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A retrieval-augmented generation (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 repeats retrieval and model work for user requests. The arrows carry different data and incur different work—such as document processing, embedding, search, and generation—so there is no meaningful universal price per arrow without specifying the provider, region, workload, and configuration.
How to read a RAG architecture diagram
Think of the diagram as two flows meeting at the index. Ingestion turns source material into searchable chunks and vectors. Serving turns a user’s question into a search, selects evidence, and gives that evidence to a language model along with the question.
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Ingestion and updates:
Source systems → connector or file landing → parse, clean, and chunk → document embedding model → vector index or store
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Repeated request and response:
User → UI or API → orchestrator → query embedding → retrieval → optional hybrid merge or reranking → prompt assembly → LLM inference → optional safety checks → answer with supporting sources → observability and feedback
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What the ingestion arrows carry and cost
Ingestion work is usually triggered by initial loading, scheduled refreshes, or detected changes. A large backfill can create a burst of processing and indexing even if normal updates are infrequent.
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| Arrow | Payload and operation | Cadence, cost, and latency dimension |
|---|---|---|
| Source → connector or landing zone | Files, records, or change events are brought into the RAG system. | Runs on arrival, refresh, or backfill. Account for connector development and operation, source licensing where applicable, transfer, and landing-zone storage. Source formats and available connectors vary. |
| Landing zone → parser and chunker | Raw content is fetched, extracted, cleaned, normalized, and split into retrievable chunks. | Runs for new or changed material. Processing time, retries, and transient storage contribute to cost; scanned or complex-layout documents may also need OCR or layout extraction. Processing duration depends on the corpus and extraction method. |
| Chunks → document embedding model | Each chunk is converted into a vector representation. | Typically incurred for initial ingestion and changed chunks, not every user request. Embedding inference or compute grows with the amount of text processed; chunk size and overlap affect the number of vectors. In Google’s reference design, indexed content and runtime queries use the same embedding model and parameters. |
| Vectors and associated text or metadata → index or store | Vectors, searchable content, and metadata are written, indexed, and kept available for retrieval. | Index build and update work occurs as content changes; storage and serving capacity continue while the index is retained and available. Managed vector search and database-backed vector search allocate operational work and capacity differently, so the box does not imply a single billing model. |
What the serving arrows carry and cost
Serving is the repeated path. Some steps can be combined or skipped, but a typical request may involve an embedding call, retrieval, and generation—in addition to application work.
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| Arrow | Payload and operation | Cadence, cost, and latency dimension |
|---|---|---|
| User → UI, API, and orchestrator | A natural-language question and, where relevant, conversation context enter the application. The orchestrator coordinates downstream work. | Occurs per request. Include application compute, authentication, networking, request logging, and any session or state storage. This work may be smaller than model inference, but it should still be measured. The placement of the orchestrator varies by design. |
| Question → query embedding | The request is encoded as a vector compatible with the indexed vectors. | Usually occurs for each search request. Include query-embedding inference or compute and a serial network or API hop. Google’s design uses the same embedding model and parameters for indexed data and runtime queries. |
| Query vector → retriever and index | The retriever searches for candidate chunks and returns their text or metadata. Search may be vector-based or combine vector and lexical results. | Occurs per retrieval. Cost and response time depend on search requests, index or database serving capacity, filters, and result transfer. Asking for more candidates can improve recall but sends more material to later steps. |
| Candidates → hybrid merge or reranker (optional) | Ranked results from multiple retrieval methods may be merged; a reranker can score the query and candidates together and reorder them. | Optional work on each request that uses it. It adds compute or model calls and a serial stage. Microsoft’s Azure guidance notes that reranking adds latency compared with standard, vector, or hybrid searches. Benchmark whether its relevance improvement justifies the added time and cost. |
| Retrieved context → prompt assembly | The original question, instructions, and selected evidence are formatted into the model’s input. | Occurs per generation request. Orchestration itself uses compute, but the main downstream consequence is prompt size: more or longer chunks generally mean more model input tokens. Redundant context can add work without helping the answer, so tune context selection and answer quality together. |
| Prompt → generator or LLM | The model receives instructions, the question, and retrieved context, then generates an answer. | Occurs for each answer generated. Model input and output inference or compute, serving capacity, time to first token, and completion latency all matter. Input context and output length affect work. A portable dollar figure cannot be inferred from the diagram alone. |
| Model → safety or response processing → user | Generated text may be screened or filtered, formatted with citations, and returned to the user. | Per request when configured. Include safety-service calls or compute and response transport. Safety may be a separate hop or part of the model platform; Google’s managed architecture includes configured safety filters. |
| Request and answer → logs, metrics, and evaluation | Operational events and, where appropriate, sampled prompts and responses feed monitoring and quality analysis. | Generated alongside requests or processed later. Account for log volume, retention, analytics, evaluation compute or model calls, and data governance. This work is easy to omit from a small diagram, but production monitoring and quality evaluation require it. |
Optional boxes belong only where they solve a measured problem
- Query rewriting, augmentation, or decomposition: A preparatory step can clarify a vague question, add useful search terms, or split a multi-part request. It can also add model work and a serial call. Use it for query types where evaluation shows a benefit.
- Hybrid search: Combine lexical and vector retrieval when exact names, identifiers, or terms matter alongside semantic similarity. Because it can produce multiple ranked lists, the diagram may need a merge step.
- Reranking: Consider it after broad retrieval when ordering candidates more accurately is worth additional latency and compute. Compare relevance and latency on representative queries rather than treating reranking as a free default.
- Graph traversal: Add this when entity relationships and multi-hop connections are central to the task. It is an advanced retrieval choice, not a required RAG component.
- Evaluation loop: Draw an offline feedback arrow from logs or test sets back to chunking, retrieval, prompts, and model configuration. Evaluation can score dimensions such as factual accuracy and relevance.
How to estimate the cost of a RAG design
First separate work that happens when content is ingested from work repeated when users ask questions. Then identify which parts are billed per operation and which require capacity that stays provisioned. Report recurring request costs separately from fixed or provisioned capacity when the chosen service bills them differently.
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A useful estimate needs assumptions for:
- Ingestion volume and update rate, including the size of any initial backfill.
- Parsing and extraction needs, chunk size, overlap, and resulting chunk count.
- Embedding model, vector count and dimensions, and index or replica configuration.
- Request rate, retrieval method, filters, and number of candidate results.
- Whether query rewriting or reranking is enabled.
- Prompt context size and expected generated output length.
- Provider, region, capacity mode, and serving configuration.
- Logging, evaluation, and retention volume and duration.
Without those assumptions, an arrow-by-arrow dollar total would hide more than it explains. Architecture references establish the steps and trade-offs, but do not by themselves define a comparable bill for a fixed workload.
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The vector index is a function in the design, not necessarily a standalone vector-database product. Hosting and storage choices change who operates components and how capacity is managed.
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| Pattern | What it illustrates | Trade-off to assess |
|---|---|---|
| Managed vector search and hosted models | Google Cloud’s reference architecture uses managed services for ingestion, embeddings, vector search, and generation. | It can reduce self-operated infrastructure work, while choices remain tied to service configuration and region. |
| Self-managed serving with database-backed vectors | Google’s GKE example places frontend, inference, and embedding services in GKE and stores vectors in PostgreSQL with pgvector. | It offers a concrete route for teams choosing open models and more infrastructure control, with corresponding infrastructure operations to own. |
| Managed platform with a relational vector store | Google’s AlloyDB reference design uses a PostgreSQL-compatible vector store and separates ingestion, serving, and quality evaluation. | It demonstrates that vector retrieval can be provided by a relational database rather than a standalone vector product. |
Compare designs on operational ownership, scaling and capacity model, data locality and access control, retrieval quality on the actual corpus, request latency, model choice, observability, and total cost for the measured workload. “Vector database versus no vector database” is often the wrong framing: the relevant question is which component provides vector retrieval and whether it fits the workload.
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