Tredence
US data science and applied ML firm known for closing the gap between models and measurable business outcomes.
Founded 2013 | San Jose, CA, USA | 4,200+ employees
custom-mlpredictive-analyticsdata-engineeringml-consultingmlops
What is Tredence?
Tredence is a data science and AI engineering company founded in 2013 and headquartered in San Jose, California. The company has grown to 4,200+ employees and specializes in applied ML, data engineering, and industry-specific AI accelerators. Tredence is particularly known for last-mile ML adoption — operationalizing data science outputs into measurable operational improvements in supply chain, retail, and healthcare. The firm bridges the gap between insights delivery and value realization.
Tredence was founded in 2013 and is headquartered in San Jose, CA, USA. The firm employs 4,200+ people and works primarily with clients in retail, manufacturing, supply chain, healthcare, financial services sectors. Its primary differentiator is: Industry-specific AI accelerators and a proven focus on last-mile ML adoption, closing the execution gap between data science output and real business value.
Tredence tech stack and services
PythonRApache SparkDatabricksAWS SageMakerAzure MLSnowflakedbtPower BITensorFlowScikit-Learn
| Service area |
|---|
| Custom ML Development |
| Predictive Analytics |
| Data Engineering |
| ML Consulting |
| MLOps |
Tredence use cases
Short answer: Tredence is best suited for Enterprises, last-mile ML adoption, supply chain and retail.
| Use case |
|---|
| Supply chain demand forecasting and inventory optimization ML model deployment |
| Customer analytics and churn prediction for retail or SaaS platforms |
| Healthcare ML for clinical decision support and patient outcome prediction |
| Data platform modernization to enable enterprise-scale ML workloads |
| ML model monitoring and retraining automation via MLOps pipeline implementation |
Tredence pricing
Short answer: Tredence uses a dedicated team, t&m, fixed project pricing approach. Minimum engagement starts at $50K.
| Engagement model | Typical range | Best for |
|---|---|---|
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
| T&M | Variable; depends on team size | Large programmes or team augmentation |
| Fixed project | From $50K | Well-defined scope |
Tredence does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
Tredence pros and cons
| Advantages | Things to consider |
|---|---|
| +Industry-specific ML accelerators reduce time-to-value compared to greenfield custom development | -Higher minimum engagement ($50K) limits accessibility for early-stage or SMB clients |
| +4,200+ team provides large-scale ML engineering capacity for enterprise programmes | -Generalist enterprise size means specialist ML depth may vary by team assignment |
| +Strong track record closing the gap between model development and operational adoption | -Less boutique flexibility than smaller ML-only firms for novel or research-adjacent problems |
| +Deep supply chain and retail ML expertise with verifiable production deployments | |
| +US HQ with onshore client management and offshore delivery model |
Tredence vs alternatives
How Tredence compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Dedicated ML boutique, strategy through production MLOps. | ML-only focus with a dedicated specialist team backed by 25 years of the parent company software delivery infrastructure — unusually deep for a firm of this size | 4.8 | Full comparison |
| LeewayHertz | Enterprises, end-to-end AI delivery, Fortune 500 clients. | Product-centric AI delivery culture with verified Fortune 500 client references including ESPN, Siemens, and 3M — now operating within The Hackett Group | 4.0 | Full comparison |
| InData Labs | Mid-market orgs, complex ML, deep data-science expertise. | Pure-play ML boutique with a measurably higher specialist-to-generalist ratio than typical service firms, confirmed by Clutch as a top AI service provider | 4.5 | Full comparison |
| HatchWorks AI | Companies wanting AI-native, generative-AI-embedded delivery. | Clutch #1 AI Services Company with a proprietary Generative Driven Development methodology claimed to reduce delivery time by 30–50% (per company website; independently unverifiable) | 4.4 | Full comparison |
| STX Next | Orgs needing ML operationalized in Python-native systems. | Europe's largest Python-specialist firm uniquely positioned to embed ML into production software without the integration friction that plagues pure-play ML boutiques | 4.3 | Full comparison |
| Addepto | Finance, energy, retail — bespoke ML with pipeline... | End-to-end AI/ML delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability | 4.2 | Full comparison |
| DataForest | Data-first companies, robust engineering foundation for ML. | Data engineering-first approach builds pipeline and data quality foundations before model development, addressing the root cause of most ML project failures | 4.2 | Full comparison |
| Forte Group | Orgs wanting Tier-1 rigor, specialist agility, roadmap to... | Structured AI service lines with Tier 1 delivery rigor and specialist consultancy agility — serving organizations that need both without enterprise-tier pricing | 4.1 | Full comparison |
| Binariks | Healthcare, fintech, insurance — compliance-first ML engineering. | Compliance-first ML engineering for regulated industries — governance and audit trails are built in from the architecture stage, not retrofitted after launch | 4.1 | Full comparison |
| Softeq | Hardware and industrial companies, edge ML on embedded... | Unique capability to combine hardware design expertise with ML engineering, deploying models at the edge where cloud-only ML firms cannot operate | 4.1 | Full comparison |
| Markovate | Retail, travel, fitness — recommendation engines, 300+ projects. | 300+ delivered projects spanning recommendation systems, computer vision, and dynamic pricing, with deeper consumer-facing ML specialization than most comparably sized firms | 4.0 | Full comparison |
| ScienceSoft | Established enterprises, 35+ years, stable US vendor. | 35+ years of enterprise delivery experience with a mature ML practice — providing compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused competitors | 4.0 | Full comparison |
| Miquido | Product teams, ML in polished products, Google-certified. | Google-certified AI/ML capability paired with strong product design — clients receive ML that works inside well-crafted user experiences, not bolted-on algorithms | 4.0 | Full comparison |
| Simform | Industrial and enterprise, cloud-native AWS ML at scale. | AWS Premier Partner with 1,000+ engineers and documented depth in industrial IoT ML — connecting physical sensor streams to cloud ML inference at production scale | 3.9 | Full comparison |
| Intuz | SMBs, fixed-price discovery, 1,700+ projects. | 1,700+ project track record with a discovery-first engagement model making enterprise-grade ML accessible to SMBs through risk-reduced fixed-price POC phases | 3.9 | Full comparison |
| Scopic | Orgs wanting custom ML, 20+ years, strong computer... | 20+ years as a distributed software company gives Scopic strong custom ML engineering discipline with confirmed production deployments across transportation and healthcare | 3.9 | Full comparison |
| N-iX | Enterprises, large-scale Eastern-Europe ML engineering capacity. | 2,400+ engineers with deep specialization in scalable AI architectures, able to field large dedicated teams for complex multi-year ML programmes at competitive Eastern European rates | 3.9 | Full comparison |
| Oxagile | Media, AdTech, sports — video ML, 20+ years... | 20+ years of video domain expertise uniquely positions Oxagile for ML use cases involving video understanding, visual search, and real-time video analytics | 3.8 | Full comparison |
| Innowise | Banking, agriculture, healthcare — compliance-aware ML. | Cross-vertical ML delivery with documented case studies in banking automation, agricultural forecasting, and healthcare diagnostics — unusual breadth across regulated industries | 3.9 | Full comparison |
| Intellectsoft | Fintech, healthcare, construction — ML in enterprise ecosystems. | Palo Alto HQ with 10 global delivery offices combining US-based account management with competitive Eastern European delivery rates for enterprise ML programmes | 3.8 | Full comparison |
| DataRoot Labs | Startups and scale-ups, AI strategy plus execution, accessible. | One of Ukraine's most recognized ML consultancies — combining strategy-level AI advisory with hands-on engineering, a combination rare at this team size and price point | 3.8 | Full comparison |
| Itransition | Enterprises, ML in legacy systems, 25+ years delivery. | 25+ years of enterprise software delivery with five dedicated R&D labs, giving clients a mature delivery operation with advanced ML research support at competitive rates | 3.9 | Full comparison |
| 10Pearls | US enterprises and government contractors, AI-native, LATAM delivery. | AI-native engineering culture with four CRN Solution Provider 500 recognitions and 1,400+ experts spanning North America and LATAM for enterprise AI programmes | 3.8 | Full comparison |
| Coherent Solutions | Microsoft-stack enterprises, #1 Twin Cities IT firm. | Ranked #1 IT consulting firm in the Twin Cities five times in six years with 2,000+ engineers across 10 development centers, offering enterprise ML at competitive rates | 3.8 | Full comparison |
| Iflexion | US orgs, ML within custom enterprise software systems. | 25 years of enterprise software delivery with 850+ professionals embedding ML into complete systems rather than delivering standalone models that require separate integration work | 3.7 | Full comparison |
| Appinventiv | Global businesses, mobile-first ML, five-continent delivery. | 1,600+ specialists with a mobile-first AI approach and global footprint delivering 1,000+ digital assets with embedded ML — strong for consumer-facing AI product work | 3.8 | Full comparison |
| Avenga | Telco, banking, automotive enterprises — 6,000+ engineer scale. | 6,000+ specialists across 44 delivery centers formed through PE-backed acquisitions, providing enterprise-scale AI delivery capacity — though cultural integration across legacy entities is ongoing | 3.7 | Full comparison |
| BairesDev | Companies wanting rapid ML scale-up, LATAM nearshore, US... | 4,000+ ML-capable LATAM engineers in US time zones with 1,200+ completed projects, enabling rapid scale-up for organizations that need to grow their ML capacity fast | 3.7 | Full comparison |
| Turing | Teams needing pre-vetted senior ML developers, staff augmentation. | AI-powered vetting platform screening 3M+ global ML developers to place the top 1% directly in client engineering teams at rates competitive with US in-house hiring | 3.7 | Full comparison |
| EPAM Systems | Large enterprises, Fortune 500 scale, global compliance. | 62,000+ engineers across 50+ countries delivering ML inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise AI programmes | 3.9 | Full comparison |
Tredence FAQ
What is Tredence?
Tredence is a data science and AI engineering company founded in 2013 and headquartered in San Jose, California. The company has grown to 4,200+ employees and specializes in applied ML, data engineering, and industry-specific AI accelerators. Tredence is particularly known for last-mile ML adoption — operationalizing data science outputs into measurable operational improvements in supply chain, retail, and healthcare. The firm bridges the gap between insights delivery and value realization.
How much does Tredence charge?
Tredence uses dedicated team, t&m, fixed project pricing. Minimum engagement starts at $50K. A discovery call is required to get project-specific quotes.
What tech stack does Tredence use?
Tredence works with Python, R, Apache Spark, Databricks, AWS SageMaker, Azure ML, Snowflake, dbt, Power BI, TensorFlow, Scikit-Learn. Primary industries served include retail, manufacturing, supply chain, healthcare, financial services.
Is Tredence right for enterprise?
Enterprises, last-mile ML adoption, supply chain and retail. 4,200+ team size. Key consideration: Higher minimum engagement ($50K) limits accessibility for early-stage or SMB clients.
What are the best Tredence alternatives?
The best alternatives to Tredence depend on your use case. Top options are:
- Tensorway: ml-only focus with a dedicated specialist team backed by 25 years of the parent company software delivery infrastructure — unusually deep for a firm of this size
- LeewayHertz: product-centric ai delivery culture with verified fortune 500 client references including espn, siemens, and 3m — now operating within the hackett group
- InData Labs: pure-play ml boutique with a measurably higher specialist-to-generalist ratio than typical service firms, confirmed by clutch as a top ai service provider
Compare Tredence with other Machine Learning Development companies
Tredence vs Tensorway Tredence vs LeewayHertz Tredence vs InData Labs Tredence vs HatchWorks AI Tredence vs STX Next Tredence vs Addepto Tredence vs DataForest Tredence vs Forte Group Tredence vs Binariks Tredence vs Softeq Tredence vs Markovate Tredence vs ScienceSoft Tredence vs Miquido Tredence vs Simform Tredence vs Intuz Tredence vs Scopic Tredence vs N-iX Tredence vs Oxagile Tredence vs Innowise Tredence vs Intellectsoft Tredence vs DataRoot Labs Tredence vs Itransition Tredence vs 10Pearls Tredence vs Coherent Solutions Tredence vs Iflexion Tredence vs Appinventiv Tredence vs Avenga Tredence vs BairesDev Tredence vs Turing Tredence vs EPAM Systems