Addepto
Warsaw AI consultancy behind an open-source LLM testing tool, now owned by KMS Technology
What is Addepto?
Addepto was founded in Warsaw in 2017 and was acquired in December 2025 by KMS Technology, an Atlanta engineering company backed by Sunstone Partners. It built ContextCheck, an open-source framework for testing language models and chatbots, which says something about how it approaches quality before release. Its chief executive says 97% of staff are AI engineers (per company website; independently unverifiable). Clutch lists $50–$99 an hour and a $10,000+ minimum project.
Addepto works primarily with clients in Manufacturing, Aviation, Logistics, Retail, Financial services sectors. Its primary differentiator is: Open-source LLM testing (ContextCheck) and a low $10,000+ Clutch entry point.
Addepto tech stack and services
| Service area |
|---|
| Evaluation & Regression Testing |
| MLOps & Model Serving |
| Data Engineering |
| RAG & Enterprise Search |
Addepto pricing
Short answer: Addepto prices its work as follows: project-based; $50–$99/hr (Clutch band). Minimum engagement starts at $10,000+ (Clutch).
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed-scope project | From $10,000+ (Clutch) | A well-defined scope |
| Time & materials | Hourly; see pricing model above | Scope that will change as you learn |
| Dedicated team | Monthly team rate; not public | Continuing the build after a pilot |
Addepto pros and cons
| Advantages | Things to consider |
|---|---|
| +ContextCheck shows real investment in testing language models | -Acquired by KMS Technology in December 2025, so teams and priorities may change |
| +A $10,000+ Clutch minimum makes a small first project possible | -No managed-run service listed |
| +Databricks experience for data-heavy builds | -Less depth on staged rollouts than the leaders |
| +Team is almost all engineers |
Addepto vs alternatives
How Addepto compares to the other top AI Implementation providers.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Teams whose AI pilot works but can't ship. | Model gateway with routing, provider fallback, and regression tests, released through a canary rollout | 4.8 | Full comparison |
| Provectus | AWS shops taking GenAI pilots to production. | AWS Premier Tier MLOps and generative AI engineering on Bedrock and SageMaker | 4.6 | Full comparison |
| Grid Dynamics | Enterprises moving several AI pilots to production. | Forward-deployed engineers working from ready-made GAIN platforms for agentic and retail AI | 4.5 | Full comparison |
| deepsense.ai | Teams that need measured accuracy before launch. | Evaluation-first engineering that proves accuracy on your data before release | 4.5 | Full comparison |
| Neurons Lab | Banks and insurers shipping AI on AWS. | Financial-services AI built inside existing compliance controls on AWS | 4.4 | Full comparison |
| Datatonic | Google Cloud users needing MLOps and managed run. | Vertex AI MLOps with a managed service to run models after launch | 4.4 | Full comparison |
| Quantiphi | Large document and contact-center AI programs. | Large Google Cloud and AWS engineering bench for document and speech AI in production | 4.3 | Full comparison |
| ML6 | European firms needing EU-compliant production AI. | European AI engineering with EU AI Act and GDPR requirements built into delivery | 4.3 | Full comparison |
| Thoughtworks | Engineering orgs wanting disciplined AI delivery. | Continuous delivery practice applied to models and prompts, with tool views published in its Technology Radar | 4.3 | Full comparison |
| N-iX | Mid-size and large firms wanting nearshore MLOps. | Dedicated MLOps practice backed by a large nearshore engineering bench | 4.2 | Full comparison |
| DS Stream | Teams wanting packaged LLMOps at nearshore rates. | Packaged LLMOps setup with evaluation, prompt versioning, guardrails, and model routing | 4.2 | Full comparison |
| RTS Labs | US mid-market firms on a small budget. | The lowest entry point on this page, with a committed ship date for production code | 4.1 | Full comparison |
| EPAM Systems | Enterprises adding AI across many products. | Engineering capacity to put AI into dozens of products under one program | 4.1 | Full comparison |
| Accenture | Multinationals rolling AI out across many countries. | Staffing and compliance depth to run production AI in many countries at once | 4.1 | Full comparison |
| Winder.ai | Teams needing senior MLOps advice and review. | Senior MLOps expertise from a recognized author, at boutique scale | 4.0 | Full comparison |
| NineTwoThree AI Studio | Funded companies launching an AI product. | Product design and engineering together for customer-facing AI launches | 4.0 | Full comparison |
| Globant | US firms wanting nearshore AI engineering. | Nearshore Latin American engineering teams in US time zones | 4.0 | Full comparison |
| SoftServe | Existing clients extending engineering into AI. | Google Cloud Premier engineering firm with an MLOps and generative AI practice | 4.0 | Full comparison |
| Capgemini | European multinationals pairing AI with outsourcing. | Production AI bundled with outsourced business operations after the WNS deal | 3.9 | Full comparison |
| InData Labs | Mid-size firms with one focused AI project. | Discovery, proof of concept, and production build from a small data science team | 3.9 | Full comparison |
| Instinctools | Palantir users building agentic workflows. | Palantir Foundry and AIP implementation alongside custom software engineering | 3.9 | Full comparison |
| Netguru | Product teams adding AI features to apps. | AI features shipped as part of web and mobile product work | 3.9 | Full comparison |
| STX Next | Python teams needing extra AI engineers. | Python engineering depth for AI that lives inside your codebase | 3.9 | Full comparison |
| 10Clouds | Smaller banks and lenders adding AI. | A dedicated financial-institutions AI unit with its own AIConsole platform | 3.8 | Full comparison |
| LeewayHertz | Buyers wanting a platform-based AI build. | Builds on its own ZBrain platform, now backed by The Hackett Group | 3.7 | Full comparison |
Addepto FAQ
What is Addepto?
Warsaw AI consultancy behind an open-source LLM testing tool, now owned by KMS Technology
How much does Addepto charge?
Pricing model: project-based; $50–$99/hr (Clutch band). Minimum engagement starts at $10,000+ (Clutch). A discovery call is required to get project-specific quotes.
What tech stack does Addepto use?
Addepto works with Databricks, Azure, AWS, Python, LangChain. Primary industries served include Manufacturing, Aviation, Logistics, Retail, Financial services.
Is Addepto right for enterprise?
Best for: mid-size firms wanting tested LLM apps cheaply. Team size: 50–249. Key consideration: Acquired by KMS Technology in December 2025, so teams and priorities may change.
What are the best Addepto alternatives?
The best alternatives to Addepto depend on your use case. Top options are:
- Tensorway: model gateway with routing, provider fallback, and regression tests, released through a canary rollout
- Provectus: AWS Premier Tier MLOps and generative AI engineering on Bedrock and SageMaker
- Grid Dynamics: forward-deployed engineers working from ready-made GAIN platforms for agentic and retail AI
Compare Addepto with other AI Implementation providers
Verify all details directly with Addepto before making a decision.