Can prompt caching tame RAG costs without sacrificing accuracy?

Every other tutorial makes retrieval-augmented generation look trivial: chunk a document, embed it, dump it in a vector database, done. That works fine on a laptop or in front of a friendly beta group. It falls apart the moment a real B2B customer uploads a 500-page compliance manual instead of a tidy PDF, because enterprise data doesn't arrive clean, static, or free of legal constraints.

The first crack shows up during ingestion. A synchronous pipeline that parses a document, calls an embeddings API chunk by chunk, and writes vectors to a database will blow past standard HTTP timeouts of 30 to 60 seconds on anything sizeable. Worse, a rate-limit hit or latency spike can kill the whole job with a 500 error, and the user's document just vanishes. The fix is a batched fan-out pipeline: the API stores the raw file in S3, fires a document_uploaded event, and immediately returns a 202. A lightweight consumer then chunks the file and groups pieces into batches — 64 chunks at a time, for example — while embedding workers pull those batches under a token bucket rate limiter instead of relying on fragile sleep() calls. That keeps individual tasks short and avoids both broker timeouts and a self-inflicted denial-of-service against the embedding API.

Multi-tenancy is the second trap, and it's the one most teams treat as an afterthought. Dumping every tenant's vectors into one index with a tenant_id filter looks efficient until an engineer misconfigures that filter and one client sees another client's confidential data — a compliance-breaking event in regulated industries. And even without a mistake, a single tenant uploading 10 million vectors can degrade search performance for every other tenant sharing that index. Giving each customer a fully dedicated database cluster solves the isolation problem but is too expensive and too operationally heavy to run at scale. Serverless vector databases — Pinecone Serverless and managed Qdrant are cited as examples — split the difference by decoupling compute from storage, isolating vectors into namespaces that only spin up compute when queried, killing idle costs while keeping tenants walled off.

Once ingestion and isolation are handled, cost becomes the last problem, and semantic caching is the obvious but flawed answer. Embedding a prompt and matching it against past prompts by cosine similarity sounds smart, but it misses specific entities. "What was the holiday policy in 2023?" and "What is the holiday policy for 2024?" score almost identically similar, yet serving the cached answer hands the user outdated or wrong information. The workaround is either an application-layer hybrid — an exact-match token filter to throw out mismatched years, backed by a cheap model that checks whether two queries actually share the same intent — or leaning on infrastructure-level prompt caching instead. That second approach doesn't cache the user's short question at all; it caches the massive system instructions and retrieved context, often 10,000-plus tokens, that repeat across queries hitting the same knowledge base. Providers recognize that repeated block, cut context token costs by up to 80%, and drop time-to-first-token to milliseconds.

None of this is glamorous, and none of it shows up in a five-minute demo. But it's the difference between a RAG prototype and something that survives contact with actual enterprise traffic, actual tenants, and actual bills.