What Engineering Leaders Actually Need from AI Coding Tools
How to track and maximize AI ROI
Most engineering teams have adopted AI coding tools by now. The problem is that almost none of them can answer a basic question: is it actually working?
Not “do developers like it” or “does it feel faster” — actually working. Measurably improving output. Worth the spend.
When leadership asks about AI ROI, engineering managers are stuck gesturing vaguely at developer sentiment surveys and anecdotal productivity gains. The data to answer the question properly just doesn’t exist in most setups.
Kilo Teams and Kilo Enterprise were built to solve this. Here’s what that looks like.
The Visibility Problem
Here’s what’s often invisible to engineering leaders:
Actual costs. Bundled pricing obscures what you’re really paying for AI. An all-inclusive monthly fee tells you nothing about which models are being used, whether heavy users are getting proportional value, or whether light users are wasting seats.
Adoption depth. “Active users” is a vanity metric. Someone who asks AI one question a week and someone who ships AI-generated code daily both count as “active.” These are not the same.
Adoption breadth. Aggregate numbers might look healthy while half the team never touches the tool. Power users can mask low adoption across the org.
Trust signals. Are developers actually accepting AI suggestions? Or are they asking questions, glancing at the output, and writing the code themselves anyway?
Without this information, optimization is impossible. Money goes into AI tooling with the hope that it helps.
What Kilo Teams and Enterprise Provide
Kilo Teams ($15/user/month) and Kilo Enterprise ($150/user/month) are built around transparency and measurability.
Transparent Pricing
Kilo passes through model provider costs with zero markup. When a developer sends a request to Claude or GPT-5.2, the cost shows up in the dashboard, attributed to that user, that project, that model. The $15/month covers the platform - team management, analytics, infrastructure. AI costs are separate and visible.
This matters for two reasons. First, teams actually know what they’re spending. Second, it changes how developers think about model selection. When costs are visible, people naturally optimize—using faster, cheaper models for simple tasks and reserving expensive models for complex reasoning.
No Rate Limiting
Other tools throttle usage or quietly downgrade model quality during peak periods. Kilo doesn’t. If a team needs to push hard during a critical sprint, that’s fine. Pay for what gets used, use what’s needed.
The AI Adoption Score
This is the core of the ROI story.
The AI Adoption Score is a 0-100 metric that quantifies how deeply a team has integrated AI into actual development work. It’s calculated from three dimensions:
Frequency (40%): How often are developers using AI? Daily usage across multiple surfaces (IDE, CLI, cloud) indicates AI has become habitual. Sporadic usage suggests it’s still a novelty.
Depth (40%): How much AI-generated code actually ships? This measures acceptance rates, retention rates (code that stays in the codebase unaltered), and multi-stage usage (planning → coding → review). High depth means developers trust the output enough to commit it.
Coverage (20%): How many team members are actually using AI, and how consistently? A team with a few power users and many non-users will score lower than a team with broad, consistent adoption.
The score tiers provide a quick read:
0-20: Minimal adoption (experimental at best)
21-50: Early adoption (some developers bought in)
51-75: Growing adoption (becoming standard practice)
76-90: Strong adoption (deeply integrated)
91-100: AI-first engineering
Week-over-week trends show whether initiatives are working. “The team’s AI Adoption Score went from 38 to 57 this quarter” is a concrete statement for leadership.
But the score isn’t just for reporting - it’s for improving. Each dimension comes with actionable recommendations. Low frequency? The dashboard suggests installing the CLI to bring AI into terminal workflows. Low depth? It recommends chaining your workflows so AI can touch multiple points across architecture, implementation, and review. Low coverage? It flags inactive seats and suggests onboarding tactics. The score tells you where you stand; the recommendations tell you how to move up.
Usage Analytics
Beyond the adoption score, Kilo provides granular usage data:
Cost and request volume by user, model, project, and day
Token usage breakdowns (input vs. output)
Model popularity across the team
Trend lines for spotting patterns
Questions become answerable: Which models are developers actually choosing? Which projects have the highest AI usage? Are costs concentrated in a few users or distributed?
Enterprise Additions
For larger organizations, Enterprise adds governance and compliance controls:
Model and provider restrictions. Filter available models by data policy (does it retain prompts?), geographic region, pricing tier, or capability. If compliance requires models that don’t train on user data, that’s enforceable org-wide.
Audit logs. Every significant action - logins, role changes, model access modifications, settings updates - is logged with timestamps and attribution.
SSO/OIDC/SCIM. Integration with existing identity providers. Automatic provisioning when people join, automatic deprovisioning when they leave.
Custom modes. Specialized AI behaviors for the team. A “Security Reviewer” mode with specific instructions and tool access. A “Documentation Writer” mode that follows the company style guide. Define it once, deploy it to everyone.
MCP Auth. For teams using remote MCP servers, manually configuring access tokens is a security headache - tokens get shared in Slack, stored in plaintext, forgotten about until they expire. Kilo’s MCP Auth handles OAuth-compatible servers properly: users authenticate through a standard OAuth flow, and Kilo stores tokens securely and refreshes them automatically. No more API keys floating around.
Migration Path
For teams currently on Cursor, Copilot, or other tools, the typical migration path:
Install Kilo alongside the current tool
Run both in parallel for a week or two
Migrate projects gradually, starting with non-critical work
Compare results and costs
Cancel old subscriptions once confident
Existing rules and configurations (like .cursorrules files) convert to Kilo’s format with minimal changes. Most teams complete migration in ~2 weeks.
When Paid Plans Make Sense
Kilo Teams and Enterprise aren’t for everyone.
Solo developers or small teams that don’t need to justify AI spend can use the free version of Kilo Code without issue.
But for engineering leaders responsible for AI adoption across an organization—for anyone who needs to answer the ROI question with something other than gut feel—this is the solution. Transparent costs, real adoption metrics, and the analytics to actually optimize how a team uses AI.
app.kilo.ai/organizations/new - setup takes about 10 minutes.
is there anything planned for individual developers other than free tier? paid, but benefficial for those - as idea, access to edu content, some tokens as subscription, etc.