Intro
In the last few months, new models and harnesses have been released at an unprecedented pace. As more options become available to engineers, choices are being made based on the task and cost rather than defaulting to the one-size-fits-all approach teams have relied on in the past. When those choices change every few weeks, teams can lose sight of what they’re actually using, what’s working, and where to focus resources.
Data Model + Storage
Tracking the overall model usage by itself wasn't enough. Our users wanted to know who made the choice, which workspace and environment the work ran in, which harness was involved, how it was authenticated, and whether an automation, skill, or MCP was used to guide the work. Analytics stores those details with each interaction. A single agent turn can trigger several actions, including skill and MCP calls, so teams can see which tools were called alongside it.
With this information teams can then:
- Compare harnesses. See which harnesses engineers choose for direct coding tasks and recurring automations.
- Trace activity. See who generated it and which workspace, environment, and automation it came from.
- Configure tools. See which skills and MCPs are being called in each environment, then adjust the tools available there.
- Track trends over time. Compare model, harness, and tool choices as teams change their setup.
Our ingestion pipeline traced over 1.8 million events across 4 weeks. We store those events in ClickHouse, allowing us to quickly group and query activity by date, organization, model, harness, automation, and environment. That keeps the dashboard feeling snappy as the number of ingested events grows.
With all of this information in one quick, snappy dashboard, CTOs and engineering managers can see where their AI resources are going and decide where to standardize, invest, or preserve flexibility.
Seeing The Industry Shift
At Replicas, aggregating usage across organizations gives us a bird’s-eye view into how the engineering industry is responding to the constant influx of new models and harnesses. A single organization can reflect a bias, so we compare usage across many organizations to identify broader trends. We can see those trends when teams switch harnesses, when models move into recurring automations, and when organizations settle on a few combinations or choose different tools for different tasks.
The tables below show harness and model usage across the 4-week launch window.
Harness Usage By Week
| Week | Codex | Claude Code | Cursor | Other |
|---|---|---|---|---|
| July 27th - August 2nd | 63.2% | 33.1% | 3% | 0.7% |
| August 3rd - 9th | 44.6% | 51.7% | 1.4% | 2.3% |
| August 10th - 16th | 58.9% | 39.5% | 1.5% | 0% |
| August 17th - 23rd | 54.6% | 43.4% | 1.1% | 0.9% |
Top 4 Models Used By Week
| Week | Opus 5 | 5.6 Sol | 5.6 Luna | Fable 5 |
|---|---|---|---|---|
| July 27th - August 2nd | 29.9% | 37.6% | 25.1% | 0.7% |
| August 3rd - 9th | 44.3% | 30.3% | 12.8% | 4.1% |
| August 10th - 16th | 30.6% | 38.9% | 19.5% | 7.2% |
| August 17th - 23rd | 32.9% | 35% | 16.7% | 8.9% |
From Usage To Better Decisions
Analytics in Replicas now brings together activity across 7 coding harnesses, 15 harness-specific authentication options, and 8 workspace sources.
Analytics isn’t about ranking engineers or declaring one model the winner. It’s about giving teams the context to make better decisions as their tools and workflows continue to evolve. As new models, harnesses, and tools emerge, Analytics will grow with them, helping teams see what changed, how it affected their usage, and where to focus next.