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Essential Questions AI Product Managers Should Ask Before Choosing a Graph Vector Database Vendor

Essential Questions AI Product Managers Should Ask Before Choosing a Graph Vector Database Vendor

Founders HelixDB

Founders HelixDB

GraphRAGproduct-managementGraph DatabaseVector DatabaseVendor SelectionDecision Making

A smart database choice can accelerate your AI roadmap, improve relevance, and cut stack complexity. Use these questions to evaluate vendors confidently and see where HelixDB fits.

At a glance: what to compare

Decision FactorWhat to Look ForHow HelixDB Approaches It
Time-to-valueSimple setup, fast integration with your LLM stackUnified graph plus vector engine with SDKs for Python, TypeScript, Go, Rust, and CLI tools
Relevance qualityHybrid retrieval combining vectors, metadata, and relationshipsNative vector search plus graph traversal for higher precision and recall
Performance at scaleLow-latency queries under real workloadsRust-fast engine with founder-reported ~2 ms vector queries and sub ms graph hops
Developer experienceFriendly APIs, type-safe queries, clear documentationHelixQL with type safety and a developer-first design
Pricing clarityPredictable, startup friendly pricingOpen source core with optional managed Helix Cloud
Security and privacyPrivate by default deployments, isolation, data portabilityPrivate VPC clusters in Helix Cloud plus an open source escape hatch
Future-proofingAgent native workflows and transparent roadmapAgent native integrations such as HelixMCP and an active community

Resources

The 7 Essential Questions

Q: How quickly can my team get to a meaningful prototype?

A: You should be able to validate retrieval quality on your own data within days, not months. The right vendor offers quickstart docs, SDKs in your team's languages (Python, TypeScript, Go, Rust), and a clear POC plan. If setup takes weeks or requires extensive vendor support just to get started, that's a red flag.

Q: Will relevance hold up in production?

A: This depends on how the vendor combines vector similarity with graph relationships and metadata filters. Pure vector search often misses context that graph traversal can capture. Hybrid retrieval (blending vectors, relationships, and filters) typically delivers better precision and recall. Ask for examples of how they handle complex queries requiring both semantic similarity and relationship-aware traversal.

Q: Can we scale without re-architecting?

A: You need concrete numbers: latency under realistic concurrency, throughput benchmarks, and a scale-up plan that doesn't require redesigning your system mid-launch. Ask for benchmarks relevant to your expected load, not vague claims. If they can’t provide latency guarantees or performance data, you're flying blind.

Q: How developer-friendly is the experience?

A: Developer experience directly impacts shipping velocity. Look for clean APIs, type-safe queries, strong documentation, and consistency across SDKs. The query language should support complex retrieval patterns without causing friction. If your team spends more time fighting the tooling than building product, that’s a problem.

Q: What's the pricing model and what does it include?

A: Pricing should be transparent and predictable across storage, queries, and support. Ideally, you can begin with open-source, verify the fit, and then transition to a managed service without early vendor lock-in. Watch out for opaque or unpredictable pricing structures.

Q: How do you handle security and privacy by design?

A: Expect private-by-default deployments, VPC isolation, and clear data handling policies. Also confirm data portability, as you should be able to export your data easily or self-host using open-source options. Avoid vendors that make it difficult to leave.

Q: What's on the roadmap for agents and advanced RAG?

A: Modern AI systems rely on agent workflows: graph-based memory, iterative reasoning, multi-step retrieval. Ask what the vendor supports today and what's planned. A database that is agent-native reduces complexity and results in more powerful RAG pipelines.

Ready to evaluate?