AI search that performs more
and costs less.
Your AI search costs more to run than it needs to, and switching to a better model means days of downtime. GraphANN™ eliminates both problems.
Drop it in as a direct replacement: up to 95% less storage, exact-quality results, and the freedom to switch to a better embedding model the moment one is released. No re-indexing. No reason to stay with infrastructure that costs more than it should.
Every time your document library grows, your vector database grows faster.
Every time a better model is released, switching means re-indexing everything.
The infrastructure that was supposed to power your AI ends up slowing it down and costing more than it should.
GraphANN™ was built to fix both of those problems at once.
Built for teams who need AI to pay off.
If your AI infrastructure costs are eating your margins
Your gross margin improves as you scale. GraphANN™ eliminates the storage overhead that makes AI unit economics hard. Your costs stay flat as you scale.
If switching embedding models feels like a week-long project
Adopt a better model the day it's released. GraphANN™ requires no re-indexing and no infrastructure freeze.
If your data can't leave your infrastructure
Your data stays inside your infrastructure. GraphANN™ runs entirely on-prem with zero outbound calls. One binary. Full compliance from day one.
Teams replacing traditional vector databases save between $15,379 and $126,695 per year on infrastructure costs alone, on a 50 GB document library.
See where your stack falls →The same search quality. Without the storage bill.
The accuracy stays high. The bill stays flat.
Finds the answer without reading everything
GraphANN™ navigates directly to the documents most likely to match your query, skipping everything else. Fast, targeted retrieval without the compute cost of scanning your entire knowledge base.
Nothing stored. Nothing wasted.
Instead of storing vectors for every document, GraphANN™ recomputes them on the fly, only for the small fraction of documents it visits. Nothing is persisted. Storage stays minimal.
Exact-quality results, without storing vectors
GraphANN™ narrows candidates in multiple passes, then reranks the shortlist exactly, delivering the same results a full-vector database would, without persisting a single vector on disk. Measured recall: 0.994 in our July 2026 benchmark.
| Capability | Traditional vector DB | GraphANN™ |
|---|---|---|
| Storage | Full vectors on disk | Compressed text only |
| Model switching | Re-embed entire knowledge base | Swap model, search immediately |
| Incremental ingestion | Rebuild index | Live within seconds |
| Deployment | Separate vector store | Single binary |
| Scaling | Storage scales with knowledge base | Storage stays flat |
| Data sovereignty | Cloud dependency | On-prem, air-gapped ready |
A better AI model is here. Switch in seconds.
With a traditional vector database, switching to a better AI model means re-embedding your entire knowledge base from scratch. For large document libraries, that's days of compute, significant cost, and a production freeze your team has to plan around.
GraphANN™ stores your documents, not your vectors. So when a better model is released, or when you decide your current model isn't good enough, you swap it out and search immediately. No migration scripts. No re-indexing. No reason to stay locked in.
Your document library grows. Your infrastructure bill doesn't have to.
A traditional vector database keeps a full embedding vector for every chunk, 1,536 bytes each at 384 dimensions, plus its own index on top. In our benchmark a 50 GB corpus balloons to roughly 320 GB on disk in Qdrant, and over 1 TB in Neo4j.
GraphANN™ stores no embedding vectors at all. It recomputes them on demand for the handful of chunks each query visits, keeping only a compressed copy of your text and a navigation graph. The same 50 GB corpus lands around 70 GB, roughly 4.7× smaller than Qdrant and around 13.7× smaller than Neo4j.
- Raw text: 50 GB
- Embedding vectors + HNSW index: ~270 GB
- Neo4j is larger still, over 1 TB
- Navigation graph + codes: ~54 GB
- Compressed source text: ~17 GB (zstd)
- Zero stored embedding vectors
No embedding vectors on disk, so your index grows with your text, not several times faster than it.
Measured July 2026 on the bundled bge-small model (384-dim) over the default ~512-char (~0.5 KB) chunks, so 50 GB of text is roughly 100 M chunks. Per-engine footprints come from a 250K-vector head-to-head (GraphANN index 189 MB vs Qdrant 884 MB vs Neo4j 2,585 MB), projected linearly; GraphANN totals add source text compressed at a conservative ~3× (zstd) and include a rerank sidecar, so they are an upper bound. Storage scales with chunk count and compressibility. A larger configured chunk size means fewer vectors. Recall and latency are measured.
Everything a production AI team needs. Nothing they don't.
Every capability built around one principle: AI infrastructure that stays out of the way.
Up to 95% less embedding and index storage than a traditional vector database
GraphANN™ computes embeddings live for every document it visits. None are persisted. That saves up to 95% of the embedding and index footprint. Total on-disk, you're around 5× smaller than Qdrant.
costOne binary. Running in minutes.
No system packages, no model downloads, no external dependencies. Drop the binary on any machine and it works.
simplicityYour data never leaves your infrastructure
Run entirely on-prem with zero outbound calls. Built for teams in regulated industries, or anyone who needs their data to stay within their own infrastructure.
securityProduction reliability. No compromises.
Strongly consistent writes across nodes, automatic shard rebalancing, and gossip-based membership. Production-grade reliability from day one.
reliabilityExact-quality results on a compressed index
GraphANN™ narrows candidates across multiple passes, then ranks the shortlist exactly. 0.994 recall in our benchmark, without storing full vectors.
accuracyOne Deployment. Separate Data.
Native tenant isolation with no custom engineering. Per-tenant private indexes, shared org-level indexes, or both in a single query.
tenancyOne Deployment. Separate Data.
If you're building a multi-tenant SaaS product, or managing knowledge across teams in a large organisation, GraphANN™ handles isolation natively. No custom engineering required.
Private indexes per customer. Shared organisation-wide indexes. Or both in a single query. Access control and rate limits scoped to any level of your tenant hierarchy.
Fine-grained access control
Every query runs in a tenant context. Tenants can access their private indexes, shared org-level indexes, or both in a single search.
Useful for SaaS platforms that need strict per-customer isolation, or enterprise deployments where teams share a knowledge base but own their own data.
From your laptop to a global cluster. Same binary.
Most vector databases need a separate infrastructure stack, specialist knowledge, and a list of dependencies to manage. GraphANN™ is a single self-contained binary.
Drop it on any machine and it runs, from a local prototype to a distributed HA cluster.
# Self-contained binary — no dependencies
./graphann serve \
--embedding-server local_onnx \
--port 38888
# Embedded model, no Ollama required
# API ready at:
→ http://localhost:38888
Zero-dependency binary
The binary embeds the inference runtime and a state-of-the-art embedding model. On first run it self-extracts to ~/.cache/graphann. Drop it anywhere and run.
Your AI investment should return
more than it costs to run.
GraphANN™ is in private beta. Request access and our team will be in touch, typically within one business day.