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+---
+slug: devoxxgenie-plugin-analytics
+title: "What 18,000 Events Tell Us About How You Use DevoxxGenie"
+authors: [stephanj]
+tags: [analytics, telemetry, local llm, ollama, qwen, agent mode, rag, mcp, IntelliJ IDEA, privacy]
+date: 2026-06-30
+description: "A look behind the curtain at DevoxxGenie's anonymous usage analytics: what it reveals about local-first AI coding, the rise of Qwen, and how developers actually use agent mode, RAG and MCP."
+keywords: [devoxxgenie, analytics, telemetry, local llm, ollama, lmstudio, qwen, agent mode, rag, mcp, intellij plugin, privacy]
+image: /img/devoxxgenie-providers-chart.png
+---
+
+# What the Numbers Say About How You Use DevoxxGenie
+
+DevoxxGenie just passed a milestone worth celebrating: **more than 72,000 downloads**, and in this month alone **29,330 active users** firing up the plugin inside IntelliJ IDEA. Thank you. Genuinely.
+
+A smaller, opt-in slice of those users also share **anonymous usage analytics**. No prompts, no code, no file contents, no personal data, just coarse aggregated signals about which features get enabled, which providers get used, and which models get picked. The goal is simple: stop guessing about what matters and start building for how people actually work.
+
+Even from that opted-in subset, **more than 18,000 telemetry events** over the past month paint a remarkably clear picture. A few of the patterns surprised me. Here is the story the data tells.
+
+<!-- truncate -->
+
+:::info A note on the charts
+The charts below are real, straight from the dashboard, but they show **proportions, not raw counts**. This post is about how the bars stack up, not exact figures. Everything DevoxxGenie collects is anonymous and aggregated by design.
+:::
+
+## 1. Local-first isn't a slogan, it's the default
+
+The single loudest signal in the entire dataset: **developers run their models locally.**
+
+![Feature usage split between Local and Cloud providers](/img/devoxxgenie-local-vs-cloud.png)
+
+Looking at *Feature Used by Provider Type*, **roughly 85% of all activity went to local providers** and only about 15% to cloud APIs. The provider leaderboard tells the same story from a different angle. The top three by prompts dispatched are all local:
+
+1. **[Ollama](/docs/llm-providers/local-models)**, by a wide margin the most-used provider
+2. **[LMStudio](/docs/llm-providers/local-models)**
+3. **[CustomOpenAI](/docs/llm-providers/custom-providers)** (local OpenAI-compatible endpoints)
+
+![LLM providers ranked by prompts dispatched](/img/devoxxgenie-providers-chart.png)
+
+Cloud providers (OpenRouter, Google, DeepSeek, Anthropic, OpenAI) all appear, but they sit well below the local pack. In total, **13 different providers** showed up in the data, from the usual suspects all the way down to Groq and Kimi. The breadth of DevoxxGenie's [provider support](/docs/llm-providers/overview) isn't theoretical; people genuinely use the long tail.
+
+The takeaway for the project is unambiguous: **local inference is the primary path**, and keeping that experience fast, private, and frictionless is the highest-leverage thing we can do.
+
+## 2. Qwen ate the model leaderboard
+
+If local is the *where*, **Qwen is the *what*.**
+
+![Models ranked by prompts dispatched, dominated by Qwen variants](/img/devoxxgenie-models-chart.png)
+
+Both the "models selected" (intent) and "prompts dispatched" (actual) charts are dominated by Qwen coder variants. **`qwen2.5-coder:7b` leads both lists.** It is the most-selected *and* the most-dispatched model. The rest of the top tier is more Qwen: `qwen3-coder:30b`, `qwen3.6:35b-mlx`, `qwen/qwen3.6-27b`, `qwen2.5-coder:14b`, and a parade of community quantizations.
+
+Two things jump out:
+
+- **Small, fast coder models win.** The 7B model out-uses everything heavier. People want a model that keeps up with their typing more than they want the biggest brain in the room.
+- **Apple Silicon is well represented.** MLX builds like `qwen3.6:35b-mlx` ranking near the top is a clear fingerprint of Mac developers running optimized local inference.
+
+The cloud models that do appear lean toward the cheap-and-fast end too (`gemini-2.5-flash-lite`, `deepseek-reasoner`), reinforcing the same "fast feedback loop" preference.
+
+## 3. People pick a model once, then hammer it
+
+Comparing *intent* (model selected) against *actual* (prompts dispatched) reveals a nice behavioral ratio: **for every model selection, developers fire off roughly 3 to 4 prompts.** `qwen2.5-coder:7b`, for instance, was dispatched several times more often than it was explicitly selected.
+
+In other words, model-switching is rare. Developers settle on a model and stay in flow. That argues for making the *default* model excellent and the *switching* experience cheap, but not for optimizing around constant model-hopping, which barely happens.
+
+## 4. Agent mode is now the headline feature
+
+![Features used per prompt: Agent mode leads](/img/devoxxgenie-features-used.png)
+
+When you look at **features actually used per prompt**, **[Agent mode](/docs/features/agent-mode) comes out on top**, ahead of everything else. Right behind it: **[Project Context (Selected)](/docs/features/project-scanner)**, then **[Streaming](/docs/features/chat-interface)** and **[RAG](/docs/features/rag)**.
+
+A couple of details worth highlighting:
+
+- **Scoped context beats "everything."** *Project Context (Selected)* is used far more than *Project Context (Full)*. Developers are deliberate. They hand the model the files that matter rather than dumping the whole repo. Good instinct, and good for token bills.
+- **Streaming is the expected default.** A large share of prompts stream their response. Watching the answer arrive token by token clearly beats waiting for a wall of text.
+
+On the *enabled per session* side, **[Custom Prompt](/docs/configuration/prompts)** is switched on in about half of sessions, followed by **Agent (about 24%)**, **Streaming (about 11%)**, **RAG (about 7%)**, **[MCP](/docs/features/mcp_expanded) (about 6%)**, and **[Web Search via Tavily](/docs/features/web-search) (about 3%)**. The headline features get broad adoption; the advanced ones have a smaller but committed following.
+
+## 5. MCP and long memory are power-user territory
+
+Two of the smaller panels are quietly the most interesting.
+
+- **MCP servers per session:** most sessions run *zero* MCP servers, but a meaningful cluster runs **2 to 5**, and a handful push into the **6 to 10** range. MCP is a power-user feature, and the power users are clearly stacking servers.
+- **[Chat memory](/docs/features/chat-memory):** the vast majority of sessions sit in the **highest memory bucket (21+ messages)**. These aren't one-shot questions. They are long, sustained conversations where context accumulates. That validates the work on chat memory management and persistent memory.
+
+Meanwhile, the **[Semantic Search](/docs/features/rag)** tool barely registers yet. It is the newest arrival, so low adoption is expected, but it is a flag to make the feature more discoverable.
+
+## What this changes
+
+None of this is collected to surveil anyone. It is collected so the roadmap reflects reality instead of my assumptions. A few decisions fall straight out of the data:
+
+- **Double down on local.** Ollama, LMStudio, and CustomOpenAI are the main stage. Their setup, speed, and model management deserve the most polish.
+- **Treat Qwen coder models as first-class citizens**, including MLX builds for Apple Silicon.
+- **Keep investing in agent mode.** It is no longer a side feature, it is *the* feature.
+- **Make newer capabilities (Semantic Search, MCP) more discoverable**, because the people who find them clearly get value from them.
+
+With **72,000+ downloads** and nearly **30,000 active users a month**, every one of these choices now lands in a lot of editors. If you'd rather not contribute any telemetry, that's completely fine. It is opt-in and easy to turn off in settings. But if you leave it on, know that it is directly shaping where DevoxxGenie goes next. Thank you for that. 🙏
+
+*Curious about exactly what is and isn't collected? It is all defined by a closed-enum schema. Only known, non-identifying values are ever recorded, and anything outside the allowlist is dropped.*
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