Best ML Model Development Companies

Addepto vs InData Labs: full comparison for 2026

Last updated: July 2026

Quick verdict

Addepto (4.4/5) edges ahead of InData Labs (4.3/5) overall. Addepto is the better choice for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. InData Labs is the stronger option for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.. The right choice depends on your project size, budget, and required tech stack.

Addepto vs InData Labs: head-to-head summary

Criterion Addepto InData Labs
Founded 2018 2014
HQ Warsaw, Poland Nicosia, Cyprus (delivery center: Minsk, Belarus)
Team size 51–200 51–200
Rating 4.4 / 5 4.3 / 5
Best for Cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build. Companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.
Pricing model Project-based Project-based
Min. engagement $10,000 $25,000
Primary tech stack Python, MLOps tooling, Cloud ML platforms (AWS/GCP/Azure) Python, Computer vision frameworks, NLP toolkits
Industries served Finance, Healthcare, Retail Transportation/logistics, Retail, Finance

Addepto vs InData Labs: overview

Addepto

Addepto is a Poland-based AI consulting firm founded in 2018 by Artur Haponik and Edwin Lisowski that focuses specifically on machine learning consulting, MLOps consulting, and data/analytics advisory work rather than broader software development. The company has around 52 employees and holds a 4.7 Clutch rating, with Clutch-reported project costs typically in the $10,000–$49,000 range, making it one of the more budget-accessible options among firms in this category. Addepto has been recognized among Forbes' top AI consulting companies and appeared on the Deloitte Technology Fast 500 EMEA list, citing 1,193 percent revenue growth over the qualifying period. In December 2025, Addepto was acquired by KMS Technology, a US-based digital engineering, data, and AI company backed by growth private equity firm Sunstone Partners; Addepto now operates as an integrated division rather than as a fully independent company.

InData Labs

InData Labs is a data science consultancy founded in 2014 by Marat Karpeko, with a registered headquarters in Nicosia, Cyprus, and its primary research and development center in Minsk, Belarus. The company focuses on predictive analytics, natural language processing, and computer vision, delivering custom AI model development for clients ranging from logistics to retail. Published case studies include a freight-rate prediction model for a transportation company and a dog-face-identification model reporting 91.96 percent accuracy, giving it more quantified, checkable outcome data than many peers of similar size.

Services and capabilities: Addepto vs InData Labs

Capability Addepto InData Labs
Custom model training
Fine-tuning & adaptation
MLOps pipeline
Model deployment & serving
Data engineering for ML
ML infrastructure management
Computer vision
NLP & LLM development
Forecasting & time-series modeling
ML strategy consulting

Tech stack comparison: Addepto vs InData Labs

Framework / platform Addepto InData Labs
PyTorch N/A N/A
TensorFlow N/A N/A
MLflow N/A N/A
AWS SageMaker N/A N/A
Amazon Bedrock N/A N/A
Google Cloud N/A N/A
Microsoft Azure N/A N/A
Kubernetes N/A N/A
Snowflake N/A N/A
NVIDIA N/A N/A

Pricing comparison: Addepto vs InData Labs

Criterion Addepto InData Labs
Minimum engagement $10,000 $25,000
Engagement models Fixed project, Advisory/consulting retainer Fixed project, Time & Material
Rate transparency Minimum disclosed Minimum disclosed
Price tier Accessible Mid-market

Target audience comparison: Addepto vs InData Labs

Dimension Addepto InData Labs
Best company size Startup to mid-market Startup to mid-market
Best industries Finance, Healthcare, Retail Transportation/logistics, Retail, Finance
Best use cases Auditing an existing ML pipeline and recommending MLOps improvements, Running a well-scoped, budget-constrained machine learning pilot Building a predictive pricing or demand-forecasting model for logistics or transportation, Developing a computer-vision classification model with a documented accuracy target
Typical project type Fixed project Fixed project

Addepto vs InData Labs: pros and cons

Addepto
+ 4.7 Clutch rating with lower typical project cost ($10K–$49K) than most peers in this comparison.
+ Named a top 10 AI consulting company by Forbes.
+ Deloitte Technology Fast 500 EMEA recognition (#143) signals strong recent revenue growth.
+ Focused specifically on ML/MLOps consulting rather than diluting attention across general software development.
- Small team (~52 employees) caps capacity for large or multiple concurrent enterprise engagements.
- Lower typical project size may signal a fit for smaller-scope work rather than large production ML platforms.
- Public case studies with named enterprise clients are limited in available sources.
- Now part of KMS Technology following the December 2025 acquisition, introducing near-term integration and roadmap uncertainty for prospective clients.
InData Labs
+ Case studies include specific, quantified model accuracy figures rather than vague outcome claims.
+ Featured among Clutch's broader provider directory with a positive review sentiment on delivery timeliness.
+ Focused specialization in predictive analytics and computer vision avoids service-line dilution.
+ Recognized in a 2016 "Top 100 Big Data" listing, indicating an established track record.
- Team size figures are inconsistent across sources (roughly 50–80 depending on source), so exact headcount is uncertain.
- Registered HQ (Cyprus) differs from the primary delivery center (Belarus), which some buyers may want clarified given regional considerations.
- Public tech-stack disclosure is limited beyond high-level specialization areas.
- Fewer large, brand-name enterprise clients named publicly compared to bigger peers.

Who should choose Addepto?

Addepto is the right choice for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..

Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. Minimum engagement starts at $10,000. Works best with clients in Finance, Healthcare, Retail.

Who should choose InData Labs?

InData Labs is the right choice for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks..

Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. Minimum engagement starts at $25,000. Works best with clients in Transportation/logistics, Retail, Finance.

Decision matrix: Addepto vs InData Labs

Your situation Recommended choice
You need full-ownership delivery on a defined project scope Addepto
You need a large dedicated team for an ongoing programme Check each company's engagement model
Your budget is at the lower end Addepto
You need specialist depth in a specific vertical Addepto
You need staff augmentation or team extension Neither; consider alternatives that offer staff aug
You need consulting before committing to a build Addepto

Use case fit: Addepto vs InData Labs

Use case Addepto fit InData Labs fit Winner
Auditing an existing ML pipeline and recommending MLOps improvements Strong Limited Addepto
Running a well-scoped, budget-constrained machine learning pilot Strong Strong Both equally
Building a predictive pricing or demand-forecasting model for logistics or transportation Limited Strong InData Labs
Developing a computer-vision classification model with a documented accuracy target Limited Strong InData Labs
Fixed-price build Limited Limited Both equally
MLOps pipeline setup Strong Limited Addepto

Verdict: Addepto vs InData Labs

Addepto (4.4/5) is the stronger overall choice for most ML Model Development projects. Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. It is best for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..

InData Labs (4.3/5) is the better choice when companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.. If your situation matches those criteria, InData Labs is a competitive option.

Related comparisons

Addepto vs InData Labs FAQ

Is Addepto better than InData Labs?

Addepto (4.4/5) scores higher overall, but "better" depends on your use case. Addepto is better for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. InData Labs is better for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks..

How do Addepto and InData Labs differ in pricing?

Addepto uses project-based pricing with a minimum engagement of $10,000. InData Labs uses project-based pricing with a minimum engagement of $25,000. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.

Which is better for enterprise: Addepto or InData Labs?

Addepto is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.

What are the main differences between Addepto and InData Labs?

Addepto's primary differentiator is: dedicated mlops-consulting service line and clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. InData Labs's primary differentiator is: publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. They also differ in team size (51–200 vs 51–200), minimum engagement ($10,000 vs $25,000), and primary industries served (Finance, Healthcare vs Transportation/logistics, Retail).

Last reviewed: July 2026. Verify all details directly with each company before making a decision.