Quantiphi
AI-first engineering firm with top Google Cloud, AWS, and NVIDIA partner tiers
What is Quantiphi?
Quantiphi was founded in 2013 and is headquartered in Marlborough, Massachusetts, with around 4,000 employees. Its own materials claim top partner tiers with Google Cloud, AWS, and NVIDIA, and ISG named it a Leader for AI and machine learning services on Google Cloud in its 2024 Provider Lens report. It has a large bench for document, speech, and contact-center AI in production, especially in healthcare, insurance, and the public sector. Big programs suit it better than one small pilot.
Quantiphi works primarily with clients in Healthcare, Insurance, Financial services, Public sector, Media sectors. Its primary differentiator is: Large Google Cloud and AWS engineering bench for document and speech AI in production.
Quantiphi tech stack and services
| Service area |
|---|
| Pilot to Production |
| MLOps & Model Serving |
| RAG & Enterprise Search |
| Google Cloud Partner |
| AWS Partner |
Quantiphi pricing
Short answer: Quantiphi prices its work as follows: project and dedicated-team pricing; rates not published. Minimum engagement is not publicly disclosed; a discovery call is required.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed-scope project | Quoted per project | A well-defined scope |
| Dedicated team | Monthly team rate; not public | Continuing the build after a pilot |
| Managed services | Monthly fee; not public | Running and monitoring live systems |
Quantiphi pros and cons
| Advantages | Things to consider |
|---|---|
| +ISG's 2024 Provider Lens rated its Google Cloud AI work a Leader | -Partner-tier claims come mostly from its own recruiting material |
| +Large team for document, speech, and contact-center systems | -Big-program orientation makes small pilots relatively costly |
| +Experience with healthcare and public-sector data rules | -Rates not published |
| +Covers both Google Cloud and AWS |
Quantiphi vs alternatives
How Quantiphi 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 |
| 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 |
| Addepto | Mid-size firms wanting tested LLM apps cheaply. | Open-source LLM testing (ContextCheck) and a low $10,000+ Clutch entry point | 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 |
Quantiphi FAQ
What is Quantiphi?
AI-first engineering firm with top Google Cloud, AWS, and NVIDIA partner tiers
How much does Quantiphi charge?
Pricing model: project and dedicated-team pricing; rates not published. Minimum engagement is not publicly disclosed. A discovery call is required to get project-specific quotes.
What tech stack does Quantiphi use?
Quantiphi works with Google Cloud, Vertex AI, AWS, NVIDIA, Snowflake. Primary industries served include Healthcare, Insurance, Financial services, Public sector, Media.
Is Quantiphi right for enterprise?
Best for: large document and contact-center AI programs. Team size: 4,000. Key consideration: Partner-tier claims come mostly from its own recruiting material.
What are the best Quantiphi alternatives?
The best alternatives to Quantiphi 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 Quantiphi with other AI Implementation providers
Verify all details directly with Quantiphi before making a decision.