RAG box.
- Evaluation loop: Draw an offline feedback arrow from logs or test sets back to chunking, retrieval, and prompt or model configuration. Google’s AlloyDB reference design describes a separate quality-evaluation subsystem that scores factual accuracy and relevance.
Different products can fill the same boxes
A diagram describes responsibilities, not a required vendor stack. Google’s published architectures illustrate several ways to arrange them:
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| Implementation pattern | What it demonstrates | Trade-off to assess |
|---|---|---|
| Managed vector search and hosted models | Google Cloud’s managed reference architecture connects uploaded content and processing services to managed embeddings and Vector Search, then uses a managed serving flow. | Less infrastructure to operate directly, with choices shaped by service configuration, capacity, and region. |
GKE services and PostgreSQL with pgvector |
Google Cloud’s GKE reference design places frontend, embedding, and inference services in GKE and stores vectors in PostgreSQL using pgvector. |
More control over infrastructure and the option to use open models, alongside responsibility for operating and scaling those components. |
| Managed database with vector support | Google Cloud’s AlloyDB reference design uses a PostgreSQL-compatible vector store and separates ingestion, serving, and quality evaluation. | Shows that vector retrieval can be provided by a database rather than a standalone vector-search product; fit depends on data, scaling, access, and workload needs. |
Compare candidate designs using operational ownership, capacity and scaling behavior, data locality and access controls, retrieval quality on the actual corpus, request latency, model choice, observability, and total measured workload cost. “Vector database versus no vector database” is a false choice when a relational database can also provide vector search.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to estimate cost without inventing a per-arrow price
First define the workload and architecture. The official architecture guidance describes steps and trade-offs, but does not establish a comparable current bill of materials for one fixed workload. A useful estimate must therefore be tied to explicit assumptions rather than a generic dollar figure.
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Separate one-time or update work from request work
- Ingestion and updates: Estimate content volume and update rate, parsing needs, resulting chunk count, embedding work, index writes, and retained storage.
- Serving: Estimate request rate, embedding calls, retrieval candidate count or top-k, whether hybrid search or reranking runs, prompt context tokens, generated tokens, and any safety processing.
- Ongoing capacity and operations: Include provisioned index or model-serving capacity where applicable, plus logging, retention, analytics, and evaluation.
Record assumptions that change the result
- Provider, region, model, and capacity or billing mode.
- Document count and size, formats, OCR needs, update frequency, chunk size, and overlap.
- Vector count and dimensions, index configuration, replicas, and retention.
- Requests per time period, candidate count, filters, prompt size, and expected answer length.
- Reranking, safety, logging, and evaluation settings.
Report variable request costs separately from recurring provisioned capacity when the provider bills them differently. This makes it possible to see whether an architecture is dominated by ingestion bursts, per-request model work, retained search capacity, or operational visibility.
How to read the diagram in practice
The arrows reveal where the system does work and where one stage can hold up the next. Ingestion cost follows additions and changes; serving cost follows requests and the context and output those requests generate. Optional retrieval stages may improve answer quality, but should be retained only when evaluation shows that the gain justifies their resource use and latency. The diagram is a map for workload-specific measurement, not a universal bill.
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