Markovate vs InData Labs: full comparison for 2026
Quick verdict
Markovate (4.5/5) edges ahead of InData Labs (4.1/5) overall. Markovate is the better choice for founders wanting a generative AI-only product partner. InData Labs is the stronger option for teams needing data science depth behind a generative AI build. The right choice depends on your project size, budget, and required tech stack.
Markovate vs InData Labs: head-to-head summary
| Criterion | Markovate | InData Labs |
|---|---|---|
| Founded | 2015 | 2014 |
| HQ | San Francisco, United States | Limassol, Cyprus |
| Team size | 51-200 | 51-200 |
| Rating | 4.5 / 5 | 4.1 / 5 |
| Primary differentiator | AI-exclusive focus dating to 2015, ahead of the current generative AI cycle | Data-science-first heritage predating the generative AI branding wave |
| Pricing model | Fixed project or dedicated team | Fixed project or dedicated team |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, OpenAI API | Python, OpenAI API, TensorFlow |
| Industries served | Fintech, Healthcare, Retail & e-commerce, Logistics | Retail & e-commerce, Gaming, Fintech, Healthcare |
Markovate vs InData Labs: overview
Markovate
Markovate has run as an AI-only agency out of San Francisco since 2015, with a team in the 51-200 range under co-founder Rajeev Sharma. Its decade of case studies has stayed centered on generative AI and machine learning product work specifically, predating the current wave of firms rebranding around large language models. That narrow focus trades breadth for depth: clients get a generative AI specialist, not a full-service development partner handling every kind of project.
InData Labs
InData Labs was founded in 2014 by gaming-industry veteran Marat Karpeko and is headquartered in Cyprus, with additional offices reported in Lithuania and the US. Staff estimates swing between roughly 65 and 200 across sources. Its practice centers on data science, predictive analytics, natural language processing, and computer vision, with generative AI layered onto that foundation rather than replacing it, positioning it closer to a data-first consultancy than a generative-AI-branded agency.
Services and capabilities: Markovate vs InData Labs
| Capability | Markovate | InData Labs |
|---|---|---|
| Generative AI | ✓ | ✓ |
| Machine learning | ✓ | ✗ |
| AI agents | ✓ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✗ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: Markovate vs InData Labs
| Framework / platform | Markovate | InData Labs |
|---|---|---|
| Python | ✓ | ✓ |
| OpenAI API | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| LangChain | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Markovate vs InData Labs
| Criterion | Markovate | InData Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed project, Dedicated team | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Markovate vs InData Labs
| Dimension | Markovate | InData Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Healthcare, Retail & e-commerce | Retail & e-commerce, Gaming, Fintech |
| Best use cases | Turning a generative AI concept into a shipped product with a small, senior team., Getting a fast generative AI prototype built before deciding on an in-house hire. | Building a generative AI feature on top of an existing data warehouse., Adding computer vision alongside generative AI to a product with image or video data. |
| Typical project type | Fixed project | Fixed project |
Markovate vs InData Labs: pros and cons
| Markovate | |
|---|---|
| + | Ten years of AI-only positioning predates most competitors' generative AI pivot. |
| + | Based in San Francisco, close to the model providers it integrates most often. |
| + | Willing to take direct founder calls rather than routing through account management layers. |
| + | Case studies describe shipped generative AI products rather than proof-of-concept demos. |
| - | Team size limits how many large concurrent engagements the agency can realistically run |
| - | No published minimum engagement figure to budget against upfront |
| InData Labs | |
|---|---|
| + | Founder's gaming background brings real-time data processing experience to computer vision work. |
| + | Cyprus headquarters (EU-based) can simplify GDPR-aligned handling for European clients. |
| + | Predictive analytics and NLP expertise predates the current generative AI wave. |
| + | More than a decade of track record in a narrower, more defensible specialty. |
| - | Reported team size varies close to 3x across public sources |
| - | Less generative AI-specific public case work than agencies built specifically around that |
Who should choose Markovate?
A typical fit: turning a generative AI concept into a shipped product with a small, senior team.
AI-exclusive focus dating to 2015, ahead of the current generative AI cycle. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Retail & e-commerce, Logistics.
Who should choose InData Labs?
A typical fit: building a generative AI feature on top of an existing data warehouse.
Data-science-first heritage predating the generative AI branding wave. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Gaming, Fintech, Healthcare.
Decision matrix: Markovate vs InData Labs
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Markovate |
| You need a large dedicated team for an ongoing programme | Markovate |
| Your budget is at the lower end | Compare: Markovate (Not disclosed) vs InData Labs (Not disclosed) |
| You need specialist depth in a specific vertical | Markovate |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Markovate vs InData Labs
| Use case | Markovate fit | InData Labs fit | Winner |
|---|---|---|---|
| Turning a generative AI concept into a shipped product with a small, senior team. | Strong | Limited | Markovate |
| Getting a fast generative AI prototype built before deciding on an in-house hire. | Strong | Limited | Markovate |
| Building a generative AI feature on top of an existing data warehouse. | Limited | Strong | InData Labs |
| Adding computer vision alongside generative AI to a product with image or video data. | Limited | Strong | InData Labs |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Markovate vs InData Labs
Markovate (4.5/5) is the stronger overall choice for most Generative AI Development projects. AI-exclusive focus dating to 2015, ahead of the current generative AI cycle.
InData Labs (4.1/5) is worth a look if you need adding computer vision alongside generative AI to a product with image or video data. If your situation matches that, InData Labs is a competitive option.
Related comparisons
Markovate vs InData Labs FAQ
Is Markovate better than InData Labs?
Markovate (4.5/5) scores higher overall, but "better" depends on your use case. Markovate's strongest advantage: ten years of AI-only positioning predates most competitors' generative AI pivot. InData Labs's strongest advantage: Founder's gaming background brings real-time data processing experience to computer vision work.
How do Markovate and InData Labs differ in pricing?
Markovate uses fixed project or dedicated team pricing. InData Labs uses fixed project or dedicated team pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Markovate or InData Labs?
Markovate 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 Markovate and InData Labs?
Markovate's primary differentiator is: AI-exclusive focus dating to 2015, ahead of the current generative AI cycle. InData Labs's primary differentiator is: data-science-first heritage predating the generative AI branding wave. They also differ in team size (51-200 vs 51-200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Fintech, Healthcare vs Retail & e-commerce, Gaming).
Verify all details directly with each company before making a decision.