ESEILANE
AI-native graph database running vector similarity and OpenCypher traversal in one query for GraphRAG pipelines.
If your retrieval eval is failing on multi-hop questions, the single-query vector-plus-traversal model is the right fix, and ESEILANE is one of the few engines designed around it rather than retrofitted. The published tiers open at $0/month for 1 graph and 10,000 nodes and $49/month for 100 graphs with 10M nodes per graph, HA replication and daily backups — cheap enough to prototype against real data without a procurement fight. What we would watch is maturity. The headline latency numbers are vendor-published, the site runs both a pricing table and an early-access waitlist promising founding-member pricing, and there are no independent benchmarks to check sub-10ms P99 against Neo4j or
Verified 6h ago · liveness 50/100 · cite: rightaichoice.com/tools/eseilane
- AI engineers building GraphRAG pipelines who need vector similarity and traversal in one query
- Platform teams running multi-tenant SaaS that needs isolated per-customer knowledge graphs
- Fraud detection and recommendation systems where relationship topology drives the answer
- Engineering teams who want a $0/month tier before committing to a graph database
- Teams with no graph modeling experience — OpenCypher is a real learning curve before you get value
- Transactional OLTP workloads that need row-store guarantees rather than graph traversal
- Anyone who needs a hiring pool and third-party documentation comparable to Neo4j's decade of ecosystem
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Skip ESEILANE if your retrieval problem is single-hop vector lookup rather than multi-hop reasoning over relationships — a dedicated vector database will be simpler, cheaper and better documented for that job.
The Free tier caps you at 1 graph and 10,000 nodes, which is enough for a prototype and not enough to validate retrieval quality on production-sized data — you will hit Pro before you have a real answer.
The published tiers put ESEILANE at the low end for a graph database with integrated vector indexing: $0/month for 1 graph and 10,000 nodes, then $49/month for 100 graphs, 10M nodes per graph, HA replication and daily backups. That undercuts the cost of running Neo4j plus a separate vector store for the same job, and it is well below enterprise graph platforms that price per node or per core. The catch is scale: once you need VPC peering, SAML SSO or SOC2/HIPAA, you leave the published table
In short
ESEILANE — AI-native graph database running vector similarity and OpenCypher traversal in one query for GraphRAG pipelines. Best for AI engineers building GraphRAG pipelines who need vector similarity and traversal in one query, Platform teams running multi-tenant SaaS that needs isolated per-customer knowledge graphs, Fraud detection and recommendation systems where relationship topology drives the answer. Free to start; paid plans from $49/mo.
What people actually say about ESEILANE — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
1 mentions across 1 source (GitHub) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +Native RDF and SPARQL support for rich knowledge representation.
- +Hybrid vector + graph retrieval enables GraphRAG workflows directly.
- +LLM-agnostic pipeline works with OpenAI, Anthropic, and others.
- +Open-source permissive license allows self-hosting and customization.
- +Multi-tenant and RBAC ready for enterprise deployments.
- −Virtually no real-world user reviews or community discussions exist.
- −Scalability and performance claims lack independent benchmarks.
- −Documentation is thin beyond the GitHub readme.
- −Pricing is opaque; no free tier or transparent plans available.
- −No proven track record of reliability in production environments.
- • Potential infrastructure costs for self-hosting
- • No free tier to evaluate
Viability Score
How well maintained and how widely used is ESEILANE? 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 →Key Features
- Graph + vector hybrid queries in a single OpenCypher statement
- db.idx.vector.queryNodes for vector similarity search inside Cypher
- GraphRAG support with native LangChain and LangGraph connectors
- Sub-10ms P99 latency quoted on graphs with billions of relationships
- 200x speed claim versus traditional graph execution
- Multi-tenant isolation for 10,000+ tenant graphs per instance
- GraphBLAS execution engine
- Compressed sparse matrix storage for nodes and edges
- Integrated full-text search and vector indexing in one database
- OpenCypher query language
- SDKs for Python, TypeScript, Go, Java, and Rust
- GraphRAG SDK 2.0
- HA replication and daily backups on Pro
- VPC peering and SAML SSO on Enterprise
- Role-based access controls across tenant graphs
About ESEILANE
ESEILANE is an AI-native graph database for engineering teams building GraphRAG pipelines, knowledge graphs, and GenAI retrieval systems. Rather than stitching a vector store onto a graph database, it executes vector similarity and graph traversal inside a single OpenCypher query, so a retrieval layer returns both semantic matches and the topology around them. The vendor's homepage shows a Cypher example that matches a vector node, calls db.idx.vector.queryNodes on a 'chunk_idx' index, then traverses BELONGS_TO and CITES edges to return document titles and source URLs in the same statement. The engine compiles graph queries into sparse-matrix operations and runs execution on GraphBLAS, which the site presents as the source of its quoted sub-10ms P99 latency on graphs with billions of relationships and a 200x speed claim. Multi-tenancy is treated as a first-class primitive: the vendor advertises 10,000+ isolated tenant graphs per instance, which suits SaaS products that need per-customer knowledge graphs without separate clusters. SDKs are documented for Python, TypeScript, Go, Java, and Rust, alongside native LangChain and LangGraph connectors, integrated full-text and vector indexing, and Redis, Snowflake, Docker, AWS and GCP integrations. The buyer is a platform or AI engineer already fighting retrieval quality, not a business analyst. The comparison to hold in mind is against general graph databases you would otherwise pair with a separate vector store: Neo4j remains the safer default for tooling and hiring depth, while Milvus and Qdrant win if you only need vectors. ESEILANE competes by removing that glue layer.
Behind the Verdict
ESEILANE's pitch is narrow and defensible: most GraphRAG projects are three systems wired together — a vector database, a graph database, and the application code that reconciles them — and the reconciliation layer is where latency and correctness go to die. Collapsing that into one OpenCypher call that can run db.idx.vector.queryNodes and then traverse CITES and BELONGS_TO edges in the same statement is a real engineering difference, not a marketing one. The GraphBLAS execution model underneath is the other substantive choice: compiling graph traversal into compressed sparse matrix operations is how you get parallel hardware utilization out of the traversal itself, and it is the reason the vendor can quote sub-10ms P99 without it sounding absurd on graphs with billions of relationships. The second differentiator is multi-tenancy. Running 10,000+ isolated tenant graphs on one instance matters if you are a SaaS company whose customers each want their own knowledge graph — the alternative is either noisy-neighbour chaos in one shared graph or a cluster per customer, and both are expensive. Role-based access controls across tenant graphs plus HA replication on Pro plus VPC peering and SAML SSO on Enterprise describe a platform that has thought about the operational shape, not just the query engine. Where we are cautious. First, the performance claims are vendor-published: 200x faster and sub-10ms P99 need to be reproduced against your own graph shape, because latency on sparse graphs and latency on dense ones are different problems. Second, the homepage runs an early-access waitlist offering founding-member pricing alongside a live three-tier pricing table — that is unusual, and it means you should confirm what a production commitment actually looks like commercially before you plan a budget around $49/month. Third, ecosystem depth is the honest gap: Neo4j has a decade of tooling, Stack Overflow answers and a hiring pool; ESEILANE has SDKs in five languages, LangChain and LangGraph connectors, and a small documentation surface. If your team already knows Cypher you will move quickly; if it does not, budget for the OpenCypher learning curve on top of everything else. The fit is specific. Build it into a GraphRAG pipeline where retrieval quality is the bottleneck; build it into a multi-tenant product where per-customer graph isolation is a requirement; build it into fraud and recommendation systems where the answer depends on relationship topology. Do not pick it for transactional OLTP, where row-store guarantees are what you actually want, and do not pick it if you only need vector search — a dedicated vector database will be simpler for that job.
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Real-world workflow fit
Concrete scenarios for the personas ESEILANE actually fits — and what changes day-one when you adopt it.
You load your document corpus, chunk it, and build entity and citation edges into a graph inside the Free tier's 1-graph, 10,000-node limit. You wire the LangChain connector, then write an OpenCypher query that calls db.idx.vector.queryNodes on a chunk index and traverses CITES and BELONGS_TO edges to return document titles and source URLs alongside the similarity score.
Outcome: You get a retrieval result that carries both semantic match and provenance in one round trip, which lets you measure whether multi-hop questions actually improve over your existing vector-only baseline before spending anything.
You move to Pro at $49/month, provision an isolated graph per customer using the 100-graph allowance, and apply role-based access controls so tenant data does not cross boundaries. HA replication and daily backups run as part of the tier rather than as separate infrastructure you build.
Outcome: Each customer gets a knowledge graph that never shares memory with another tenant, and you avoid the cluster-per-customer cost that the isolation requirement would otherwise force on you.
You port your existing Cypher queries to ESEILANE, since OpenCypher is the common surface, then test the fraud-detection paths that were timing out. Because vector indexing is native, you also fold in the embedding similarity that previously lived in a separate vector store and was joined in application code.
Outcome: You get a single engine answering both the traversal and the similarity half of the query, and you can benchmark throughput against the hardware footprint you were already paying for.
Use Cases
- Ground an LLM in both semantic matches and the topology around them with a single GraphRAG query
- Build a question-answering system over an enterprise knowledge base where answers require multi-hop reasoning
- Run per-customer knowledge graphs for a multi-tenant SaaS product on one instance
- Semantic search over legal documents with entity resolution across citations
- Fraud detection where relationship topology, not individual records, drives the signal
- Product recommendations that combine embedding similarity with graph neighbourhood
- Long-term memory for a conversational agent stored as a knowledge graph
- Knowledge graph construction from unstructured text using LLMs
Models Under the Hood
as of 2026-09-01
Limitations
- The headline numbers — sub-10ms P99 and 200x faster — are vendor-published on the homepage; there are no independent benchmarks to check them against on your own graph shape.
- Documentation and community are thin relative to Neo4j, so expect to lean on the vendor's engineering team rather than Stack Overflow.
- The homepage simultaneously publishes a three-tier pricing table and an early-access waitlist promising founding-member pricing, so confirm what a production commitment actually costs before building a budget around the listed $49/month.
- OpenCypher is a real learning curve for teams without prior Cypher experience.
- The homepage lists GraphRAG SDK 2.0 as available, but the site does not publish a roadmap, so planning around unshipped features is guesswork.
as of 2026-10-08
Verification history
We have re-verified ESEILANE 7 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.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published ESEILANE tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/month
Ideal for
An AI engineer prototyping a GraphRAG query or validating the hybrid vector-plus-traversal model on a small corpus before asking for budget.
What this tier adds
Starting tier: $0/month for 1 graph, 10,000 nodes, OpenCypher access and community support, with standard latency and no HA replication.
Pro
$49/month
Ideal for
A platform team putting a GraphRAG pipeline or multi-tenant knowledge graph into production and needing backups and failover without an enterprise contract.
What this tier adds
Moves from 1 graph to 100 graphs and from 10,000 to 10M nodes per graph, and adds HA replication, vector indexing, daily backups and email support for $49/month.
Enterprise
Custom
Ideal for
A regulated or security-reviewed organisation that needs tenant isolation at infrastructure level, SSO and a signed compliance posture before it can ship.
What this tier adds
Adds unlimited graphs, dedicated clusters, 24/7 SLA support, VPC peering, SAML SSO and SOC2/HIPAA compliance on a custom quote.
Where the pricing makes sense
The company stage and team size where ESEILANE's pricing actually pencils out — and where peers do it cheaper.
The published tiers put ESEILANE at the low end for a graph database with integrated vector indexing: $0/month for 1 graph and 10,000 nodes, then $49/month for 100 graphs, 10M nodes per graph, HA replication and daily backups. That undercuts the cost of running Neo4j plus a separate vector store for the same job, and it is well below enterprise graph platforms that price per node or per core. The catch is scale: once you need VPC peering, SAML SSO or SOC2/HIPAA, you leave the published table
Setup time & first value
How long it actually takes to get something useful out of ESEILANE — broken out by persona, not the marketing-page minute.
For an AI engineer already fluent in Cypher, first query against real data is a short session: pick a Python or TypeScript SDK, load nodes and edges, build the vector index, and run a hybrid query — the LangChain and LangGraph connectors remove most of the orchestration work. Teams learning OpenCypher from scratch should add a few days of graph modeling before they get a query that reflects their
Switching to or from ESEILANE
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Neo4j: port Cypher queries largely as written, then fold your separate vector store into db.idx.vector.queryNodes so similarity and traversal share one statement.
- →From a separate vector database plus application-side graph joins: move chunk embeddings into a native vector index and replace the join logic with OpenCypher traversal.
- →From a hand-rolled GraphRAG pipeline: adopt the LangChain or LangGraph connector and consolidate the retrieval step into the database.
- →From a Postgres-plus-pgvector retrieval layer: remodel entities and edges as a graph and re-express the joins as Cypher traversal.
- ↗To Neo4j: export nodes and edges and re-import using its bulk loader, then re-add a separate vector store for the similarity half of your queries.
- ↗To a dedicated vector database: keep only the embedding half of your pipeline and rebuild the graph traversal in application code.
- ↗To PostgreSQL with pgvector: flatten graph entities into relational tables and express traversal as recursive CTEs, accepting the performance hit on deep paths.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “ESEILANE”, and we withheld 6: 6 could not be judged, because “ESEILANE” 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 ESEILANE.
Official links
Tools that pair well with ESEILANE
Common stack mates teams adopt alongside ESEILANE, with the specific reason each pairing earns its keep.
Milvus
Open-source vector database for billion-scale similarity search, hybrid retrieval, and RAG
Tidb
Open-source distributed SQL database unifying transactions, HTAP analytics, and native vector search for AI agents.
Pinecone
Fully managed vector database and knowledge platform for AI retrieval, with Pinecone Nexus compiling enterprise data into governed knowledge for agent queries.
Featured Head-to-Head Comparisons
Eseilane vs Spider Cloud
For AI agents needing real-time web data, Spider Cloud is the clear choice with its low-cost, high-speed scraping and latest Browser AI commands. ESEILANE is better suited for teams building knowledge-graph-driven GraphRAG applications from structured data. Choose based on whether your bottleneck is ingesting unstructured web content or reasoning over structured relationships.
Eseilane vs Temporal Ai
Temporal AI and ESEILANE solve different problems. If you need fault-tolerant orchestration for AI agents or microservices, with automatic retries and human-in-the-loop, choose Temporal AI. If your primary challenge is blending vector search with knowledge graphs for GraphRAG, ESEILANE is purpose-built. For most AI engineering teams, the more mature Temporal AI (with a freemium model and recent usage-based billing) is the safer bet unless your core needs are graph-based retrieval.
Eseilane vs Screenplayiq
ScreenplayIQ and ESEILANE serve completely different domains. Choose ScreenplayIQ if you're a screenwriter or producer seeking data-driven script feedback and financial projections; its free tier and affordable Pro plan make it accessible. Choose ESEILANE if you're an AI developer building knowledge-graph-powered applications that need hybrid retrieval and LLM integration. There is no overlap in use cases.
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