INOD vs IT

Innodata Inc. vs Gartner, Inc. — Valuation Comparison 2026

INOD

Information Technology Services
Innodata Inc.
Quality
9.7
out of 10
Value Trap
6
SAFE
Price
$99.35
Last close
Models
12/13
Active
VS

IT

Information Technology Services
Gartner, Inc.
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$161.18
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType INOD Fair ValueINOD Upside IT Fair ValueIT Upside
Bayesian DCF Intrinsic $9.89 -90.0% $422.24 +162.0%
Earnings Power Value Intrinsic $14.27 -85.6% $67.30 -58.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for INOD vs IT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

INOD vs IT — Which Stock Is More Undervalued?

IT scores higher with a 10.0/10 quality rating vs INOD's 9.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Innodata Inc. (INOD) and Gartner, Inc. (IT) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

INOD currently trades at $99.35 with a QOC of 9.7/10, while IT trades at $161.18 with a QOC of 10.0/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).