Startup profile · funding coverage
PuppyGraph funding, valuation and investors
Graph-query engine for live data warehouses without moving data, backed by a $10M November 2026 Series A.
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Funding, valuation & investors
Answer-first snapshotLatest funding
Series A
$10M · November 10, 2026
Latest known valuation
Undisclosed
Series A · November 10, 2026
Total disclosed equity funding
$15M
Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.
Current status
private
series a · Santa Clara, California, USA
Investors in latest funding
Lead: Particle Future
Other: defy.vc
Overview
PuppyGraph provides a virtual graph layer that interprets underlying SQL/stream/object-store data as connected relationships. Teams can issue Gremlin or Cypher traversals against data where it already lives. Developer Edition is free for a single node and two connected data sources; Enterprise pricing is based on server CPU and RAM, not users, queries or stored data, with single sign-on and observability.
Production performance depends on workload and connectivity; vendor benchmarks are not independent guarantees.
Why PuppyGraph is interesting
CEO Weimo Liu announced a $10M Series A led by Particle Future on Nov 10, 2026; total disclosed fundraising is $15M including historical $5M defy.vc-led seed. PuppyGraph lets AI agents and analysts traverse relationships in many live data sources without operating a separate copy of a graph database.
Product & use cases
Illustrative engineering workflow: connect a warehouse or data lake and optionally a second source; map existing rows, keys and tables into a graph schema; test Gremlin/Cypher traversals for fraud/supply-chain chains and visualize results; use access controls and deploy in own cloud; have an AI agent query this graph with provenance; move to multi-node Enterprise for production, SSO, health checks, Datadog and SLA. No copy-based ETL is required for the graph view, but source performance, permissions and query availability remain constraints.
- Fraud analyst traverses connected transfers and accounts to find multi-hop patterns absent from simple SQL dashboards.
- Platform engineer exposes graph relationships over existing lakehouse and databases without moving records to a second graph store.
- AI agent uses graph traversal as one investigative tool; time-to-answer depends on source system, graph schema and query shape.
Key facts
- 2026-11-10: $10M Series A led by Particle Future; defy.vc participates; $15M total incl. the prior $5M seed.
- No ETL graph query and data virtualization over existing databases, lakehouses, cloud storage; no universal performance claim.
- Developer Edition: free forever $0 for one node and at most two concurrent sources; Enterprise pricing based on CPU/RAM; 30-day trial.
Funding history (newest first)
Series A
2026-11-10 $10M Valuation: Undisclosed- Particle Future (lead)
- defy.vc (participant)
Source: Read source · puppygraph.com
Competitive landscape
Vendor calls PuppyGraph an ETL-free graph engine, not an independent graph-modeling breakthrough. Compare data freshness, joins across heterogeneous sources, traversal latency, compute CPU/RAM price, access controls and operational dependency on underlying stores. $0 developer tier has 2-source and single-node restrictions; enterprise price quote needed.
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Neo4j direct
Mature graph database often stores indexed graph data separately; virtual graph vs materialized graph is the key trade-off.
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TigerGraph direct
Native graph database and analytics alternative, potentially requiring separate data pipelines.
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Amazon Neptune direct
Managed graph database in AWS; different operations and data movement tradeoffs.
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Kùzu adjacent
Embedded graph DB for local analytics workloads; not the same distributed zero-ETL use case.
Market / IPO context
Editorial / static context — not a live quote.
FAQs about PuppyGraph
Practical answers founders, operators, and investors typically search for.
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Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.