--- title: "Cost-efficient alternatives for large-scale search and vector databases" description: "What sets the cost of full-text and vector search at scale, the alternatives to Elasticsearch and dedicated vector databases for large datasets, and the trade-offs between a dedicated search engine and SQL with search extensions." canonical: https://infino.ai/blog/cost-efficient-search-and-vector-alternatives/ published: 2026-10-05 --- # Cost-efficient alternatives for large-scale search and vector databases Oct 5, 2026 ยท engineering At scale, what a search or vector engine costs is set by its architecture more than by its list price. Three questions decide most of the bill: how many copies of the data the engine keeps, what kind of storage holds them, and whether compute has to grow every time the corpus does. A system that keeps replicated copies on always-on block storage costs more per stored gigabyte than one that keeps a single copy in object storage, and the gap widens as the corpus grows while the query load stays flat. This page compares architectures, not price lists. For estimates on a real workload, the [pricing methodology](https://infino.ai/pricing-methodology/) shows every input behind our calculator. | system | copies of the data | where it lives | what grows with the corpus | SQL over results | | -------------------------- | -------------------------------------------------------- | ---------------------------------------------------------- | ---------------------------------------------------- | --------------------------------------- | | Infino | one searchable copy, as Parquet | object or block storage, hot ranges cached in RAM and NVMe | compute scales apart from the corpus | yes, search runs as SQL table functions | | Elasticsearch / OpenSearch | primary plus replicas, in engine-private segments | hot tier on block storage, object-backed tiers slower | cluster grows with retained data | partial: ES\|QL, a SQL plugin, PPL | | Qdrant | engine-private indexes | block storage on always-on servers | servers sized to the index | no, retrieval API | | Pinecone | engine-private indexes | object storage, managed | usage-priced, dedicated read nodes at sustained load | no, retrieval API | | Postgres + pgvector | tables plus text and vector indexes in the same database | block storage | search and the application share instances | yes, full Postgres SQL | | ClickHouse | MergeTree tables | block or object storage | nodes sized to the working set | yes, its own dialect | Checked against each project's documentation in September and October 2026. The [comparison pages](https://infino.ai/vs/) go system by system. ## Alternatives to traditional full-text search engines A traditional search cluster such as Elasticsearch or OpenSearch keeps its index in engine-private segments on block storage attached to always-on nodes, with replicas for availability. Indexing a large dataset takes real compute, and the hot tier has to hold everything that must answer quickly. Retained data and query capacity grow together, so a larger corpus means a larger cluster even when traffic stays flat. Both offer object-backed warm and cold tiers, which cost less and answer more slowly. The alternatives for search over large datasets fall into three groups: - **The same model under a different license.** OpenSearch is Apache-2.0 and keeps the cluster architecture, so the cost shape stays the same. - **Text search inside a database you already run.** Postgres has native full-text search, with BM25 from an extension. ClickHouse has fast token filtering next to analytical SQL. Both avoid a second system, and both put search load on the database that also serves everything else. - **Object-storage-native retrieval.** The index lives in object storage at object-storage rates, a local cache holds the hot part, and compute is sized to the queries rather than to the corpus. Infino is in the third group. It is an open source retrieval engine for full-text, vector and SQL search that keeps one searchable copy as standard Parquet in object storage, with the BM25 and vector indexes inside the files. There is no hot tier or replica set to size for retention, and nothing in the local cache is the durable copy. Infino implements a supported Elasticsearch Query DSL subset, so compatible clients keep the shape of their requests. [The Elasticsearch replacement page](https://infino.ai/elasticsearch-replacement/) covers the migration path. ## Cost-efficient vector database alternatives Four factors set the cost of a large vector corpus: - **Storage footprint.** Full-precision embeddings are large, and an index that must sit in memory or on local disk multiplies the price of every stored vector. - **Indexing compute.** Building and rebuilding an approximate index over millions of vectors is a significant job that recurs as data changes. - **Serving capacity.** Always-on servers sized to the index cost the same at midnight as at peak. - **Operations.** Sharding, rebalancing and upgrades of a dedicated cluster are ongoing work for someone. Dedicated vector engines such as Qdrant serve from always-on servers sized to the index, with quantization settings to shrink it. Serverless services such as Pinecone price by usage, which suits uneven traffic and adds up at a high sustained query rate. pgvector keeps vectors in Postgres, which is efficient while the corpus is modest and the database has room. The other approach integrates vector search into the storage format the data already lives in. Infino stores the embeddings in the Parquet file next to the rows, so retention is billed at object-storage rates, and serves them from an approximate IVF index with a full-precision rerank, tuned from the table's own data. Hot byte ranges stay in a RAM and NVMe cache, and compute scales apart from the corpus. There is no dedicated vector cluster to keep running. [Vector search on object storage](https://infino.ai/blog/vector-search-on-object-storage/) explains how the index stays fast without living in memory. ## Trade-offs in search architecture The choice usually comes down to two familiar patterns and one newer one: - **SQL with search extensions.** Postgres with pgvector and a BM25 extension keeps search next to your data and the SQL you already write. The text index, the vector index and the application workload then share one database you scale and replicate, standard Postgres has no native horizontal sharding when that node fills, and combining the two rankings is your own code. - **A dedicated search engine.** Elasticsearch, OpenSearch, Qdrant or Pinecone ranks well and scales search on its own. It also holds a second copy of the data in a format only it reads, needs a sync loop to keep that copy current, and offers partial SQL or none. - **One engine over an open format.** Search, ranking and SQL run in one engine over files that other tools can also read. Infino takes the third path. BM25, vector search and reciprocal rank fusion run in one query, and each search is a table in DataFusion SQL, so results can be filtered, joined and grouped in the same statement. SQL is read-only, and writes go through the API. The open format is what keeps the architecture flexible. Data in a proprietary format can be read only through the engine that wrote it, so leaving costs a full export and reload. Parquet is readable by any Parquet reader, so the same files serve search, analytics and export without a second copy. Infino's embedded search indexes require Infino, and the columns stay ordinary Parquet. [You don't need two databases for hybrid search](https://infino.ai/blog/hybrid-search-one-engine/) walks through the cost of the two-system pattern. ## Common questions ### What's a cheaper alternative to Elasticsearch for search over large datasets? An engine that keeps the index in object storage instead of on replicated block storage, so retained data stops setting the cluster size. Infino keeps one searchable copy as Parquet in object storage and implements a supported Elasticsearch Query DSL subset. OpenSearch keeps the same cluster model under Apache-2.0, and Postgres or ClickHouse add text search to a database you already run. [Elasticsearch replacement](https://infino.ai/elasticsearch-replacement/). ### Which vector databases are cost-efficient for managing large datasets? The ones that store vectors at object-storage rates and size compute to the queries rather than to the corpus. Infino keeps embeddings in Parquet on object storage with an IVF index and a full-precision rerank. Serverless vector services suit uneven traffic, and dedicated vector servers stay on and are sized to the index. [Infino vs Qdrant and Pinecone](https://infino.ai/vs/qdrant-pinecone/). ### Should I use a dedicated search engine or SQL with search extensions? Extensions keep search next to your data but load the database you scale. A dedicated engine scales search on its own but adds a second copy and a sync loop. Infino is a search engine whose results are SQL relations over Parquet, which removes the second copy without loading the application database. [Infino vs Postgres and ClickHouse](https://infino.ai/vs/postgres-clickhouse/). ### Is Infino cheaper than Elasticsearch or OpenSearch? Infino keeps one searchable copy in object storage, reducing the hot storage and replica capacity carried by a traditional search cluster. Infino Cloud meters storage, write tokens, read tokens, and returned bytes. Plans are on the pricing page. [Pricing](https://infino.ai/pricing/).