AI Agent Knowledge Graph / Ontology Cost Calculator
Estimate the cost to build, store, embed, and query a knowledge graph or ontology for AI agent memory.
Graph scope
Prices are directional USD estimates. Last updated: 2026-07-07. See notes.
Estimated total 12-month cost
Enter scope to see cost.
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Initial build
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Monthly steady-state
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Storage / queries
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Human review
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Backend comparison (monthly steady-state)
| Backend | Base | Entities | Queries | Writes | Total / mo |
|---|---|---|---|---|---|
| Neo4j AuraDB | — | — | — | — | — |
| Amazon Neptune Serverless | — | — | — | — | — |
| Azure Digital Twins | — | — | — | — | — |
| Oxigraph / RDFlib self-hosted | — | — | — | — | — |
| Memgraph Cloud | — | — | — | — | — |
| TypeDB Cloud | — | — | — | — | — |
| ArangoDB Oasis | — | — | — | — | — |
| PostgreSQL + pgvector (self-hosted) | — | — | — | — | — |
Graph strategy
Enter a scope to see a strategy verdict.
Frequently asked questions
What is a knowledge graph for an AI agent?
A knowledge graph stores entities (people, products, concepts) and their relationships as structured nodes and edges. Agents use it for grounded retrieval, reasoning, and long-term memory beyond vector-only RAG.
When should I use a graph DB instead of plain vector RAG?
Use a graph when answers depend on multi-hop relationships, precise lineage, or schema constraints. Vector RAG is cheaper and simpler for semantic similarity search over unstructured text.
What drives the biggest knowledge-graph costs?
Entity extraction via an LLM usually dominates the one-time build cost. At scale, monthly storage base fees and high query volumes can exceed extraction costs within a year.
Are self-hosted graph stores always cheaper?
Not always. Self-hosted Oxigraph or PostgreSQL avoid per-entity/per-query metered pricing, but you pay VM, backup, and operational labor. Managed options win at small scale and for teams without a DBA.
How often should I refresh the graph?
Refresh when source documents or schemas change. Many production agents do a full rebuild quarterly and incremental updates weekly or daily, depending on how fast new entities appear.
Notes
- Entity extraction assumes each entity requires reading one or more source documents and generating structured output.
- Storage costs include a monthly base fee plus metered per-entity and per-query/write pricing where applicable.
- Embedding costs cover one embedding per entity; relation embeddings are not included unless you treat relations as separate entities.
- Human review cost assumes a fixed number of minutes per entity reviewed at the specified hourly rate.
- Self-hosted backends use a flat VM/hosting estimate; adjust base fee if your cluster size differs.