Summary
- A graph database vendor evaluation starts with identifying the problem to be solved, then examines data modeling, query performance, scalability, operations, and total cost of ownership (TCO).
- A structured RFP asks every vendor the same specific, verifiable questions about architecture, performance, and operations, so answers can be compared side by side instead of judged on broad claims such as “highly scalable.”
- Performance should be tested on your workload: dataset size and relationship density, multi-hop query depth, concurrency, data loading, and updates while queries run. Standardized third-party benchmarks provide a useful baseline but do not replace that testing.
- A proof of concept (PoC) should answer specific questions in four phases (scope, model, test, evaluate), and it should be willing to conclude that graph is not the right fit.
- Due diligence on security, support, commercial terms, and exit options matters as much as technical performance, and AI readiness should be judged separately for the database and the surrounding application stack.
Choosing a graph database is a long-term architectural decision. The right platform can help organizations uncover relationships across complex datasets, support real-time applications, and build a foundation for knowledge graphs and AI. The wrong choice can limit multi-hop query performance, increase operational costs, and create architectural constraints that become expensive to reverse as data volumes and application demands grow.
With a structured vendor evaluation, teams can move beyond product demonstrations and benchmark claims to assess how a database performs against real business and technical requirements.
The most useful evaluation starts with the problem to be solved, then examines data modeling, query performance, scalability, operations, and total cost of ownership (TCO). Whether you are drafting an RFP or running a proof of concept (PoC), the goal is to establish measurable criteria that every vendor must meet.
You’ll learn:
- How to define requirements and evaluation criteria before comparing any vendor
- Which RFP questions produce specific, comparable answers on architecture, performance, and operations
- How to test performance, scalability, and AI readiness with a realistic proof of concept
- Which due diligence checks matter beyond the technology
Define Your Requirements Before Comparing Vendors
Before evaluating products, define why you need a graph database and what success looks like. Graph databases are particularly useful when applications need to explore relationships across entities, follow multi-hop connections, or analyze interconnected data. But if the workload primarily involves retrieving individual records without exploring relationships, another database technology may be a better fit.
Start by answering these questions:
- What business problem are we trying to solve?
- Which decisions or processes should improve?
- What data sources must be connected?
- Are queries primarily transactional, analytical, or a combination?
- What are the expected data volume, growth rate, query latency, and concurrency?
- What existing infrastructure and technical skills must the solution accommodate?
For example, a fraud detection application may need to identify suspicious connections across accounts and transactions in real time. A knowledge graph supporting enterprise search may prioritize data integration, relationship exploration, and explainable retrieval.
These workloads can have different architectural requirements, so a single generic benchmark is rarely sufficient.
Which Graph Database Evaluation Criteria Matter Most?
A useful evaluation framework should cover both technical capabilities and business considerations, as shown in the following table:
| Evaluation area | Questions to ask | Why it matters |
| Data model | How are entities, relationships, and properties represented? | Determines how naturally the database fits the domain |
| Query capabilities | Can it support complex multi-hop queries? | Affects application functionality and query complexity |
| Performance | How does it perform on representative workloads? | Establishes whether service-level targets are achievable |
| Scalability | How does performance change as data and concurrency grow? | Reduces the risk of costly redesign |
| Availability | What failover, backup, and recovery mechanisms are supported? | Helps protect business continuity |
| Integration | Which connectors, APIs, and data-loading tools are available? | Influences the implementation effort |
| Security | How are access control, encryption, and auditing handled? | Supports governance and compliance |
| Developer experience | How mature are documentation, tooling, and language support? | Affects productivity and maintenance |
| Commercial model | How are licenses, infrastructure, and support priced? | Helps establish the total cost of ownership |
[Design cue: Table]
What Should You Ask Graph Database Vendors?
A graph database RFP should request specific, verifiable answers rather than broad claims such as “highly scalable” or “enterprise-ready.” The following RFP questions can help standardize responses across shortlisted vendors:
Architecture and Data Modeling
- How does the platform represent and store entities, relationships, and properties?
- Which graph models and query languages does it support?
- How does it handle data model evolution and changing relationships?
- Can it represent relationships with their own attributes?
- What mechanisms are available for loading, updating, and deleting graph data?
- How does the platform support data integration across multiple source systems?
Beyond raw performance, the underlying architecture determines how easily a graph database fits into your existing technology stack. TigerGraph is an enterprise platform for real-time relationship intelligence, built as a distributed graph database with built-in vector search, enabling multi-hop relationship analysis and semantic similarity within the same platform Its cloud service separates storage from compute, allowing organizations to scale each independently as workload demands change. . Its Graph Data Science Library includes more than 50 algorithms that run in the database, supporting graph analytics and g AI workflows without requiring a separate graph-processing engine.
Performance and Scalability
- What are the expected query latency and throughput for our target workload?
- How does performance change as graph size and relationship density increase?
- How does the platform handle concurrent reads and writes?
- What are the limitations of distributed queries and graph partitioning?
- Which tuning practices are required to meet performance targets?
Performance benchmarks should reflect real-world graph workloads and not just simple queries against small datasets. Require vendors to demonstrate multi-hop query latency, p95/p99 response times, throughput under concurrent reads and writes, and performance as relationship density and data volume increase. A database that performs well in an isolated benchmark may behave very differently under production conditions. TigerGraph publishes benchmark reports covering query response, loading, storage requirements, graph algorithms, and distributed scaling. The next section explains how to check results like these against independent, third-party tests.
Operations and Resilience
- What backup, restore, replication, and disaster recovery capabilities are available?
- How are upgrades and data model changes managed?
- What monitoring and diagnostic tools are included?
- How does the system recover from node or infrastructure failures?
- What operational skills and staffing levels are required?
TigerGraph is available as a managed cloud service, TigerGraph Savanna, and as self-managed deployments. That lets you test your availability, recovery, and staffing questions against the operating model your team would actually use.
How Should You Evaluate Performance and Scalability?
Vendor benchmarks can provide useful starting points, but results depend on dataset characteristics, hardware, configuration, indexing, and query design. A benchmark that does not resemble your workload may tell you little about production performance.
Standardized benchmarks give you a common baseline. The Graph Data Council (formerly the Linked Data Benchmark Council, or LDBC) publishes respected, independent graph benchmarks, each built for a different kind of workload. Choose the one that best fits your use case:
- Financial crime and risk (FinBench): complex reads across connected data alongside continuous inserts, closest to fraud and anti-money laundering environments.
- Operational applications (Social Network Benchmark Interactive): complex reads combined with continuous updates, relevant for high-throughput applications that must stay current.
- Analytical workloads (Social Network Benchmark Business Intelligence): aggregation-heavy queries that touch large portions of the graph while updates occur.
A consistent test plan should consider:
| Evaluation area | What to measure | What it reveals |
| Data scale | Dataset size, relationship density, and projected growth | Scalability and capacity limits |
| Initial data load | Time, throughput, resource use | Ingestion efficiency |
| Representative multi-hop queries | Latency and correctness | Query performance |
| Concurrent workload | Throughput and p95/p99 latency | Behavior under production-like load |
| Failure recovery | Recovery time and data integrity | Operational resilience |
| Data updates | Freshness and query consistency | Suitability for changing data |
[Design cue: Table]
Use the same data, query definitions, hardware assumptions, and success criteria for each vendor wherever possible. Document configuration changes and allow vendors to explain legitimate optimization opportunities.
Conduct a Proof of Concept Before Committing
A proof of concept should answer specific questions that cannot be resolved through documentation alone. It should not become an open-ended exercise in building an impressive demo.
A practical PoC can be organized into four phases:
- Scope: Agree on the business problem, target queries, data sources, and measurable success criteria.
- Model: Design the graph using input from both technical teams and domain experts.
- Test: Load representative data and run agreed queries under realistic conditions.
- Evaluate: Document results, limitations, implementation effort, and projected operational costs.
Include developers, architects, operations staff, security stakeholders, and business owners. Each group evaluates different aspects of the solution.
A useful PoC should also identify when graph technology is not the best fit. This makes the result more valuable than a demonstration designed only to confirm an initial preference.
If you already run a graph database in a pilot, add one more question: can the platform carry the workload from pilot to production? Pilots that perform well at small scale can struggle once data volume, concurrency, and update rates reach production levels, so test those conditions rather than the pilot’s. TigerGraph is built for organizations making that move from pilot projects to production decision intelligence.
Evaluate AI Readiness and Knowledge Graph Capabilities
As organizations explore generative AI, graph databases increasingly serve as infrastructure for knowledge graphs and graph-enhanced retrieval-augmented generation (GraphRAG). However, the evaluation should distinguish between database capabilities and the capabilities of the surrounding application stack.
Ask vendors:
- How can the platform store and query knowledge graphs?
- What integrations are available for LLMs, embedding models, and orchestration frameworks?
- Can retrieval combine semantic similarity with multi-hop graph queries?
- How can answers be linked to source records and supporting relationships?
- How are graph updates, data quality, and access permissions managed?
- What additional components are required to build and operate the complete solution?
For enterprise AI, the critical question is whether the platform can retrieve relevant information and preserve the relationships needed to support an answer. Evaluate hybrid retrieval that combines vector similarity with multi-hop graph traversal, the ability to trace answers to supporting entities and source records, and how access controls are enforced across retrieval. These capabilities determine whether a graph database can support grounded, auditable AI applications and not simply store a knowledge graph.
Vendor Due Diligence: Beyond the Technology
Technical performance is only one part of a vendor evaluation. Due diligence should also address the supplier’s ability to support the deployment throughout its expected lifecycle, such as:
- Security and compliance: Request relevant certifications, audit documentation, encryption details, and access-control capabilities.
- Deployment options: Confirm support for the required cloud, on-premises, or hybrid environment.
- Support: Clarify support hours, escalation paths, service-level commitments, and upgrade assistance.
- Product roadmap: Understand how the vendor prioritizes maintenance, compatibility, and new capabilities.
- Commercial terms: Review licensing metrics, renewal conditions, support fees, and potential cost changes as usage grows.
- Exit strategy: Assess data export, migration options, and dependencies on proprietary query languages or APIs.
A Vendor Evaluation Is Only as Good as the Workload You Test
Selecting a graph database is a decision about more than query speed or feature count. It involves choosing a data model, an operational architecture, and a vendor relationship that can support evolving business requirements.
A structured RFP, a realistic proof of concept, and consistent evaluation criteria help organizations distinguish demonstrated capabilities from marketing claims. They also make it easier for engineers and business leaders to agree on trade-offs.
The strongest evaluation tests what matters in production: multi-hop query performance, concurrent reads and writes, distributed scalability, operational resilience, and the ability to support connected enterprise AI. TigerGraph is built for these requirements, combining distributed graph processing, native graph analytics, and hybrid retrieval in a single platform.
To determine whether TigerGraph is the right fit for your organization, evaluate it against your own data, performance, and operational requirements. Start with TigerGraph’s free tier to test representative workloads, or request a demo to walk through your evaluation criteria with the TigerGraph team.
FAQs
What should a graph database RFP include?
A graph database RFP should include your business requirements and workload (data volume, query latency, concurrency), specific questions on architecture and data modeling, performance and scalability, and operations and resilience, plus security, support, and commercial terms. Ask for specific, verifiable answers rather than broad claims such as “highly scalable” or “enterprise-ready,” and send identical questions to every vendor so responses can be compared directly.
How do you run a graph database proof of concept?
Run it in four phases: scope the business problem, target queries, data sources, and success criteria; model the graph with technical and domain experts; test with representative data under realistic conditions; and evaluate results, limitations, implementation effort, and projected operating costs. Include developers, architects, operations staff, security stakeholders, and business owners, and use the same data and queries for every vendor.
Which performance metrics matter when evaluating a graph database?
Measure data scale and relationship density, initial data load time and throughput, latency and correctness on representative multi-hop queries, throughput with p95 and p99 latency under concurrent load, query consistency while data is updating, and recovery time after failures. Standardized benchmarks from the Graph Data Council (formerly LDBC) provide a third-party baseline, including TigerGraph’s audited Social Network Benchmark Business Intelligence result, but they supplement testing on your own workload rather than replacing it.
How do you evaluate a graph database for AI and GraphRAG?
Separate what the database provides from what the surrounding application stack must supply. Ask how knowledge graphs are stored and queried, which LLM, embedding, and orchestration integrations exist, whether retrieval can combine semantic similarity with multi-hop graph queries, how answers link back to source records, and what extra components the complete solution needs. TigerGraph combines graph processing, vector search, and hybrid retrieval on one platform for GraphRAG, which reduces the number of separate systems involved.
What vendor due diligence questions apply beyond the technology?
Cover security and compliance (certifications, audit documentation, encryption, access control), deployment options, support hours and escalation paths, product roadmap, commercial terms including renewal conditions and cost changes as usage grows, and exit strategy: data export, migration options, and dependence on proprietary query languages or APIs.
