SharpVector is live · free · last verified 22 Oct 2026 · viability monitor
SharpVector
Open-source, in-memory text vector database for .NET apps that adds semantic search and RAG without provisioning a vector server.
Choose Build5Nines.SharpVector when you want semantic search inside one .NET process and don't want to run a vector server. The built-in local text vectorizer removes external LLM dependencies entirely, and you can swap in OpenAI, Azure OpenAI (text-embedding-ada-002) or Ollama (nomic-embed-text) embeddings when quality demands it. File persistence, stream persistence and BasicDiskVectorDatabase cover keeping data between runs. It is a library, not a distributed database — the project page itself points large workloads at Azure Cosmos DB, PostgreSQL with pgvector, Azure AI Search and Elasticsearch. If you need multi-node scale, or you're not on .NET, take one of those instead.
Verdict generatedVerified 8d ago · liveness 44/100 · cite: rightaichoice.com/tools/sharpvector
- .NET developers who want embedded semantic search
- Prototyping RAG pipelines in C#
- Desktop and server apps needing local search
- Offline and edge computing scenarios
- Workloads with millions of vectors needing multi-node scale
- Teams that want a managed cloud vector database
- Non-.NET stacks (the library is C#)
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Skip SharpVector if your corpus has to scale to millions of vectors across multiple nodes, or if your stack isn't .NET — it's a single-process C# library, and the project page itself points those workloads to Azure AI Search, Cosmos DB or Elasticsearch.
SharpVector is free and open source, so cost isn't the comparison axis — infrastructure is. It undercuts Azure AI Search, Azure Cosmos DB, PostgreSQL with pgvector and Elasticsearch because there is no server to provision or pay for, but that saving is bought with a memory-bound, single-process ceiling.
In short
SharpVector — Open-source, in-memory text vector database for .NET apps that adds semantic search and RAG without provisioning a vector server. Best for .NET developers who want embedded semantic search, Prototyping RAG pipelines in C#, Desktop and server apps needing local search. Free to use.
What is SharpVector?
SharpVector — Open-source, in-memory text vector database for .NET apps that adds semantic search and RAG without provisioning a vector server. Best for .NET developers who want embedded semantic search, Prototyping RAG pipelines in C#, Desktop and server apps needing local search. Free to use.
Build5Nines.SharpVector is an open-source, in-memory text vector database library for .NET. You add text to a BasicMemoryVectorDatabase, and the library vectorizes it, stores it with whatever custom metadata you attach, and lets you search it by vector similarity. Cosine similarity is the default comparison, Euclidean distance is configurable, and you can supply your own comparison algorithm. Embeddings come from a built-in local text vectorizer that needs no external LLM server, or you can plug in OpenAI/Azure OpenAI embedding models such as text-embedding-ada-002, or Ollama models such as nomic-embed-text, when you want higher-quality vectors. Persistence is handled three ways: file persistence, persisting to a stream, and a BasicDiskVectorDatabase that keeps data on disk between runs. The library also ships text chunking utilities, async/await methods for non-blocking operations, and console-app plus Generative AI + RAG + ONNX samples. It is aimed at .NET developers who want semantic search, text-similarity recommendations, or a RAG prototype inside a single process — including offline and edge scenarios — rather than standing up Azure Cosmos DB, PostgreSQL with pgvector, Azure AI Search, or Elasticsearch. Microsoft's Tulika Chaudharie, Principal Product Manager for Azure App Service, is quoted on the project page calling it an ideal choice for their sample RAG implementation.
Key features
- In-memory vector storage via BasicMemoryVectorDatabase
- Semantic search with cosine similarity as the default
- Configurable Euclidean distance comparison
- Custom vector comparison algorithms you can write yourself
- Built-in local text vectorizer with no external LLM server
- Pluggable embeddings via OpenAI and Azure OpenAI (e.g. text-embedding-ada-002)
- Pluggable embeddings via Ollama models (e.g. nomic-embed-text)
- Custom metadata stored alongside each text entry
- Async/await methods for non-blocking database operations
- File persistence
- Persist to stream
- BasicDiskVectorDatabase for on-disk storage between runs
- Text chunking utilities
- Generative AI + RAG + ONNX sample
- Console app sample
Our long-form take written Jul 22, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-10-22
Limitations
- SharpVector is an in-memory, single-process library, so its upper bound is the application's memory.
- Persistence is available through file storage, persistence to a stream, and BasicDiskVectorDatabase, but the vendor describes it as an embeddable library rather than a distributed or multi-node database — the project page itself steers large workloads to Azure Cosmos DB, PostgreSQL with pgvector, Azure AI Search, and Elasticsearch.
- It is C#/.NET only, so it does not serve applications on other stacks.
as of 2026-10-11
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Is SharpVector still active?
SharpVector is live · free · last verified 22 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is SharpVector? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about SharpVector?
What people actually say about SharpVector — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about SharpVector— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 22 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified SharpVector 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed SSL certificate
- — changed our verdict, pricing fit, who should skip it, switching in, summary, switching out, hidden costs, features, pricing tiers, description, setup time
- — changed pricing tiers
- — changed who should skip it, setup time, description, use cases, our verdict, summary, features, hidden costs, who it suits, integrations, limitations, switching in, switching out, pricing fit
- — changed limitations
- — changed domain expiry, SSL certificate
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does SharpVector cost in 2026?
SharpVector: Free to use. Pricing last verified 22 Oct 2026.
Plans compared
Every published SharpVector tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open source | Free | — | Individual .NET developers and teams who want embedded semantic search without any licence cost — the whole library is free to use in commercial and personal apps.Adds: Starting tier — there is no paid edition; the project is free and open source under the licence published on its License page. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where SharpVector's pricing actually pencils out — and where peers do it cheaper.
SharpVector is free and open source, so cost isn't the comparison axis — infrastructure is. It undercuts Azure AI Search, Azure Cosmos DB, PostgreSQL with pgvector and Elasticsearch because there is no server to provision or pay for, but that saving is bought with a memory-bound, single-process ceiling.
Has SharpVector's pricing changed?
No plan price for SharpVector has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick SharpVector?
Pick SharpVector if you are .NET developers who want embedded semantic search, Prototyping RAG pipelines in C#, Desktop and server apps needing local search, Offline and edge computing scenarios.
Look elsewhere if you are Workloads with millions of vectors needing multi-node scale, Teams that want a managed cloud vector database, Non-.NET stacks (the library is C#).
Skip it if Skip SharpVector if your corpus has to scale to millions of vectors across multiple nodes, or if your stack isn't .NET — it's a single-process C# library, and the project page itself points those workloads to Azure AI Search, Cosmos DB or Elasticsearch.
GeneratedReal-world workflow fit
Concrete scenarios for the personas SharpVector actually fits — and what changes day-one when you adopt it.
Install the NuGet package, create a BasicMemoryVectorDatabase, loop over your document array calling AddText() with a category string as metadata, then call Search() on a query string and bind the returned texts and metadata to your results list.
Outcome: Working semantic search inside the app the same session, with no server, no API key and no network dependency.
Run the shipped Generative AI + RAG + ONNX sample, index a small document set using the local vectorizer first to validate retrieval quality, then switch the embedding provider to OpenAI text-embedding-ada-002 or Ollama nomic-embed-text and re-index the same corpus.
Outcome: A validated retrieval step you can hand to the LLM prompt, plus a measured read on whether local vectors are good enough before you pay for hosted embeddings.
Back the vector store with BasicDiskVectorDatabase (or file/stream persistence) so indexed notes survive restarts, and run entirely on the local vectorizer because the device has no reliable internet connection.
Outcome: Semantic search that keeps working on a disconnected device and survives application restarts.
Use cases
- Add semantic search over documents or notes to a .NET app without running a vector server.
- Embed similarity search directly inside a desktop or server .NET application.
- Power text-similarity recommendation features entirely in-memory.
- Prototype a RAG pipeline using the shipped ONNX sample before committing to a hosted vector service.
- Run semantic search offline or at the edge where there is limited or no internet connectivity.
- Develop and test vector features locally without installing a database server.
- Chunk and index small-to-medium corpora with custom metadata attached to each entry.
- Swap between the built-in local vectorizer and OpenAI or Ollama embeddings as quality needs change.
Setup time & first value
How long it actually takes to get something useful out of SharpVector — broken out by persona, not the marketing-page minute.
Add the NuGet package and you can have a BasicMemoryVectorDatabase returning similarity results in roughly 15–30 minutes for a small corpus; the bundled console and Generative AI + RAG + ONNX samples cut that to minutes. Plan an extra hour if you're wiring OpenAI/Azure OpenAI or Ollama embeddings and re-indexing.
Switching to or from SharpVector
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Azure AI Search: swap the index call for BasicMemoryVectorDatabase.AddText() and the query call for Search() for corpora that fit in process memory.
- →From PostgreSQL with pgvector: read your rows out, AddText() each one with its columns as metadata, and drop the database connection.
- →From Azure Cosmos DB vector search: export embeddings or raw text, re-add via AddText(), and keep the same query strings against Search().
- →From Elasticsearch: re-index documents through the text chunking utilities and AddText(), then replace the query DSL with a Search() call.
- →From hand-rolled cosine similarity code: replace your own vector math with the built-in cosine, Euclidean or custom comparison implementation.
- ↗To Azure AI Search: export text and metadata from the vector store and push them into a search index once your corpus outgrows process memory.
- ↗To PostgreSQL with pgvector: dump text plus embeddings to rows and move similarity queries into SQL for multi-user, multi-node access.
- ↗To Azure Cosmos DB: migrate to a managed store when you need durability and scale beyond one process.
- ↗To Elasticsearch: move to a distributed index when you need horizontal scale and richer query capabilities.
- ↗To a hosted embeddings provider: keep almost all code and swap the vectorizer for OpenAI text-embedding-ada-002 or Ollama nomic-embed-text.
Resources & guides
- Resourcesharpvector.build5nines.com
Get Started · SharpVector
Helpful link from sharpvector.build5nines.com
- Conceptssharpvector.build5nines.com
Concepts · SharpVector
Core ideas explained from sharpvector.build5nines.com
- Resourcesharpvector.build5nines.com
Text Chunking · SharpVector
Helpful link from sharpvector.build5nines.com
- Resourcesharpvector.build5nines.com
Persistence · SharpVector
Helpful link from sharpvector.build5nines.com
- Resourcesharpvector.build5nines.com
Embeddings · SharpVector
Helpful link from sharpvector.build5nines.com
- Resourcesharpvector.build5nines.com
Samples · SharpVector
Helpful link from sharpvector.build5nines.com
- Resourcesharpvector.build5nines.com
Resources · SharpVector
Helpful link from sharpvector.build5nines.com
Tutorials & Learning
YouTube returned 6 videos for “SharpVector”, and we withheld 6: 6 could not be judged, because “SharpVector” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about SharpVector.
Official links
What are the best SharpVector alternatives?
The closest alternatives to SharpVector we list are Weaviate, Vespa and Milvus. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Weaviate | Freemium | Pricing not published | — |
| Vespa | Freemium | Pricing not published | — |
| Milvus | Freemium | Pricing not published | — |
Alternatives to SharpVector
View all
Tools that pair well with SharpVector
Common stack mates teams adopt alongside SharpVector, with the specific reason each pairing earns its keep.
Weaviate
Weaviate is an open-source AI database for vector search, hybrid search, and RAG with built-in embeddings and agent memory.
Vespa
Vespa is an open-source AI search platform combining vector, text and tensor retrieval with machine-learned ranking in one engine.
Milvus
Open-source vector database for GenAI apps, scaling from a pip install to tens of billions of vectors
SharpVector vs its closest rival
SharpVector's closest head-to-head is with Spider Cloud — If you need to feed your AI agent fresh web data at scale, Spider Cloud is the obvious pick with its Rust-powered API, Silk extraction model, and 99.9% success rate. If you're a .NET developer embedding semantic search into a desktop app without external dependencies, SharpVector's free in-memory library is a perfect fit. The two tools solve completely different problems, so the decision hinges on whether your bottleneck is data access or vector storage. Verdict generated
Head-to-head comparisons
Sharpvector vs Spider Cloud
If you need to feed your AI agent fresh web data at scale, Spider Cloud is the obvious pick with its Rust-powered API, Silk extraction model, and 99.9% success rate. If you're a .NET developer embedding semantic search into a desktop app without external dependencies, SharpVector's free in-memory library is a perfect fit. The two tools solve completely different problems, so the decision hinges on whether your bottleneck is data access or vector storage.
GeneratedSharpvector vs Voyage Ai
Choose Voyage AI if you need high-accuracy, domain-specific embedding models (finance, legal) with long context and enterprise compliance. Choose SharpVector if you are a .NET developer prototyping or building a small-scale app that needs an embedded vector store with zero cost and minimal dependencies.
GeneratedSharpvector vs Temporal Ai
For teams building production-grade AI agents or multi-step workflows that require fault tolerance and state persistence, Temporal AI is the clear choice. If you're a .NET developer needing a simple, embedded vector search for prototyping or edge apps, SharpVector offers a lightweight, no-infrastructure alternative. They solve entirely different problems — pick based on your orchestration vs. semantic search need.
GeneratedFrequently asked questions
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