Unified data modeling has boosted graph insights and now drives faster recommendationsThe first and biggest pain point I noticed was the AQL learning curve; for developers coming from an SQL background, AQL feels initially unfamiliar. There are no widely available online courses or bootcamps teaching AQL in the way that there are for SQL or even Cypher. Better structured learning resources and interactive tutorials would significantly lower the barrier to entry. The second pain point is pricing transparency; cost estimations at scale are not straightforward. When planning for infrastructure growth, it is difficult to predict exactly how costs will scale with increasing nodes, edges, and query volume. A proper cost calculator on their website would be extremely helpful. The third pain point is query optimizer limitations; for very complex multi-level graph traversals, the query optimizer sometimes makes suboptimal execution choices, requiring us to manually hint the optimizer in certain cases, which should not be necessary in a mature database platform. Finally, the ecosystem maturity is another concern; compared to MongoDB or PostgreSQL, the community and third-party tooling around ArangoGraph are still relatively small, resulting in fewer Stack Overflow answers, fewer integrations, and fewer tutorials. None of these are deal-breakers, but they reflect the growing pains of a platform that is still maturing. The core technology itself is generally excellent. One thing I really wish ArangoGraph would improve is the Visual Graph Explorer performance. It is a fantastic feature conceptually, but when the graph grows beyond a certain size, say fifty thousand plus nodes, the explorer becomes noticeably sluggish. Rendering a large graph in the browser gets heavy, so a smarter sampling or progressive loading approach would make it much more usable at scale. Another small but frustrating issue is the error messaging in AQL; when a query fails, the error messages can sometimes be cryptic and unhelpful. As a developer, you often spend more time debugging the error messages than actually fixing the query. More descriptive and actionable error messages would save a lot of developer frustration. Lastly, I would also appreciate a dark mode option for the UI; it sounds minor, but developers spend long hours in the interface, and a dark mode option is something the community has been requesting for a long time. These are not critical issues, but they are the type of polish that separates a good product from a truly great one. A few more improvements I have not mentioned include better GraphQL support, as ArangoGraph has some GraphQL integration, but it is not seamless. Many modern applications are built on GraphQL, and having first-class GraphQL support would make ArangoGraph much more accessible to frontend developers who are not familiar with AQL. Improved data import tools are also needed; migrating existing data into ArangoGraph from other databases like PostgreSQL or MongoDB has been more manual than expected. A proper migration wizard with schema mapping and data transformation built in would significantly reduce onboarding friction. Lastly, better Kubernetes integration would benefit teams running hybrid or on-premises deployments, with native Kubernetes operators being more mature and better documented, as we have seen several community complaints regarding this during our research phase. These improvements would really elevate ArangoGraph from a great database to a complete graph intelligence ecosystem.
