How we model the time saved
The headline figures, from about 6 hours a week for a network engineer up to 9.5 for DevOps or IT support lead, come from a single, transparent model. We count six recurring sources of friction, estimate how often each happens and how long it costs, and add them up. Then we apply a conservative recovery rate, because moving into one app removes most of that overhead but not all of it.
The three breakdowns below show the working for each role. The number of tools in active rotation is what drives the cost, so we model each profile separately rather than quoting one average.
The per-instance assumptions, shared across every model
App-to-app context switch
2 min, switch + reorientation
Manual data transfer (copy/paste IPs, tokens, payloads)
1.5 min
Tool startup, heavyweight cold start
1 min
Licence / update / version-mismatch friction
5 min per event
Machine setup / reinstall
amortised over 26 weeks
Security-approval friction (cloud tools flagged by policy)
20 min per event
GraphDagger recovers roughly 70% of the resulting weekly friction. Not 100%, because you still do the actual investigative work and still move between tools inside the app, but the switching, re-authenticating, copy-pasting, cold starts, update prompts and approval overhead largely collapse into one window.
Network engineer
7 to 9 tools in active use · ~30 switches/day
Security / forensics engineer
10 to 13 tools in active use · ~40 switches/day
DevOps/IT support lead
12 to 15 tools in active use · ~50 switches/day
These rows for context switching also illustrate why more tools compound the loss faster than they add to it: with 15 tools in rotation there are 105 possible tool-to-tool transitions to hold in your head, against just 10 with five tools. The switching cost climbs far faster than the tool count.
The hidden cost of too many tools
And why more tools compound loss quadratically.
Combinatorial switching pairs
As tool count rises, the possible tool pairs grow quadratically faster, the cognitive load of working with 15 tools is 10 times greater than working with 5. More tools equals an exhausted brain. If you've ever had twelve windows open during an incident, you already know this. GraphDagger puts everything in one place.
from 5 tools
from 15 tools
the cognitive load
But switching cost is only half the problem. The other half is invisible until an incident forces it into view.
The data coherence problem
When investigating an incident across multiple tools, the engineer must mentally track how findings in each tool relate to findings in all the others, a hidden cost that rarely gets counted but significantly impacts investigation quality and speed.
Consider an engineer investigating an intermittent authentication failure with six tools open simultaneously:
Six windows, open at once
- Wireshark open showing TCP streams between client and auth server
- Proxyman intercepting the HTTP requests and responses
- JWT.io open to decode the bearer token from the captured traffic
- Postman ready to replay the request with modified parameters
- Termius SSH'd into the auth server to tail the application log
- DBeaver connected to the session database to inspect token records
Each time a new finding appears in one tool, the engineer must mentally map it to the state of all the others. With GraphDagger, all of this lives in a single session. The data coherence problem is drastically reduced, a qualitative improvement that goes beyond counting hours saved.
For engineers working on complex multi-system incidents, GraphDagger's advantage is larger than any time or cost estimate captures. Faster resolution of a production outage or security incident has a value that dwarfs the licence cost many times over.