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BetterDB Cloud – monitor Valkey/Redis in VPCs with a lightweight agent

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BetterDB Cloud – monitor Valkey/Redis in VPCs with a lightweight agent

If your Valkey or Redis instance runs in a private network whether self-managed on EC2, in a Docker Compose stack, or on a managed service getting monitoring data out is annoying. VPC peering, bastion hosts, or running your own Prometheus + redis_exporter + Grafana stack. None of it is hard, but none of it is fun either. We just launched BetterDB Cloud with an agent that runs inside your network as a lightweight container. It connects outbound to our platform over WSS - no inbound ports, no VPC peering, no firewall changes. Drop it next to your database and it starts collecting. What the agent captures that's different from existing tools: Slowlog and COMMANDLOG persistence. Valkey's slowlog holds 128 entries by default. Once the buffer fills, old entries are gone. The agent captures every entry before it rotates and persists it. When your p99 spikes at 3am, the evidence is still there at 9am. COMMANDLOG (Valkey 8.1+) tracks large requests/replies - that 50MB MSET killing your network is now visible. Client attribution. Not just "something was slow" but "app-server-03 sent 80% of the HGETALL user:* queries that caused the slowdown." Connects slow queries to specific clients. Pattern analysis. Instead of scrolling raw slowlog, you see aggregated patterns: "HGETALL user:* accounts for 80% of slow queries, avg 12ms, mostly from 2 clients." Per-slot metrics for clusters. If you're running clustered Valkey, per-slot key counts and memory distribution for spotting hot slots. 99 Prometheus metrics. If you already have Grafana, just point it at our /prometheus/metrics endpoint. Get Valkey-specific data that redis_exporter doesn't expose. Important caveat on managed services: The agent works everywhere, but the value varies. On self-managed instances and standard ElastiCache, you get the full picture - slowlog, client lists, ACL logs, config monitoring. On fully managed services like ElastiCache Serverless that restrict admin commands (SLOWLOG, CLIENT LIST, ACL LOG), the agent is limited to what INFO provides. We surface core metrics (memory, CPU, connections, ops/sec, replication) but the historical persistence features that make BetterDB unique won't be available. The dashboard shows you exactly which features are available vs restricted for your instance. Self-hosted: https://github.com/BetterDB-inc/monitor Cloud: https://betterdb.com Docs: https://docs.betterdb.com Everything is free during beta - no credit card. Happy to answer architecture questions, take feature requests, or talk about the Valkey ecosystem.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, dock · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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