Name two situations where Flat brute force search is still the correct production choice in a 2026 vector pipeline.

**Flat search** is brute force scan: compare the query vector to every vector in the corpus and return the K with the smallest distance. It is O(N), exact, and the simplest possible vector search. It is also the baseline that every ANN method is measured against. In 2026 production, two situations still call for Flat. **(1) Tiny corpora.** Below roughly 100k vectors, building an HNSW or IVF index costs more (in engineering time, memory overhead, and parameter tuning) than the brute force scan saves at query time. A 50k-vector corpus with 1024-dim float32 embeddings is 200 MB; a single CPU can scan that in 2-5ms. ANN cannot beat that meaningfully and adds operational complexity. Pinecone, Weaviate, and Qdrant all expose Flat (or 'FLAT' / 'BRUTE_FORCE') as an explicit per collection option for this regime. **(2) Ground-truth generation for ANN tuning.** When you tune an HNSW M and ef_search, an IVF nlist and nprobe, or a PQ pq_m, you measure recall@K. Recall is defined as the fraction of the true top-K that your ANN method retrieves. The only way to know the true top-K is to run Flat on the same query set. Every recall number in every ANN benchmark traces back to a Flat baseline. A third niche: regulated workloads. Legal discovery, certain financial-compliance contexts, and some healthcare use cases contractually require 'exact nearest neighbor' rather than 'approximately nearest neighbor.' Flat is the only method that satisfies the spec.

**Flat is brute force search:** compute distance to every vector, sort, return top-K. It is O(N) in corpus size and exact by construction. **Situation 1: tiny corpora.** Below roughly 100k vectors at typical embedding dimensionality (768-1536), Flat returns in single digit milliseconds on a single CPU core. ANN at that scale adds build time (HNSW graph construction is non trivial), memory overhead (the graph itself plus index metadata), and parameter-tuning surface area, all to save microseconds the user will never feel. Pinecone exposes Flat via `metric=cosine, index_type=flat`. Weaviate has a Flat vector index type explicitly for small classes. Qdrant offers `on_disk=true` with no graph as the equivalent. The 2026 reality: any corpus that fits comfortably on one machine in raw form and serves under 100 QPS is a Flat candidate. **Situation 2: ground truth for ANN tuning.** Every recall@K number in every ANN benchmark depends on Flat. Recall is defined as `|ANN_topK ∩ true_topK| / K`, and the only way to compute `true_topK` is to brute force the query set. When tuning HNSW M, ef_search; IVF nlist, nprobe; PQ pq_m; or any quantization scheme, the workflow is: (a) generate a representative query set, (b) run Flat on the corpus for each query to get the true top-K, (c) sweep the ANN parameter, (d) compute recall against the Flat baseline at each parameter setting. Without the Flat baseline, you are tuning blind. **Situation 3 (niche but real): regulatory or contractual exactness.** Legal discovery (eDiscovery), certain financial-compliance workloads, and healthcare contexts that require deterministic similarity matching cannot use ANN because 'approximately' is not a defensible word in those contexts. Flat is the only method that satisfies an 'exact nearest neighbor' contract.