InfluxDB vs QuestDB
One of fourteen models named InfluxDB first on the direct prompt; zero named QuestDB. Both were named by all fourteen models and InfluxDB carries 76 labels and QuestDB 38, so the shares are not directly comparable.
Named in two categories this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the time series databases page.
By framing
InfluxDBFirst choices, of fourteen modelsQuestDB
Across every category in the November 2026 Edition, InfluxDB and QuestDB were named in the same answer 115 times, of the 222 answers naming InfluxDB and the 126 naming QuestDB. In those answers QuestDB took the first choice three times and InfluxDB twenty-six.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | | |
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | | | |
| Grok 4.20 | | | | | | |
| Mistral Small | | | | | | |
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | |||
| GLM 4.7 FlashX | | | | |||
| MiniMax M2.5 | | | | | ||
| GPT-6 Luna | | | | | | |
| Muse Glimmer 30B | | | | | |
InfluxDB QuestDB first choice named as an alternative argued againstblank: not namedEach cell is one answer, InfluxDB on the left and QuestDB on the right.The direct prompt
InfluxDB first, QuestDB not the choice
1 of 14 modelsQuestDB was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
13 of 14 modelsThe answer put something else first and named one of the two as an alternative.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
InfluxDB16%#2 of 8QuestDB0%#5 of 8What the models said about InfluxDB
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“avoid InfluxDB 3 Core (OSS) for production unless your use case genuinely fits within 72-hour retention and 5 databases” Claude Haiku 4.5 · negative prompt · hard negative
“When to avoid: If stability, easy upgrades, or long-term maintenance are priorities. Many teams are migrating away” Grok 4.20 · negative prompt · hard negative
“InfluxDB 3 is a different generation from v1/v2, and its Core edition has documented query-range limitations that may matter for historical analytics.” GPT-6 Luna · scale prompt · soft negative
“InfluxDB is considered the best overall reference point for broad time series workloads spanning metrics, IoT, and real-time analytics with strong ingest.” Claude Haiku 4.5 · comparative prompt · first choice
“TimescaleDB (if using PostgreSQL) or InfluxDB Open Source tend to offer the best balance of features, performance, and cost” Claude Haiku 4.5 · budget prompt · first choice
“Start with InfluxDB if you want a proven, well-supported solution with a managed cloud option and extensive integrations.” Kimi K2 · direct prompt · first choice
What the models said about QuestDB
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“High ingestion throughput with SQL and open formats like Parquet, with zero license cost → QuestDB open-source core.” Muse Glimmer 30B · budget prompt · first choice
“QuestDB if performance and raw speed are priorities and you're starting fresh” MiniMax M2.5 · budget prompt · first choice
“QuestDB is another good option for teams needing to handle large volumes of time-series data without a high cost.” Mistral Small · budget prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.