Turing
AI-vetted talent platform connecting companies with ML engineers from a 4M+ developer network since 2018.
What is Turing?
Turing was founded in 2018 by Jonathan Siddharth and Rohan Aroe and is headquartered in Palo Alto, California. The company operates as an AI-powered talent marketplace and technology services firm with a network of 4M+ vetted software engineers, data scientists, and STEM experts. Turing has raised $247M at a $2.2B valuation from WestBridge Capital and Foundation Capital, and serves 1,000+ clients including Fortune 500 companies and governments. Note: Turing is primarily a talent marketplace — clients provide direction; Turing supplies vetted engineers rather than owning ML delivery outcomes. (Funding, valuation, and client count per Turing official website and Crunchbase.)
Turing was founded in 2018 and is headquartered in Palo Alto, CA. The firm employs 6,859 people and works primarily with clients in saas, fintech, healthcare, retail, financial sectors. Its primary differentiator is: AI-vetted 4M+ developer network — fastest route to pre-screened ML talent for staff augmentation.
Turing tech stack and services
| Service area |
|---|
| Staff Aug |
| ML Consulting |
| Custom ML Build |
Turing use cases
Short answer: Turing is best suited for companies needing rapid, vetted ML staff augmentation.
| Use case |
|---|
| Staff augmentation for ML engineering team |
| Rapid placement of vetted data scientists |
| Dedicated ML engineer for product team |
| Team extension for ongoing ML system maintenance |
| Scaling ML team during growth sprint |
Turing pricing
Short answer: Turing uses a dedicated team, t&m pricing approach. Minimum engagement is not publicly disclosed; a discovery call is required.
| Engagement model | Typical range | Best for |
|---|---|---|
| T&M | Variable; depends on team size | Large programmes or team augmentation |
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
Turing pros and cons
| Advantages | Things to consider |
|---|---|
| +4M+ AI-vetted engineers — largest pre-screened ML talent pool in the category | -Talent marketplace model — Turing supplies engineers; client provides direction and owns outcomes |
| +$2.2B valuation with $247M raised — stable platform with institutional backing | -Less suited to projects needing a delivery firm with end-to-end accountability |
| +1,000+ clients including Fortune 500 and government organisations | -Delivery quality depends on client PM capability — not owned by Turing |
| +Fastest path to pre-screened ML engineer placement |
Turing vs alternatives
How Turing compares to the other top Machine Learning agencies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market teams, full-lifecycle ML ownership. | Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team | 4.8 | Full comparison |
| InData Labs | Fintech, healthcare, SaaS — production ML with GenAI/RAG. | Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries | 4.6 | Full comparison |
| Artefact | Large enterprises, industrial-scale ML and data strategy. | Enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm | 4.5 | Full comparison |
| N-iX | Enterprise teams, ML plus cloud engineering, European delivery. | 2,400+ engineers covering ML, cloud, and data under one firm — strong for large multi-track programmes | 4.4 | Full comparison |
| Sigmoid | Fortune 500 retail/CPG/financial firms, AI-first data platforms. | Sequoia-backed AI and data engineering specialist with a Fortune 500 client portfolio in retail and CPG | 4.3 | Full comparison |
| Scopic | Healthcare, fintech enterprises — genuinely custom ML. | 20-year track record of custom ML engineering across 1,000+ projects — no API-wrapper shortcuts | 4.2 | Full comparison |
| Miquido | Product companies, ML embedded in mobile/web products. | AI-plus-product development — ML capabilities integrated with UX engineering, not delivered as a standalone model | 4.2 | Full comparison |
| NineTwoThree AI Studio | Mid-market scale-ups, boutique AI/ML studio. | Inc. 5000 AI studio with Clutch Top 50 ranking — boutique delivery model with direct principal access | 4.1 | Full comparison |
| RTS Labs | US finance/healthcare firms, pilot-to-production AI. | Pilot-to-production ML with deep data engineering roots — Snowflake, Azure, and AWS native | 4.1 | Full comparison |
| SciForce | Production NLP/CV systems, cost-effective Eastern Europe. | End-to-end ML delivery — from requirements to post-launch support — with NLP and computer vision depth | 4.0 | Full comparison |
| LeewayHertz | Enterprise clients, AI engineering, Hackett-backed. | Backed by The Hackett Group since Sept 2024 — AI engineering within an enterprise transformation consulting firm | 4.0 | Full comparison |
| DATAFOREST | US/EU companies, affordable AI, verified Clutch ratings. | 4.9-star Clutch rating across 27 verified reviews — one of the highest-rated AI firms in Eastern Europe | 4.0 | Full comparison |
| Kanerika | US enterprises, AI strategy plus data engineering. | Enterprise data-to-value specialist — ML consulting plus data integration and process automation in one engagement | 4.0 | Full comparison |
| DataArt | Enterprises, established firm, fintech/travel ML depth. | 1997-founded, 5,700-engineer global firm — enterprise scale and continuity across ML and software in fintech and travel | 3.9 | Full comparison |
| ELEKS | Enterprise clients, ML within full-service consulting. | 30+ years of enterprise software delivery — ML within a stable, large-org structure for risk-averse buyers | 3.9 | Full comparison |
| Yalantis | Healthcare, fintech — compliance-aware ML plus IoT. | Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs | 3.9 | Full comparison |
| Avenga | European enterprises, large-scale ML transformation. | Formed from a 2019 merger — 3,800+ engineers across Europe for large ML and digital transformation programmes | 3.9 | Full comparison |
| Intellectsoft | Fortune 500, AI modernisation of legacy systems. | AI modernisation specialist for Fortune 500 mission-critical systems — legacy transformation, not greenfield | 3.8 | Full comparison |
| Azumo | US companies, cost-effective Latin American nearshore ML. | Latin American nearshore delivery — US time-zone alignment with rates below fully on-shore alternatives | 3.8 | Full comparison |
| Iflexion | Enterprises, ML within software modernisation. | 25 years of software delivery with ML integrated — 800+ clients provide a verified delivery track record | 3.8 | Full comparison |
| Altamira | Companies, production-ready AI agents from day one. | AI-native product-build firm — delivers fully integrated, trained AI agents ready for production from day one | 3.8 | Full comparison |
| Maruti Techlabs | Mid-market companies, cost-effective US/India ML delivery. | Dual US-India delivery with AWS Marketplace listing — cost-effective ML for mid-market budgets | 3.8 | Full comparison |
| Keyrus | International enterprises, industrial-AI implementation. | From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations | 3.8 | Full comparison |
| Itransition | Global enterprises, ML within full software delivery. | 25+ years of full-cycle delivery to 30+ countries — ML within a large proven software engineering organisation | 3.8 | Full comparison |
| Acropolium | SaaS startups, ML features in custom product builds. | 22 years of bespoke product engineering — ML as a product feature, not a standalone model delivery | 3.8 | Full comparison |
| Kanda Software | Healthcare, pharma — compliance-aware AI development. | Regulatory-domain ML specialist — AI for pharma and healthcare with compliance and IP ownership built in | 3.7 | Full comparison |
| Binariks | Companies, cost-effective ML with cloud/IoT integration. | Multi-cloud and IoT-integrated ML delivery — AWS, GCP, and Azure with IoT sensor data pipelines | 3.7 | Full comparison |
| Centric Consulting | US enterprises, ML within business transformation. | Business-outcome ML consulting — AI within management transformation, not pure technology delivery | 3.7 | Full comparison |
| Space-O Technologies | Startups/SMBs, accessible ML in healthcare/e-commerce. | Budget-accessible ML for startups — low minimum engagement with India-based rate advantage | 3.7 | Full comparison |
| Modak | Large enterprises, AI-driven data modernisation. | ML-powered data engineering — uses ML itself to accelerate data prep and modernisation at enterprise scale | 3.7 | Full comparison |
Turing FAQ
What is Turing?
Turing was founded in 2018 by Jonathan Siddharth and Rohan Aroe and is headquartered in Palo Alto, California. The company operates as an AI-powered talent marketplace and technology services firm with a network of 4M+ vetted software engineers, data scientists, and STEM experts. Turing has raised $247M at a $2.2B valuation from WestBridge Capital and Foundation Capital, and serves 1,000+ clients including Fortune 500 companies and governments. Note: Turing is primarily a talent marketplace — clients provide direction; Turing supplies vetted engineers rather than owning ML delivery outcomes. (Funding, valuation, and client count per Turing official website and Crunchbase.)
How much does Turing charge?
Turing uses dedicated team, t&m pricing. Minimum engagement is not publicly disclosed. A discovery call is required to get project-specific quotes.
What tech stack does Turing use?
Turing works with Python, TensorFlow, PyTorch, AWS, Azure, Google Cloud, scikit-learn. Primary industries served include saas, fintech, healthcare, retail, financial.
Is Turing right for enterprise?
Companies needing rapid, vetted ML staff augmentation. 6,859 team size. Key consideration: Talent marketplace model — Turing supplies engineers; client provides direction and owns outcomes.
What are the best Turing alternatives?
The best alternatives to Turing depend on your use case. Top options are:
- Tensorway: full-lifecycle ml ownership — model design, training infrastructure, and drift monitoring in one team
- InData Labs: deep ml and genai specialist with 10+ years of production deployments across regulated industries
- Artefact: enterprise ml at 1,500-consultant scale across 26 countries — strategy, deployment, and ai factory in one firm