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 | Last updated: July 2026
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 | Details |
|---|---|
| Supply chain demand forecasting and inventory optimization ML model deployment | Available for retail, manufacturing, supply chain, healthcare, financial services clients |
| Customer analytics and churn prediction for retail or SaaS platforms | Available for retail, manufacturing, supply chain, healthcare, financial services clients |
| Healthcare ML for clinical decision support and patient outcome prediction | Available for retail, manufacturing, supply chain, healthcare, financial services clients |
| Data platform modernization to enable enterprise-scale ML workloads | Available for retail, manufacturing, supply chain, healthcare, financial services clients |
| ML model monitoring and retraining automation via MLOps pipeline implementation | Available for retail, manufacturing, supply chain, healthcare, financial services clients |
Tredence use cases
Short answer: Tredence is best suited for enterprise teams that need last-mile ML adoption — operationalizing data science into measurable supply chain, retail, or healthcare outcomes.
| Use case | Industries | Approach |
|---|---|---|
| Supply chain demand forecasting and inventory optimization ML model deployment | retail, manufacturing | Python, R |
| Customer analytics and churn prediction for retail or SaaS platforms | retail, manufacturing | Python, R |
| Healthcare ML for clinical decision support and patient outcome prediction | retail, manufacturing | Python, R |
| Data platform modernization to enable enterprise-scale ML workloads | retail, manufacturing | Python, R |
| ML model monitoring and retraining automation via MLOps pipeline implementation | retail, manufacturing | Python, R |
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 | Teams needing a dedicated ML specialist boutique with... | ML-only focus with a dedicated specialist team backed by 25 years of Anadea software delivery infrastructure — unusually deep for a firm of this size | 4.8 | Full comparison |
| LeewayHertz | Enterprises seeking end-to-end AI/ML product delivery with a... | Product-centric AI delivery culture with verified Fortune 500 client references including ESPN, Siemens, and 3M — now operating within The Hackett Group | 4.6 | Full comparison |
| InData Labs | Mid-market organizations with specific, complex ML problems requiring... | 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 seeking AI-native teams that embed generative AI... | 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 | Organizations that need ML models operationalized inside complete... | 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 | Mid-market companies in finance, energy, or retail needing... | 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 needing robust data engineering infrastructure as... | 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 | Organizations needing the engineering discipline of a larger... | 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, and insurance organizations needing ML built... | 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 manufacturers and industrial companies needing ML integrated... | 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, and fitness platforms needing ML-powered recommendation... | 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 needing ML consulting from a 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 needing ML embedded inside polished digital... | 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 companies needing cloud-native ML on... | 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 | Small and mid-size businesses needing custom AI/ML solutions... | 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 | Organizations needing fully custom ML engineering with 20+... | 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 needing large-scale ML engineering capacity in Eastern... | 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, and sports companies needing ML with... | 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 | Regulated industry organizations — banking, agriculture, healthcare —... | 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 | Enterprises in fintech, healthcare, and construction needing ML... | 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 needing AI strategy alongside execution,... | 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 needing ML integrated into complex legacy software... | 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-based enterprises and government contractors needing AI-native 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 | Midwest enterprises and Microsoft-stack organizations needing ML capabilities... | 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-based organizations needing ML integrated into complete custom... | 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 needing mobile-first ML delivery at scale... | 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 | Large enterprises in telco, banking, or automotive needing... | 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 needing rapid ML team scale-up using LATAM... | 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 that need to extend their ML engineering... | 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 requiring ML at Fortune 500 scale... | 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?
Enterprise teams that need last-mile ML adoption — operationalizing data science into measurable supply chain, retail, or healthcare outcomes. 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 anadea 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
Last reviewed: July 2026. Verify all details directly with Tredence before making a decision.