# Algomine vs Sparkbit

> Two top-rated AI Consulting agencies in Warsaw. Side by side: published rates, team size, service and industry coverage, and the curator's assessment of both — pairings are never sold.

Canonical page: https://www.aiagencies.eu/compare/algomine-vs-sparkbit
Generated: 2026-09-23 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Algomine | Sparkbit |
|---|---|---|
| Editorial score | 64/100 | 64/100 |
| Hourly rate | €45-85/hr (directory-listed) | €85-130/hr (directory-listed) |
| Team size | 50-249 | 10-49 |
| Location | Warsaw, Poland | Warsaw, Poland |
| Founded | 2015 | 2014 |
| Last reviewed | 2026-09-09 | 2026-09-23 |
| Services | AI Consulting, AI Development, Generative AI | AI Consulting, AI Development |
| Industries | FinTech, Manufacturing, E-commerce | Healthcare, Manufacturing, E-commerce |

## Our take on Algomine
Reviewed 2026-09-09 by Gabor Kiss against published evidence.

The multi-agent engagement is the one that shows the shape of the practice. For a Swedish industrial group of more than 15,000 people operating in over a hundred countries, Algomine ran two parallel workstreams for more than two years: maintaining two production AI applications, a document analyzer and a configuration assistant for bespoke heat exchangers, while building the greenfield multi-agent platform meant to replace a hard-coded chatbot the client had bought elsewhere.

The reasons the client gives are worth reading, because they are the reasons enterprises actually change supplier. Siloed applications with no shared governance, rigid predefined workflows that made every new use case expensive, and AI spend concentrating inside a single vendor ecosystem.

Twenty case studies are published across generative research assistants, retail planogram generation, computer vision for pharmacy shelves and defect management in precision manufacturing. Eleven name their client, and the client list runs to PKO Bank Polski, mBank, Rossmann, Eurocash and Warta.

Algomine publishes no rate itself; the €45–85/h shown here comes from a third-party directory listing. The weaker fit is a buyer who needs a published outcome figure, because the studies explain architecture and engagement shape rather than reporting numbers.

### Key strengths
- The industrial engagement is documented as a two-year, two-workstream relationship covering legacy maintenance and greenfield platform work at the same time
- Vendor lock-in is treated as an architectural requirement, with the platform built to be model and vendor agnostic after the client's costs concentrated in one ecosystem
- Twenty published case studies with eleven naming the client, across banking, retail, pharmacy and precision manufacturing

### Good to know
- The flagship multi-agent client is anonymized despite being identifiable from the description—ask for a speakable reference on it
- The studies explain architecture rather than reporting results; ask what the platform changed in cost, cycle time or adoption
- Named clients like PKO Bank Polski and Rossmann carry no described engagement—ask what was delivered for the two closest to your sector

## Our take on Sparkbit
Reviewed 2026-09-23 by Gabor Kiss against published evidence.

Sparkbit publishes ML case studies with the model, the metric and the handover spelled out: for 5x5 Technologies it retrained a YOLOv8 tower-equipment detector from F1 0.72 to 0.86 over a 12-month engagement on a telecom digital-twin platform, and for the New York health-tech startup phy it built a posture-analysis core covering 20+ conditions whose algorithms went into the client's FDA application.

The studies also show where work stopped: the anti-counterfeit label project ended after discovery when the findings argued against continuing, and the context-aware telematics system was a grant-funded R&D program whose hardware rollout, the study says, was held back by the supply chain.

Most dated AI work falls in 2020–2022; the LLM-era record is an anonymized RAG assistant over 7,000+ PDFs for a Swiss device manufacturer (2025), described as in production, and an Italist data-quality pipeline whose GPT-4 stack sits under a 2020–2022 label.

The weaker fit is buyers who need business outcomes measured in money or hours—results here are model metrics and qualitative gains—or a named reference for a recent LLM deployment.

### Key strengths
- Model metrics are published with the stack: F1 0.72 to 0.86 at 5x5, about 0.85–0.95 F1 on key telematics perception functions, and ~8k concurrent users sustained with under 0.002% timeouts
- Five client executives are quoted by name and title—including 5x5 Technologies' CEO, Italist's CTO and Spicerr's CEO—four of them on the case study their quote refers to
- The services page lists what is handed over—repos, infrastructure-as-code, test suite, dashboards, runbooks and IP assignment—and the 5x5 study reports that handover took place

### Good to know
- No founder or engineer is named anywhere on the site—ask who would lead your project and to see that person's prior ML work
- The Italist study lists GPT-4 while the index dates it 2020–2022 and files it under Health-tech—ask for the dates and scope of the LLM work specifically
- Outcomes such as "duplicates down" and "operational savings" carry no figures—ask each reference what the system changed in hours, cost or error rate

## Which to choose
Choose Algomine for:
- a more cost-effective engagement (~40% lower rate)
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in FinTech
- broader service offering — also covers Generative AI

Choose Sparkbit for:
- closer collaboration at a smaller team scale
- disclosed specialization in Healthcare

## Questions buyers ask

### Which is cheaper, Algomine or Sparkbit?
Algomine is cheaper by roughly 40% — €65/hr median vs €108/hr at Sparkbit. Published rates cover different things at different agencies — seniority mix, discovery and included revisions are the usual variables — so treat the gap as a question to ask, not a verdict.

### Which has more Manufacturing experience, Algomine or Sparkbit?
Both agencies show documented Manufacturing work and have similar industry breadth. Compare directly on the agency profiles: Algomine and Sparkbit.

### Which scores higher overall, Algomine or Sparkbit?
Both score equally well overall (64/100). The deciding factor is specialization — see the editorial quotes and Strengths sections above. Full methodology.

### Which is faster to engage, Algomine or Sparkbit?
Neither publishes lead times, so this is a read on team size rather than a measured answer. Sparkbit runs the smaller team, which usually means fewer procurement gates and a shorter path to kickoff. Algomine runs a larger one, which tends to mean more steps but more capacity to start parallel workstreams. Current kickoff availability is the number that actually decides it — ask both.

### When should I consider both Algomine and Sparkbit?
Consider running parallel discovery briefs with Algomine and Sparkbit if your project spans multiple workstreams, you want competitive proposals to compare scope and approach, or you're undecided between the specialization angles each brings (see the Best for cards above). Most engagements ultimately go with one — but the parallel-brief phase is a low-cost way to validate fit.

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Source: AIAgencies.eu — the curated register of European AI agencies. Rate provenance: "agency-confirmed" = disclosed via submission or claimed profile; "directory-listed" = Clutch or two agreeing B2B directories (lone unverified sources are never written). Full method: https://www.aiagencies.eu/methodology · Rate index: https://www.aiagencies.eu/rates