InData Labs vs SPD Technology: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.4/5) edges ahead of SPD Technology (3.9/5) overall. InData Labs is the better choice for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. SPD Technology is the stronger option for fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs SPD Technology: head-to-head summary
| Criterion | InData Labs | SPD Technology |
|---|---|---|
| Founded | 2014 | 2006 |
| HQ | Nicosia, Cyprus (R&D and delivery centers in Lithuania and the US) | London, United Kingdom |
| Team size | 80+ | 650+ |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Best for | Companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop. | Fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience. |
| Pricing model | Fixed project, Time & Materials | Fixed project, dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Generative AI/GPT tooling, Computer vision frameworks | Python, OpenAI API, Anthropic API |
| Industries served | Cross-industry, Predictive Analytics | Fintech, Financial Services |
InData Labs vs SPD Technology: overview
InData Labs
InData Labs is a data science and AI consultancy legally headquartered in Nicosia, Cyprus, founded in 2014 by video-gaming industry veteran Marat Karpeko, with R&D and delivery centers in Lithuania and the US. The 80+ person firm runs its own R&D center and covers a wide technical band from generative AI and GPT integration through predictive analytics, forecasting, and computer vision. Its Cyprus legal HQ gives clients an EU-entity contracting structure alongside nearshore delivery capacity.
SPD Technology
SPD Technology is a London, UK software product development company founded in 2006, with 650+ engineers across 30+ countries and 460+ delivered custom projects. It secured direct partnerships with OpenAI, Anthropic, and AWS specifically to reinforce its cloud and AI/ML capabilities, serving fintech, digital payments, and data-engineering clients including PitchBook, Morningstar, and Blackhawk Network.
Services and capabilities: InData Labs vs SPD Technology
| Capability | InData Labs | SPD Technology |
|---|---|---|
| ML Development | ✓ | ✓ |
| AI Consulting | ✓ | ✗ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| MLOps | ✗ | ✓ |
| Data Engineering | ✓ | ✗ |
| Staff Augmentation | ✗ | ✓ |
Tech stack comparison: InData Labs vs SPD Technology
| Framework / platform | InData Labs | SPD Technology |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | N/A | ✓ |
| Microsoft Azure | N/A | N/A |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | N/A |
| PyTorch | N/A | N/A |
| LangChain | N/A | N/A |
| Databricks | N/A | N/A |
Pricing comparison: InData Labs vs SPD Technology
| Criterion | InData Labs | SPD Technology |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Time & Materials | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: InData Labs vs SPD Technology
| Dimension | InData Labs | SPD Technology |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Cross-industry, Predictive Analytics | Fintech, Financial Services |
| Best use cases | Generative AI and GPT integration projects, Predictive analytics and forecasting | Fintech and payments platform AI features, OpenAI/Anthropic-based generative AI integrations |
| Typical project type | Fixed project | Fixed project |
InData Labs vs SPD Technology: pros and cons
| InData Labs | |
|---|---|
| + | Founded 2014 — one of the longer-running boutique data science firms in this list |
| + | In-house R&D center is a differentiator versus pure staff-augmentation vendors |
| + | Cyprus legal HQ with Lithuania/US delivery centers gives EU-entity contracting plus nearshore delivery |
| + | Broad technical range from generative AI to classic forecasting and computer vision |
| - | 80+ employee band is imprecise — exact current headcount is not independently published |
| - | Legal HQ (Cyprus) is a smaller AI hub than its Lithuania delivery center, which may matter to buyers wanting an on-the-ground presence |
| - | Pricing model and minimum engagement are not published |
| SPD Technology | |
|---|---|
| + | Direct partnerships with OpenAI and Anthropic, in addition to AWS, are a distinctive and verifiable technology relationship |
| + | 650+ engineers across 30+ countries and 460+ delivered custom projects show significant scale and reach |
| + | Notable enterprise clients including PitchBook, Morningstar, and Blackhawk Network |
| + | London HQ combined with globally distributed delivery centers balances local client access with cost-effective delivery |
| - | AI/ML is one of several practices (fintech, payments, data engineering, cloud) rather than the company's sole focus |
| - | 650+ person, 30+ country delivery footprint can mean variable team consistency across engagements |
| - | Founded 2006 as a general software product company — AI/ML partnerships are a comparatively recent strategic addition |
Who should choose InData Labs?
InData Labs is the right choice for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision.. Minimum engagement starts at Not published. Works best with clients in Cross-industry, Predictive Analytics.
Who should choose SPD Technology?
SPD Technology is the right choice for fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience..
Secured direct partnerships with OpenAI, Anthropic, and AWS specifically to reinforce its cloud and AI/ML capabilities — a more direct foundation-model-vendor relationship than most peers on this list disclose.. Minimum engagement starts at Not published. Works best with clients in Fintech, Financial Services.
Decision matrix: InData Labs vs SPD Technology
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | SPD Technology |
| Your budget is at the lower end | Compare: InData Labs (Not published) vs SPD Technology (Not published) |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | SPD Technology |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs SPD Technology
| Use case | InData Labs fit | SPD Technology fit | Winner |
|---|---|---|---|
| Generative AI and GPT integration projects | Strong | Strong | Both equally |
| Predictive analytics and forecasting | Strong | Limited | InData Labs |
| Fintech and payments platform AI features | Limited | Strong | SPD Technology |
| OpenAI/Anthropic-based generative AI integrations | Limited | Strong | SPD Technology |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: InData Labs vs SPD Technology
InData Labs (4.4/5) is the stronger overall choice for most Machine Learning Development projects. Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision.. It is best for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
SPD Technology (3.9/5) is the better choice when fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience.. If your situation matches those criteria, SPD Technology is a competitive option.
Related comparisons
InData Labs vs SPD Technology FAQ
Is InData Labs better than SPD Technology?
InData Labs (4.4/5) scores higher overall, but "better" depends on your use case. InData Labs is better for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. SPD Technology is better for fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience..
How do InData Labs and SPD Technology differ in pricing?
InData Labs uses fixed project, time & materials pricing with a minimum engagement of Not published. SPD Technology uses fixed project, dedicated team pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: InData Labs or SPD Technology?
SPD Technology 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 InData Labs and SPD Technology?
InData Labs's primary differentiator is: runs its own r&d center rather than purely project-based delivery, spanning generative ai/gpt integration through classic predictive analytics and computer vision.. SPD Technology's primary differentiator is: secured direct partnerships with openai, anthropic, and aws specifically to reinforce its cloud and ai/ml capabilities — a more direct foundation-model-vendor relationship than most peers on this list disclose.. They also differ in team size (80+ vs 650+), minimum engagement (Not published vs Not published), and primary industries served (Cross-industry, Predictive Analytics vs Fintech, Financial Services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.