Karat evaluates traditional coding ability: algorithms, data structures, CoderPad coding under time pressure. It does not evaluate AI agent proficiency, token efficiency, prompt quality, or verification discipline. If your team uses Claude Code and Cursor daily, Karat will not tell you how well a candidate does the actual job. Altor's service was built specifically for that gap.
Side-by-Side Comparison
| Capability | Karat | Altor |
|---|---|---|
| Evaluates Cursor / Claude Code / Copilot usage | ✗ Not offered | ✓ Core focus |
| Token efficiency scoring | ✗ Not offered | ✓ Every report |
| AI session transcript review | ✗ Not offered | ✓ Annotated and analyzed |
| Traditional algorithm / DSA evaluation | ✓ Primary offering | Optional add-on |
| Live interview with human expert | ✓ Trained interview engineers | ✓ AI agent specialist |
| Verification discipline scoring | ✗ Not measured | ✓ 40% of rubric |
| Prompt quality / orchestration judgment | ✗ Not measured | ✓ Scored dimension |
| Volume commitments required | ✗ Yes — minimum commitment standard | ✓ No — pay per interview |
| Pricing | $200–$450/interview, no public pricing | Contact for quote, no commitment |
| Works for startups hiring <50 engineers/year | ✗ Economics don't work at low volume | ✓ Designed for this scale |
| Report turnaround | 24–48 hours | Within 24 hours |
What Karat Measures (And What It Can't)
Karat's model is built around trained human "Interview Engineers" who conduct structured CoderPad sessions. They score candidates on:
- Algorithm and data structure problem-solving
- Code correctness and efficiency
- Communication during problem-solving
- Time management under pressure
- Code style and readability
These are real skills. But they test a version of the engineering job that is rapidly disappearing. Karat's format explicitly bans or ignores AI tools — because the rubric was designed before AI tools became standard. A candidate who scores 4.5/5 on a Karat interview may be completely unprepared to work in a codebase where the team uses Claude Code and Cursor every day.
When to Use Karat
Karat is a good fit when:
- You're hiring at high volume (50+ engineers per year) and need consistent, standardized technical screening
- The role genuinely requires traditional algorithmic depth — embedded systems, low-level infrastructure, cryptography
- You need a baseline signal on raw coding ability before introducing AI tool evaluation
- You have a large existing relationship with Karat and are evaluating what to add
When to Use Altor
Altor is the right fit when:
- The role requires daily use of Cursor, Claude Code, or GitHub Copilot
- You're hiring AI-native engineers and need to know if a candidate truly understands AI agent workflows — not just that they "use ChatGPT sometimes"
- You've had hires who seemed strong in traditional interviews but underperformed in an AI-native team environment
- You're a startup hiring 5–30 engineers per year where Karat's volume minimums don't make economic sense
- You want to evaluate token efficiency, prompt quality, and verification discipline — none of which Karat measures
- You want to run the AI-enabled interview format that Meta, Google, and Canva have adopted, without building the rubric and process yourself
→ Choose Karat if:
- Hiring 50+ engineers/year at enterprise scale
- Traditional coding signal is primary requirement
- AI tools are not central to the role
- Want standardized cross-team benchmarking
→ Choose Altor if:
- Role uses Claude Code or Cursor daily
- Hiring <50 engineers/year
- Need AI agent proficiency signal specifically
- Team already adopted AI-enabled interview format
The Case for Running Both
For some roles, you want both signals. Use Karat (or a traditional coding screen) to establish a baseline on raw algorithm and debugging depth. Then use Altor to evaluate AI agent proficiency in the same candidate. The combination gives you the complete picture: can they think clearly without AI, and can they direct AI clearly when it's available?
Meta's current format runs exactly this combination — one traditional coding round, one AI-enabled round. The companies getting hiring right in 2026 are not choosing one or the other. They're sequencing them.
Add AI agent evaluation to your interview loop
Altor runs the AI agent interview round that Karat can't. We evaluate what actually predicts performance in an AI-native engineering team.
See also: Complete guide to AI agent interviewing · Altor's interview service · Free scoring rubric