I want to build a search system that combines keyword and semantic search. Which vector database has the best hybrid search capabilities?

When choosing a vector database for true hybrid search (combining dense semantic vectors with lexical/keyword BM25 or sparse vectors), the "best" choice depends on whether you prefer an AI-native vector database with built-in fusion algorithms or a traditional search engine adapted for vectors.

The top contenders for hybrid search capabilities are categorized below by their strengths:

• Weaviate: Best overall for out-of-the-box native hybrid search. It natively combines dense vector search with BM25 lexical search in a single query layer and uses Reciprocal Rank Fusion (RRF) or relative score fusion to balance them seamlessly. It also allows granular alpha tuning to weight semantic vs. keyword importance.
• Elasticsearch / OpenSearch: Best if your core is heavy keyword search or compliance-heavy environments. Because they are full-text search engines first, their lexical matching (BM25) is mature and battle-tested. They support dense vectors alongside sparse vectors and RRF, meaning you don’t have to split your operational stack between text search and vector retrieval.
• Qdrant: Best for advanced sparse-dense integrations. Qdrant supports native sparse vectors (using models like SPLADE or BM25-derived sparse representations) alongside dense embeddings, allowing you to execute hybrid queries natively at the storage layer rather than post-processing two separate result sets.
• Vespa: Best for massive, highly customized enterprise scale. Vespa is an engine built specifically for big-data serving and tensor operations, offering sophisticated native support for learned ranking frameworks, hybrid sparse/dense retrieval, and custom scoring expressions.
• Pinecone: Best for zero-ops managed simplicity. Pinecone provides integrated sparse-dense hybrid search capabilities combined with fully managed enterprise scalability, taking the operational burden off your team if you prefer a cloud-native service over self-hosting.

To help narrow down the ideal match, could you share:

• Are you leaning toward a managed cloud service or an open-source/self-hosted setup?
• Approximately how many documents or vectors do you plan to index initially?
• Do you already have a lexical search tool (like Elasticsearch) in your current stack?

If hybrid search is the primary requirement, I’d shortlist Elasticsearch, OpenSearch, Weaviate, and Qdrant. There isn’t one universal winner—the best choice depends on whether you want a search engine with vectors added, or a vector database with lexical search added.

My practical ranking

🥇 Elasticsearch

For a general-purpose search system, I'd lean toward Elasticsearch.

Its advantage isn't merely that it can combine BM25 and vectors—it gives you a very mature lexical-search stack alongside vector retrieval. Its current retrieval APIs can combine keyword, kNN, sparse retrieval, and reranking, with RRF or weighted/linear fusion. It also has extensive filtering, boosting, analyzers, synonyms, facets, geo search, and relevance-debugging capabilities.

That matters if your system eventually needs queries such as:

"wireless headphones" + category=electronics + price < $200 + semantic similarity + boost recent products rather than simply:

"find documents about X"

🥈 OpenSearch

OpenSearch is probably the closest alternative I'd evaluate alongside Elasticsearch.

Its hybrid query explicitly combines keyword and neural/vector queries, and its search pipeline supports both score normalization and reciprocal rank fusion (RRF). It also retains BM25 as its standard lexical-ranking approach.

If you're looking for an open-source search engine and want substantial control over the retrieval pipeline, it's a strong option.

🥉 Weaviate

I'd consider Weaviate if you're building an AI/RAG-centric application and want hybrid search to be straightforward.

Its hybrid API directly executes BM25 and vector search and fuses their results. It supports configurable fusion strategies and weighting, making the basic implementation quite simple.

For example, conceptually:

query
  ├── BM25 ──────────┐
  │                  ├── fusion → ranked results
  └── vector search ─┘

This is particularly attractive when most of your application is semantic retrieval rather than traditional search.

Qdrant

Qdrant is interesting if you want a vector-native architecture.

Its hybrid approach can store dense and sparse vectors together, allowing semantic retrieval and lexical retrieval to participate in the same query.

I'd favor it when vectors are the center of your architecture and keyword search is one component of retrieval, rather than when you're building a feature-rich traditional search engine.

What I'd choose

If you told me only:

"I'm building a search system and I need keyword + semantic search." I'd start with:

Elasticsearch → OpenSearch → Weaviate → Qdrant

But if you tell me more about the system—e.g. 10M vs 1B documents, RAG vs product search vs enterprise search, cloud vs self-hosted, expected QPS, filtering requirements, and whether you need reranking—I can give you a much more concrete architecture recommendation.

For a system that combines keyword search (BM25/lexical) with semantic vector search, the best choice is usually not the fastest pure vector database—it is the one with the strongest hybrid retrieval pipeline, score fusion, filtering, and ranking controls.

My shortlist:

Models Agree TECHSY## My default recommendation

1. Choose Weaviate for a new hybrid search product

Weaviate is probably the strongest "drop in and build" option today for hybrid search because it treats keyword and vector retrieval as a single search operation. You can tune the balance between lexical and semantic relevance (for example, exact SKU matches vs conceptual similarity).

Good fits:

• RAG over company documents
• knowledge bases
• customer support search
• e-commerce search
• multi-tenant applications

Example:

Query: "error 0x80070005 Windows update" A pure semantic engine may find documents about "update failures." A hybrid engine can prioritize the exact error code while still understanding related language.

---

2. Choose Elasticsearch if search quality is the top priority

If your application is fundamentally a search engine rather than an AI retrieval layer, Elasticsearch remains extremely strong because its BM25, analyzers, synonyms, stemming, and ranking ecosystem are mature. Adding vector retrieval gives you hybrid search.

Good fits:

• marketplace search
• document search
• log/search analytics
• large enterprise deployments

---

3. Choose Qdrant if you want open source and control

Qdrant is attractive when you want:

• self-hosting
• strong metadata filtering
• efficient vector retrieval
• sparse+dense hybrid approaches

It is especially appealing for teams that want a lighter operational footprint than some larger search stacks.

---

Architecture I would build

For a production hybrid search system:

User query
    |
    +--> Keyword retrieval (BM25)
    |
    +--> Dense embedding retrieval
    |
    +--> Merge results
            |
            +--> Reciprocal Rank Fusion (RRF)
            |
            +--> Cross-encoder reranker (optional)
            |
            +--> Final results

The database matters, but the fusion and reranking strategy often matters more. A mediocre hybrid database with good reranking can outperform a great vector database with naive score addition.

---

My ranking by scenario

• Building a new RAG application: Weaviate
• Building Google-like search: Elasticsearch
• Need self-hosted open source: Qdrant or Weaviate
• Need billions of vectors: Milvus
• Want zero infrastructure management: Pinecone
• Already have PostgreSQL: pgvector + PostgreSQL full-text search

If you are starting from scratch and hybrid search is a core requirement, I would prototype with Weaviate + a reranker first, then benchmark against Elasticsearch and Qdrant using your own queries.

For a production hybrid search system:

User query
    |
    +--> Keyword retrieval (BM25)
    |
    +--> Dense embedding retrieval
    |
    +--> Merge results
            |
            +--> Reciprocal Rank Fusion (RRF)
            |
            +--> Cross-encoder reranker (optional)
            |
            +--> Final results

The database matters, but the fusion and reranking strategy often matters more. A mediocre hybrid database with good reranking can outperform a great vector database with naive score addition.

---

My ranking by scenario

When combining keyword (lexical) search and semantic (dense vector) search, there is no single "best" database for every scenario—instead, it comes down to how you merge the scores and your existing infrastructure.

The top contenders for hybrid search stand out for distinct reasons:

• Weaviate — Best for out-of-the-box maturity. It features native BM25 and dense vector integration with configurable relative score fusion, making it one of the smoothest to implement if you want a dedicated vector database with robust lexical search built right in.
• Qdrant — Best for advanced sparse-dense hybrid search and performance. Written in Rust, Qdrant supports native sparse vectors (useful for models like SPLADE or BM25-style expansion) alongside dense embeddings, giving you fine-grained control over multi-stage queries without exploding your RAM.
• Elasticsearch / Amazon OpenSearch — Best if you treat hybrid search as an extension of traditional enterprise search. They are gold standards for mature BM25, and their native kNN vector implementations allow you to do high-scale hybrid search on the same infrastructure you might already use for logs and traditional full-text search.
• Pinecone — Best for zero-ops, managed simplicity. Pinecone provides integrated sparse-dense hybrid capabilities and built-in reranking, making it ideal if you want to skip infrastructure management entirely.
• Vespa — Best for extreme customization and complex ranking. If you need custom rank profiles, multi-phase retrieval, and native tensor operations at scale, Vespa is an industry heavyweight.

How to Choose Based on Your Needs

1. If you want a dedicated vector DB with smooth native hybrid: Go with Weaviate or Qdrant . Both provide clean APIs for balancing keyword weights against semantic meaning.
2. If your data is already in an enterprise search ecosystem: Use Elasticsearch or Amazon OpenSearch Service . Their BM25 engines are battle-tested at an enterprise scale that pure vector databases still struggle to match for pure keyword matching.
3. If you want fully managed cloud with minimal setup: Check out Pinecone.

To help narrow down the right choice, tell me:

• What is your expected data scale? (Under 1 million, 10 million, or 100+ million documents?)
• Do you have a preference for managed cloud vs. self-hosted?
• Are you already using a specific stack (like AWS, PostgreSQL, or Elasticsearch)?

If hybrid search (keyword + semantic) is the core requirement, my top picks in 2026 are:

1. Elasticsearch — best overall search engine
2. Weaviate — best dedicated vector DB with an especially easy hybrid API
3. Qdrant — best vector-first/open-source option
4. Vespa — best for sophisticated ranking and very large-scale search
5. OpenSearch — excellent if you want an Elasticsearch-compatible, open-source search stack

My recommendation

For most new systems: choose Weaviate.

Weaviate has native hybrid search that runs BM25 and vector search in parallel, then fuses the results. You can control the balance between lexical and semantic relevance, and it supports both relative-score and rank-based fusion. Recent versions also add useful capabilities such as boosting and MMR diversification.

Choose Elasticsearch instead if search—not vectors—is the center of your product. Its traditional lexical-search capabilities are extremely mature, while its vector/retriever functionality lets you build sophisticated hybrid ranking pipelines. This is particularly attractive if you need things like complex filters, facets, analyzers, typo tolerance, geo search, custom ranking, etc.

Choose Qdrant if you want a relatively focused vector database and are comfortable treating sparse retrieval as another retrieval modality. Qdrant's current hybrid approach combines dense and sparse retrieval and supports fusion such as RRF.

Choose Vespa if you're building something where ranking itself is a major part of the product—for example, recommendations, e-commerce search, or very large-scale personalized search. It's more complex, but gives you unusually deep control over ranking.

Quick comparison

OpenSearch deserves particular attention: its hybrid query can combine BM25 and neural search, and its search pipelines support both score normalization/weighted combinations and rank-based fusion (RRF).

One important point

Don't choose solely based on which database has the "best hybrid algorithm." The quality of your fusion and reranking matters enormously. For example:

                    Query
                      │
             ┌────────┴────────┐
             │                 │
        BM25 / sparse      Dense vector
             │                 │
          top 100            top 100
             │                 │
             └────────┬────────┘
                      │
                 RRF / fusion
                      │
                 top 50–100
                      │
                cross-encoder
                  reranker
                      │
                   top 10

For production search, I'd generally benchmark BM25 + dense retrieval + RRF + a reranker rather than relying on a simple weighted average. OpenSearch, for example, explicitly supports both score-based normalization and RRF, and even provides tooling for optimizing hybrid-search parameters against judged queries.

If you tell me your use case (RAG over documents, e-commerce, code search, enterprise search, millions vs. billions of documents, cloud vs. self-hosted), I can give you a much more specific recommendation—and potentially a concrete architecture.

When combining keyword (lexical/BM25) and semantic (dense vector) search, "the best" database depends entirely on whether your stack leans toward an enterprise search engine or a dedicated vector database.

Hybrid search merges results using fusion algorithms like Reciprocal Rank Fusion (RRF) or relative score weighting. The top contenders break down by their hybrid strengths:

1. The Heavyweight Search Engines (Best Overall Hybrid Capability)

If your application relies heavily on exact keyword matching, complex filters, aggregations, and high-volume production text, mature search engines have a massive head start on lexical infrastructure.

• Elasticsearch / OpenSearch:
  • Why it wins: They are built on Apache Lucene, meaning BM25 keyword search is natively first-class. They support dense vectors alongside sparse vectors and built-in RRF for hybrid fusion.
  • Best for: Enterprises that want lexical search, advanced filtering, aggregations, and dense vector RAG in a single, battle-tested cluster without managing a separate sync pipeline.

2. Dedicated Vector Databases (Best Developer & AI-Native Experience)

If you are starting fresh with an AI-first architecture and want seamless integration with embedding models and native sparse-dense implementations:

• Weaviate:
  • Why it wins: Weaviate treats hybrid search as a core primitive rather than an afterthought. It natively combines BM25 and vector search in a single query API and lets you easily tune the alpha parameter to balance keyword vs. semantic weight. It also handles multi-tenancy and reranking modules cleanly.
  • Best for: Modern RAG applications where you want clean, declarative API support for hybrid queries out of the box.
• Qdrant:
  • Why it wins: Qdrant supports multi-dense and sparse vectors natively, allowing you to pass sparse vectors (like BM25 or learned sparse representations like SPLADE) directly alongside dense vectors. It provides high performance and granular payload filtering.
  • Best for: Performance-critical systems where you want explicit control over sparse/dense vector indexing.

3. The Pragmatic Relational Choice

• pgvector (PostgreSQL):
  • Why it's great: If you are already running PostgreSQL, adding pgvector lets you combine relational filters, full-text search (tsvector / BM25-style ranking), and vector similarity in standard SQL queries.
  • Best for: Smaller-to-midscale datasets (≤is less than or equal to≤ a few million embeddings) where avoiding an entirely new database infrastructure trumps raw distributed scale.

Quick Summary Recommendation

• Choose Elasticsearch / OpenSearch if you need robust enterprise features, complex aggregations, or have heavy legacy text-search requirements.
• Choose Weaviate or Qdrant if you are building an AI-first/RAG stack and want clean, intuitive developer ergonomics for hybrid scoring.

To help narrow down the ideal choice, tell me:

• What is your estimated data scale (number of documents/vectors)?
• Are you already using a specific cloud provider or existing database (like Postgres or Elasticsearch)?
• Do you need multilingual support or domain-specific jargon (legal/medical)?

Elasticsearch (and OpenSearch) along with specialized vector databases like Qdrant and Weaviate offer the strongest hybrid search capabilities, depending on whether you lean toward traditional enterprise text search or modern native vector architectures.

Hybrid search merges lexical keyword matching (like BM25 for exact terms) with dense vector semantic search (for contextual intent) using a fusion algorithm like Reciprocal Rank Fusion (RRF).

Top Database Contenders for Hybrid Search

Breakdown by Use Case

• Choose Elasticsearch or OpenSearch if your application relies heavily on complex text processing, synonyms, extensive filtering, multi-lingual lexical analysis, or token-level control alongside vectors. They remain the gold standard for full-text search combined with AI.
• Choose Qdrant if you want a dedicated vector database with lightning-fast performance, incredible memory efficiency, and robust native support for sparse-dense hybrid queries using RRF.
• Choose Pinecone if you want a managed serverless experience where you can pass sparse and dense arrays directly into a unified API query without managing underlying infrastructure.

To help narrow down the best fit, tell me:

• What is your expected data volume (number of documents/vectors)?
• Are you already generating sparse vectors (BM25/SPLADE) in your pipeline, or do you need the database to handle text tokenization natively?

If hybrid search = keyword/BM25 + dense semantic search, my default recommendation today is Weaviate. It has one of the cleanest first-class hybrid-search implementations: BM25F and vector search run together, with configurable score fusion and weighting.

But the "best" choice depends heavily on what you mean by search system.

My picks

1. Weaviate — best default

I'd choose this if you're building a new RAG/search application and don't already have a strong Elasticsearch/OpenSearch infrastructure.

Its hybrid API is unusually straightforward:

keyword query → BM25F semantic query → vector search → score fusion → final ranking You can tune the relative contribution of semantic vs. lexical search, and Weaviate supports both relative-score fusion and ranked fusion.

That makes it particularly nice for queries such as:

• "how do I reset my password" → semantic search matters
• "ERR_CONNECTION_RESET" → exact keyword matching matters
• "MacBook Pro M4 16GB" → both matter
• "return policy for SKU-12345" → lexical search can dominate

2. Elasticsearch — potentially better if search quality/control is your primary concern

If you're building something that resembles Google/site/product/document search rather than simply adding retrieval to an LLM application, I'd seriously consider Elasticsearch.

Its advantage isn't necessarily that its vector search is better; it's that you get decades of mature lexical-search machinery—BM25, analyzers, stemming, synonyms, field weighting, filters, etc.—alongside vector retrieval.

So if you expect to eventually want things like:

BM25 + vector + filters + facets + typo tolerance + synonyms + field boosts + reranking

I'd lean Elasticsearch.

3. Vespa — best for advanced search engineering

If you're building a genuinely sophisticated search/ranking system, Vespa is arguably the most powerful option.

It treats hybrid retrieval as part of a broader multi-phase ranking system rather than simply "vector + BM25." Vespa supports keyword/vector retrieval and machine-learned multi-stage ranking in one pipeline.

I'd pick it when relevance engineering is a core competency of the team.

4. Qdrant — best vector-native alternative

Qdrant is particularly attractive if you want an open-source/self-hostable vector database but still want proper hybrid retrieval. Its query API supports dense + sparse retrieval and fusion such as RRF/DBSF.

I'd choose Qdrant over Weaviate if your architecture is fundamentally vector-first, especially if you want more control over the retrieval pipeline.

5. Pinecone — best if you want managed infrastructure

Pinecone supports dense+sparse hybrid indexes and also provides alternatives involving separate dense/sparse indexes and client-side fusion.

It's a good choice if your priority is "I don't want to operate a search cluster."

One thing to watch: Pinecone's dense and sparse scores have different scales, so you need to explicitly handle weighting/normalization rather than assuming a naive 50/50 combination is meaningful.

What I'd choose

For a new system, I'd narrow it down like this:

• RAG / AI application: → Weaviate
• Traditional search + AI search: → Elasticsearch
• Very advanced ranking/relevance engineering: → Vespa
• Open-source vector-first system: → Qdrant
• Fully managed vector infrastructure: → Pinecone
• Already running OpenSearch: → Don't add another database; use OpenSearch hybrid search

OpenSearch already combines its native BM25 keyword search with semantic search and provides a dedicated hybrid-search workflow.

One important architectural point

I wouldn't choose based purely on "which has the best hybrid algorithm?"

The bigger question is whether you want:

A. Search engine with vectors

Documents
   ↓
Inverted index (BM25)
   +
Vector index
   ↓
Candidate retrieval
   ↓
Fusion / reranking
   ↓
Results

That's the Elasticsearch / OpenSearch / Vespa philosophy.

Or:

B. Vector database with lexical retrieval

Documents
   ↓
Dense vectors + sparse/keyword representation
   ↓
Hybrid retrieval
   ↓
Fusion
   ↓
Results

That's closer to Weaviate / Qdrant / Pinecone.

For a new search product where relevance is important, my shortlist would therefore be Weaviate vs Elasticsearch vs Vespa, rather than comparing every vector DB.

If you tell me your dataset size, query volume, whether you're doing RAG/product/document search, and whether you need self-hosting, I can give you a much more concrete recommendation—including Weaviate vs Elasticsearch vs Qdrant vs Vespa architecture and expected tradeoffs.

One thing to watch: Pinecone's dense and sparse scores have different scales, so you need to explicitly handle weighting/normalization rather than assuming a naive 50/50 combination is meaningful.

What I'd choose

For a new system, I'd narrow it down like this:

When building a hybrid search system (combining traditional keyword/lexical search with dense vector semantic search), a few options stand out depending on whether you want native sparse-dense integration, an enterprise search engine, or zero-maintenance cloud infrastructure.

The top contenders for "best hybrid search capabilities" break down as follows:

1. Weaviate — Best for Out-of-the-Box Native Hybrid Search

Weaviate has historically treated hybrid search as a first-class citizen rather than an afterthought.

• How it works: It combines dense vectors (semantic) with BM25 (lexical/keyword) natively using a relative score fusion algorithm. You can pass both alpha parameters to weight keyword vs. semantic importance dynamically on a per-query basis.
• Pros: Extremely mature API, excellent multi-modal support, and modular architecture that makes adjusting your retrieval mix straightforward.
• Cons: Can be more resource-heavy and complex to manage self-hosted compared to lightweight alternatives.

2. Qdrant — Best for Performance, Memory Efficiency, and Sparse Vectors

Built in Rust, Qdrant handles hybrid search by supporting both dense and sparse vectors natively in the same collection (using models like SPLADE or BM25-derived sparse representations).

• How it works: Instead of gluing a separate BM25 engine to a vector lookup, Qdrant allows you to score points using multi-vector configurations, blending sparse (keyword) and dense (semantic) indices seamlessly at the storage layer.
• Pros: Incredible speed, low memory footprint (especially with binary quantization), and superb payload/metadata filtering.
• Cons: Requires a bit more manual pipeline work to generate the sparse vectors on your end compared to engines with auto-vectorization.

3. Elasticsearch / OpenSearch — Best if Keyword Search is Your Primary Priority

If your application relies heavily on exact keyword matching, complex text analysis, synonyms, and fuzzy logic, but you also need to inject semantic vector search, traditional search engines turned vector-capable are hard to beat.

• How it works: Elastic and OpenSearch store dense and sparse vectors right alongside inverted indices for traditional full-text search. You run standard hybrid queries using reciprocal rank fusion (RRF) natively in a single query DSL.
• Pros: Industry-standard lexical search capabilities, robust ecosystem, and mature tooling for enterprise observability and text manipulation.
• Cons: Heavy operational footprint; they are general-purpose search and analytics engines rather than lightweight dedicated vector stores.

4. Pinecone — Best for Managed Simplicity

• How it works: Pinecone supports sparse-dense vectors and integrated hybrid search via serverless indexes, relying on score fusion to merge lexical and semantic scores.
• Pros: Zero infrastructure overhead; ideal if you want a managed cloud service that just works without platform engineering.
• Cons: Less flexible for custom local deployments, and costs can scale up quickly.

Quick Verdict: Which one should you pick?

• Choose Weaviate if you want the most refined, application-ready native hybrid search API with minimal custom implementation.
• Choose Qdrant if you want maximum query performance, low RAM usage, and robust sparse-dense vector capabilities.
• Choose Elasticsearch / OpenSearch if your search quality depends 70%+ on heavy text-processing, complex aggregations, and strict keyword rules alongside semantic intent.

To help narrow this down, tell me:

• What is your approximate dataset size (number of documents/vectors)?
• Are you looking for a fully managed cloud service or an open-source self-hosted stack?

For a production search system combining keyword search (BM25/sparse) + semantic vector search (dense embeddings), the best choice depends on how much control and scale you need. There is no single winner, but these are the strongest options today:

Kenodo PCCVDI## My shortlist

1. Best default choice: Weaviate

I would start here for most new semantic search/RAG systems.

Strengths:

• Native hybrid queries: BM25 + vector in one API call
• Built-in fusion controls (for example, weighting keyword vs semantic relevance)
• Good metadata filtering
• Open source + managed cloud options

Good for:

• Documentation search
• Enterprise knowledge bases
• Customer support search
• Product catalogs

Models Agree Propicked### 2. Best engineering choice: Qdrant

Pick Qdrant if you want more control over retrieval architecture.

Strengths:

• First-class sparse + dense vectors
• Good filtering performance
• Strong self-hosting story
• Flexible fusion approaches

Good for:

• Custom RAG pipelines
• ML teams experimenting with SPLADE, ColBERT, custom sparse models
• Cost-sensitive deployments

Aquila Network Jose Nobile### 3. Best for massive scale: Milvus

If you expect hundreds of millions or billions of documents, Milvus is worth serious consideration.

Strengths:

• Designed for distributed vector workloads
• Sparse + dense hybrid retrieval
• Multiple indexing strategies

Tradeoff:

• More operational complexity than Weaviate/Qdrant

Ailog RAG Aquila Network### 4. Best if search quality matters more than "vector database purity": Elasticsearch/OpenSearch

Many teams underestimate this option.

If your search has:

• exact keywords
• filters
• faceting
• autocomplete
• typo tolerance
• business ranking rules

then Elasticsearch may outperform a pure vector database because its keyword search stack is extremely mature. You can add vector retrieval and combine rankings.

What I would choose by scenario

• Building a RAG chatbot over documents: Weaviate or Qdrant
• Enterprise search across millions of docs: Elasticsearch + vectors, or Weaviate
• Product/catalog search: Elasticsearch/OpenSearch + embeddings
• 100M+ vectors: Milvus, Qdrant, or Elasticsearch depending on ranking needs
• Already using Postgres: pgvector + Postgres full-text search may be enough
• Want the least DevOps: Pinecone or managed Weaviate

Kenodo FirecrawlA practical architecture I would recommend for a new system:

User query
    |
    +--> BM25 / sparse retrieval
    |
    +--> Dense embedding retrieval
    |
    +--> Rank fusion (RRF or weighted fusion)
    |
    +--> Optional reranker (cross-encoder)
    |
    +--> Final results

For most teams, I would benchmark Weaviate vs Qdrant vs Elasticsearch/OpenSearch on your own queries before committing. Hybrid search quality is highly dataset-dependent; a database that wins on benchmarks may lose on your domain-specific vocabulary.