Vector database solutions selected on your workload, not on a benchmark blog post
Our vector database solutions give you vector search that stays fast and predictable in cost as your corpus grows. We size your workload and compare access filters, latency, operating cost and maintenance needs before choosing or tuning a database.
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What is a vector database?
A vector database stores numerical embeddings that represent the meaning of text, images or other data, and finds the most similar items to a query quickly. It is also the storage and search layer beneath semantic search, retrieval-augmented generation and recommendation systems, where results are matched by meaning rather than exact keywords.
The database chosen before the workload was known
Vector database selection usually happens at the prototype stage, when the corpus is small and everything is fast. The decision then calcifies, and the constraints surface at production scale when migration is expensive.
The other common outcome is over-engineering. For example, teams deploy a dedicated distributed vector store for two hundred thousand documents, which Postgres with pgvector would have served on hardware they already run.
Size the workload, then pick
We start with the numbers that actually determine the choice: corpus size, growth rate, query volume, latency budget, filtering complexity, update frequency and whether data can leave your infrastructure.
Those answers usually narrow the field of vector database solutions to one or two options quickly. We will frequently recommend pgvector on a database you already operate, because adding infrastructure has a real ongoing cost that comparison articles never count.
What our vector database solutions include
Choosing the database is the smaller half. However, the larger half is index configuration, and it is where most performance and cost problems originate.
Index type, dimensionality, quantization, and the recall-versus-latency trade-off all need deciding against your actual query patterns. For example, a configuration that returns 95% recall in 40 milliseconds may cost four times one returning 92% in 60 milliseconds, and whether that matters depends entirely on the application.
Our vector database solutions also cover the operational realities: how documents get updated and deleted, how re-indexing happens without downtime, how filtered queries stay fast, and what happens when the embedding model changes.
Our vector database consulting covers vector database development and vector database integration: vector similarity search, vector embeddings storage, a vector database for RAG and vector search optimization for speed and recall.
- Workload sizing: corpus, growth, query volume, latency budget, filter complexity
- Database selection with the reasoning and trade-offs documented
- Index design: type, parameters, quantization, recall/latency/cost tuning
- Embedding model selection and dimensionality decisions
- Update, deletion and re-indexing strategy without downtime
- Monitoring, cost tracking and capacity planning
Is vector database right for your business?
Teams building semantic search, RAG or recommendation features where the prototype worked and production did not. Specifically, latency creeping upward as the corpus grows is the classic symptom.
Vector database solutions also help teams facing a cost surprise. That is because managed vector services price on stored vectors and query volume, and a corpus that grows tenfold produces a bill that grows with it, often to the point where self-hosting becomes clearly cheaper.
- Prototypes that degraded when the corpus reached production scale
- Teams whose managed vector service bill is growing faster than usage
- Applications where filtered queries have become unacceptably slow
- Organizations with data residency rules that rule out managed services
- Teams planning an embedding model change and needing a migration path
- Engineering groups wanting an independent second opinion before committing
What vector database can do for your business
A choice you can defend
Selection driven by your workload numbers, with the trade-offs and rejected options documented for future reference.
Predictable cost
Cost modeled at projected scale before commitment, so the bill at ten times the corpus is not a surprise.
Latency that holds under growth
Index configuration tuned against real query patterns, not defaults, so performance survives corpus growth.
Filters that stay fast
Pre-filtering and index design that keeps metadata-filtered queries performant, which is where naive setups collapse.
No unnecessary infrastructure
We regularly recommend pgvector on your existing database, because a new system to operate has real ongoing cost.
A migration path
Embedding models change. We design so re-embedding and re-indexing is a planned operation rather than an emergency.
Problems vector database solves
Latency growing with the corpus
Queries fine at 10k documents, slow at 2M. Index type and parameter tuning is usually the fix rather than more hardware.
Managed service bill escalating
Pricing scaling with stored vectors. So we model self-hosted alternatives and show the crossover point.
Filtered queries collapsing
Metadata filters causing timeouts. Instead, pre-filtering strategy and index design fix what post-filtering cannot.
Data residency blocking a managed service
Compliance ruling out a hosted vector store. Instead, we design self-hosted equivalents inside your boundary.
No way to change embedding models
Locked to one model because re-embedding is unplanned. Because of that, we build the migration path from the start.
Over-engineered from day one
Distributed clusters for a modest corpus. Right-sizing, however, frees both budget and operational attention.
What every vector database engagement includes
Everything below is in scope on a standard engagement. Equally, nothing listed here becomes an upsell halfway through the build.
Workload analysis
Corpus size and growth, query volume and shape, latency budget, filter cardinality and update patterns, quantified before any recommendation.
Database evaluation
Candidates benchmarked against your actual data and queries rather than public benchmarks, with results documented.
Index design and tuning
HNSW, IVF or flat index selection, parameter tuning, and quantization decisions balancing recall, latency and memory.
Embedding strategy
Model selection, dimensionality trade-offs, and whether domain-specific embeddings justify their cost for your content.
Hybrid search implementation
Combining dense vector search with keyword matching and reciprocal rank fusion, since pure semantic search misses exact identifiers.
Update and re-index pipeline
Incremental updates, soft deletion, and zero-downtime re-indexing for when the embedding model or chunking changes.
Monitoring and capacity planning
Query latency percentiles, recall sampling, index size, and cost tracking with projections against growth.
Migration execution
Where a change is warranted, we plan and run the migration with dual-write and verification rather than a cutover.
Which AI models and tools do we use for vector database?
We are not tied to one vendor. Instead, accuracy, cost per task, latency and where your data may live drive every model and infrastructure choice.
How we deliver vector database, step by step
Vector database work follows five set stages, so you always know what comes next and what it delivers.
Discovery
First, we measure corpus size, growth, query volume, latency budget, filter complexity and whether data can leave your infrastructure.
AI Strategy
Next, we benchmark one or two candidate databases on your own data and queries, and model cost at projected scale.
Pilot Build
Then we build a fixed-scope pilot with tuned index settings, so you see real recall and latency before committing.
Implementation
After that, we integrate the database with your live systems and set up updates, re-indexing and any dual-write migration.
Optimization
Finally, we track latency percentiles, recall sampling and cost against growth, and report findings to you in writing.
How much does a vector database project cost?
Vector database solutions are priced on the data volume, the query load and the systems we integrate. You get a fixed written price before we start, with the database chosen on a test using your own data.
Book a free 30-minute call and tell us how much content you need to search. We will recommend an option and show you the expected running cost.
- A written savings estimate before any paid work
- Fixed scope and fixed price, agreed up front
- Full ownership of everything we build for you
- An honest recommendation when the numbers do not work
Figures are internal measurements across recent engagements, reported to every client monthly in writing.
Vector database FAQs
Do we actually need a dedicated vector database?
Often not, and this is the most common finding in our selection engagements. If you are under roughly five million vectors and already run Postgres, pgvector will usually serve you well at essentially no additional infrastructure cost or operational burden. However, dedicated vector databases earn their keep at larger scale, with demanding latency requirements, or with complex metadata filtering.
Which vector database is best?
There is no general answer, and any article claiming one is selling something. The decision is driven by corpus size, growth rate, query volume, latency budget, filter complexity, data residency rules and how much operational capacity your team has. We benchmark candidate vector database solutions against your actual data and queries rather than relying on published benchmarks, which almost never resemble real workloads.
Our vector search has become slow. What usually causes that?
In order of frequency: index parameters left at defaults that suited a small prototype, metadata filtering applied after retrieval instead of before, index type mismatched to the corpus size, and memory pressure causing the index to spill to disk. However, adding hardware rarely solves it, even though it is usually the first thing teams try.
What happens when we want to change embedding models?
Every vector needs regenerating and re-indexing, which is why we build the re-embedding pipeline during the initial engagement rather than after. Done properly it is a planned dual-index operation with verification and no downtime. Done unplanned, however, it is a weekend outage, which is how many teams first discover the problem.
Can you keep everything inside our own infrastructure?
Yes. Self-hosted Qdrant, Weaviate, Elasticsearch or pgvector all run entirely within your own cloud account or data center, with no data leaving your boundary. Self-hosted vector database solutions are the standard approach for clients with HIPAA, federal contracting or strict data residency requirements.
How much does this cost?
It depends on data volume, query load and integrations. We test options on your data first, then give you a fixed written price. Book a free 30-minute consultation to scope it.
Ready to scope your vector database solutions project?
Book a free 30-minute consultation. Bring one workflow. You will leave with a realistic estimate of what it would cost to automate and what it would save.