Managed AI services have moved beyond model hosting into the day-to-day work of keeping AI systems reliable, secure, measurable, and cost-controlled in production.
For buyers, the most important capabilities now include model and data monitoring, LLM evaluation, MLOps or LLMOps, incident response, governance, infrastructure management, and ongoing optimization.
The right operating model depends on the system: a regulated decision engine needs different controls from a customer-facing RAG assistant or a portfolio of internal AI agents.
Strong providers connect engineering and operations so teams can improve AI continuously without losing visibility into quality, spend, security, or ownership.
1. Itexus
Itexus develops AI-enabled software with a strong concentration in fintech and financial services. Its AI capabilities cover LLM systems, RAG pipelines, copilots, conversational interfaces, credit scoring, risk modeling, predictive analytics, fraud and anomaly detection, document processing, and AI-agent workflow automation. The company also offers data engineering and MLOps, AI strategy and roadmapping, AI audits, optimization, and responsible AI work.
For managed AI programs, the useful combination is product engineering plus the operational disciplines needed after deployment. Itexus builds automated quality gates, testing and release controls into delivery and supports production software over time. That makes it a practical fit for banks, fintech companies, insurers, and other organizations that need AI features integrated into business-critical applications rather than isolated experiments.
Key Facts
- Best for: Fintech and financial-services teams operating AI inside production products
- Core services: Managed AI, AI/ML development, LLM and RAG systems, MLOps, data engineering, AI integration, AI audit and optimization
- Specialization: Credit, risk, fraud, document intelligence, predictive analytics, and AI workflow automation
- Notable strength: Combines AI product development with production engineering and ongoing software support
- Headquarters: Dover, DE, USA
- Founded: 2013
- Team size: 150+ engineers
Contact Information
- Website: itexus.com
- Email: moc.suxeti@ofni
- Address: 8, The Green, STE road, Dover, DE 19901, United States
- LinkedIn: www.linkedin.com/company/itexus
- Facebook: www.facebook.com/itexus
- Twitter: x.com/ItexusSoft
- Instagram: www.instagram.com/itexus.soft
2. Tredence
Tredence is a data and AI services company with a dedicated MLOps practice split into advisory, implementation and managed services. The managed tier covers ongoing model deployment, productionization, monitoring, drift detection, retraining and continuous process improvement aimed at lowering platform costs.
Its MLWorks accelerator handles model observability, feature store and drift detection. Published case studies include a global CPG company running 100,000 forecasting models across 18 markets and a retailer whose production support held 98% SLA adherence over 18 months. It suits enterprises operating many models on Databricks-style data platforms.
Key Facts
- Best for: Enterprises running large portfolios of production ML models
- Core services: MLOps advisory, implementation, managed model operations, LLMOps
- Specialization: Model observability and drift detection with the MLWorks accelerator
- Headquarters: San Jose, CA, USA
- Founded: 2013
- Team size: 1,001–5,000 employees
Contact Information
- Website: tredence.com
- LinkedIn: www.linkedin.com/company/tredence
3. Mission Cloud
Mission Cloud is an AWS-focused provider with an AI management line that includes LLM operations. The service covers deploying, scaling and maintaining large language models, fine-tuning, model versioning with rollback, demand-based infrastructure scaling, anomaly detection in model behavior, performance monitoring and ongoing cost optimization.
Related offerings include AI Gateway and AI Foundation for running AI on AWS, alongside work with Amazon Bedrock and SageMaker. One published case study moved an AI SaaS company's LLMOps setup from Google Cloud to AWS and cut data loading times by 50%. It suits teams that run their AI workloads on AWS.
Key Facts
- Best for: Companies operating LLM workloads on AWS
- Core services: LLM operations, fine-tuning, model monitoring, AI cost optimization
- Specialization: Managed AI on AWS with Bedrock and SageMaker
- Parent company: CDW
- Headquarters: Los Angeles, CA, USA
- Team size: 201–500 employees
Contact Information
- Website: missioncloud.com
- LinkedIn: www.linkedin.com/company/missioncloud
4. Provectus
Provectus builds and operates production AI and data systems. Engagements move through a one-week fixed-fee Sprint, an 8 to 14 week Enable phase for deployment and tuning, and an ongoing, outcome-priced Realize phase with quarterly reviews, so the same team keeps running the system after launch.
The company reports 150+ customers in production, 400+ AI builders and consultants, AWS Premier Tier Services Partner status and Anthropic Preferred Claude Partner status. Its blueprints target financial services, insurance, healthcare and life sciences, such as Submission Flow and Evidence Lens. It suits regulated companies that want AI operated against agreed business outcomes.
Key Facts
- Best for: Regulated firms wanting outcome-priced ongoing AI operations
- Core services: AI system build, deployment and tuning, ongoing AI operations
- Specialization: Insurance, financial services and healthcare AI on AWS
- Headquarters: Palo Alto, CA, USA
- Founded: 2010
- Team size: 501–1,000 employees
Contact Information
- Website: provectus.com
- Address: 447 Sutter Street, Suite 405, San Francisco, CA 94108
- LinkedIn: www.linkedin.com/company/provectus-it-inc
5. AI Superior
AI Superior offers a dedicated AI Managed Services practice built around the ongoing operation of AI systems. Its scope includes model monitoring, performance tuning, AI infrastructure and cloud management, automated retraining, proactive issue resolution, security, compliance, and lifecycle management. The company also develops custom AI software, giving clients a path from strategy and implementation into continuous operations.
This model is well suited to organizations that already have AI in production, or are preparing to move there, and want one team to watch model behavior and technical performance after launch. The combination of monitoring, optimization, retraining, and governance is particularly relevant when models affect regulated workflows, sensitive data, or decisions that need a clear operating process.
Key Facts
- Best for: Organizations seeking end-to-end management of deployed AI systems
- Core services: AI managed services, model monitoring, performance tuning, automated retraining, AI infrastructure management
- Specialization: AI lifecycle management, security, compliance, and custom AI development
- Notable strength: Explicit managed-service coverage from production monitoring through ongoing model optimization
- Headquarters: Darmstadt, Germany
- Founded: 2019
- Team size: 11–50 employees
Contact Information
- Website: aisuperior.com
- Phone: +49 6151 3943489
- Email: moc.roirepusia@ofni
- Address: Robert-Bosch-Str. 7, 64293 Darmstadt, Germany
- LinkedIn: www.linkedin.com/company/ai-superior
- Facebook: www.facebook.com/aisuperior
- Twitter: x.com/aisuperior
- Instagram: www.instagram.com/ai_superior
6. PwC
PwC provides AI Managed Services for enterprises that want ongoing support across generative, cognitive, and analytical AI. Its managed AI offering includes continued fine-tuning and monitoring so deployed solutions can stay aligned with changing business needs. The service sits within a broader managed-services model, which can be useful when AI operations need to connect with transformation, data, governance, risk, and industry processes.
For organizations scaling AI beyond individual pilots, PwC brings an operating perspective rather than treating deployment as the finish line. Its managed approach is especially relevant to companies building reusable AI capabilities across functions, including AI agents and decision-support systems, while maintaining centralized oversight and a structured delivery model.
Key Facts
- Best for: Large organizations scaling AI across multiple business functions
- Core services: AI managed services, generative AI, cognitive AI, analytical AI, monitoring, ongoing fine-tuning
- Specialization: Enterprise AI operations tied to broader managed services and transformation programs
- Notable strength: Ongoing AI refinement paired with enterprise governance and operating-model support
- Headquarters: London, UK
- Team size: 10,001+ employees
Contact Information
- Website: www.pwc.co.uk
- Phone: +44 (0)20 7583 5000
- Email: moc.cwp@nnyl.aititeal
- Address: 1 Embankment Place, London WC2N 6RH, United Kingdom
- LinkedIn: www.linkedin.com/company/pwc-uk
- Facebook: www.facebook.com/PwCUK
- Instagram: www.instagram.com/pwc_uk
7. OSKI Solutions
OSKI Solutions designs, builds, integrates, and operates production AI systems, including LLM assistants, RAG applications, AI agents, machine-learning pipelines, and computer-vision solutions. Its delivery process extends through evaluation, guardrails, observability, cost controls, and production operations. After launch, the team monitors accuracy and cost, tracks drift, and iterates prompts and models.
That end-to-end structure fits companies that want the same senior engineering team to own the path from proof of concept to a system running under real user load. OSKI also integrates AI into existing products and APIs with rate limits, fallbacks, and monitoring, which is useful when a managed AI engagement has to coexist with a mature application stack rather than replace it.
Key Facts
- Best for: Product teams that need LLM, RAG, agent, or ML systems built and operated in production
- Core services: AI development, LLM and RAG development, AI integration, machine learning, evaluation, monitoring, cost control
- Specialization: Production AI with observability, guardrails, and ongoing prompt and model improvement
- Notable strength: One senior-led engagement can cover discovery, build, hardening, deployment, and operation
- Headquarters: Tallinn, Estonia
- Team size: 51–200 employees
Contact Information
- Website: oski.site
- Email: etis.ikso@tcatnoc
- Address: Kaupmehe tn 7-120, Tallinn, 10114, Estonia
- LinkedIn: www.linkedin.com/company/oski-solutions
8. Mufasa Labs
Mufasa Labs has a Managed AI Operations service focused specifically on keeping production AI running. The operating scope includes quality and drift monitoring, evaluation runs when models change, token-cost management, integration patching, and incident response under SLA. The company can operate systems it originally built or take over existing AI environments.
This is a direct fit for teams that have reached production but do not want to build a 24/7 MLOps function internally. Mufasa Labs also emphasizes client ownership of the cloud environment, repository, documentation, and runbooks, which can make the managed relationship easier to govern and eventually transition. The service is structured around operational continuity rather than a one-time deployment.
Key Facts
- Best for: Teams that need ongoing operations for production AI without building an internal MLOps bench
- Core services: Managed AI operations, drift monitoring, evaluations, cost management, integration maintenance, SLA incident response
- Specialization: Production AI takeover and ongoing operation
- Notable strength: Can run newly built systems or assume responsibility for an existing AI stack
- Headquarters: Peoria, AZ, USA
Contact Information
- Website: www.mufasalabs.com
9. Accenture
Accenture runs data and AI capabilities as managed services, covering the operational layer needed to keep enterprise AI useful after implementation. Its work includes MLOps and AI operations, and current AI Managed Services roles extend into AgentOps for AI agents and copilots, with observability, incident response, release management, prompt and workflow releases, and AI cost management.
The company is most relevant to enterprises operating AI across many systems, teams, or cloud environments. Accenture can combine AI operations with data, cloud, governance, and wider managed-services programs, which helps when production AI depends on shared platforms and cross-functional processes. The emphasis is on repeatable operations, service management, and continuous improvement at enterprise scale.
Key Facts
- Best for: Enterprises running AI, copilots, and agents across complex technology estates
- Core services: AI managed services, MLOps, AgentOps, observability, incident response, release management, AI cost management
- Specialization: Large-scale data and AI operations integrated with cloud and enterprise managed services
- Notable strength: Operational coverage extends from models and agents to the data and cloud platforms around them
- Headquarters: Dublin, Ireland
- Founded: 1989
- Team size: 10,001+ employees
Contact Information
- Website: www.accenture.com
- Phone: +352264231
- Address: 7 Rue Lou Hemmer, Niederanven, Luxembourg, 1748
10. Deloitte
Deloitte combines GenAI with technology and business-process managed services. Its current managed-services material covers GenAI-enabled application management and AI operations, including models where teams deploy, run, support, and continuously improve intelligent applications. The firm also applies GenAI inside managed IT, finance, procurement, and other operating functions.
This approach is relevant when AI is not a single application but part of a larger service environment with process ownership, support requirements, and enterprise controls. Deloitte can connect AI operations with application management and function-specific managed services, which helps organizations standardize how AI-enabled workflows are supported after rollout. The model is especially suited to transformation programs where the operating process matters as much as the underlying model.
Key Facts
- Best for: Enterprises embedding GenAI into managed applications and business processes
- Core services: GenAI managed services, application management, AI operations, technology managed services
- Specialization: AI-enabled operating models across IT and business functions
- Notable strength: Connects AI operations with broader application and process managed services
- Headquarters: London, UK
- Founded: 1845
- Team size: 10,001+ employees
Contact Information
- Website: www.deloitte.com
- Phone: +1 205 321 6000
- Address: 420 North 20th Street, Suite 2400, Birmingham, AL, 35203-3289, United States
- LinkedIn: www.linkedin.com/company/deloitte
- Facebook: www.facebook.com/deloitte
- Twitter: x.com/deloitte
- Instagram: www.instagram.com/lifeatdeloitteus
11. A-listware
A-listware combines machine learning and AI engineering with managed IT, cloud management, application management, DevOps, security, and 24/7 infrastructure monitoring. Its managed-services model can cover cloud and on-premises environments under SLAs, while its application practice includes ongoing monitoring, modernization, maintenance, and feature delivery.
The company also has published work in AI-driven IT support automation, which connects its AI and managed-operations capabilities in a practical use case. For businesses building AI-enabled applications without a separate operations organization, A-listware can support the surrounding infrastructure, cloud services, application lifecycle, and engineering work that keep those systems available. This makes it a useful option when the managed AI requirement extends beyond the model itself.
Key Facts
- Best for: Companies that want AI/ML engineering together with managed infrastructure and application support
- Core services: Machine learning and AI, managed IT, cloud management, managed applications, DevOps, security
- Specialization: End-to-end support for cloud and on-premises applications and infrastructure
- Notable strength: Managed-service coverage can support both AI-enabled software and the IT environment around it
- Headquarters: London, UK
Contact Information
- Website: a-listware.com
- Phone: +44 (0)142 439 01 40
- Email: moc.erawtsil-a@ofni
- Address: St. Leonards-On-Sea, TN37 7TA, UK
- LinkedIn: www.linkedin.com/company/a-listware
- Facebook: www.facebook.com/alistware
12. Persistent Systems
Persistent Systems positions Managed AI Services as part of the foundation for operationalizing AI safely at scale. Its approach covers managed AI platforms, governance frameworks, enterprise operating models, managed AI infrastructure, standardized deployment, monitoring, access control, and continuous optimization. The company also supports organizations moving AI workloads from pilots into production environments.
Persistent is well suited to enterprises that need a repeatable AI platform rather than a separate operating process for every model or use case. The emphasis on infrastructure, governance, monitoring, and optimization helps teams create a shared control layer for AI adoption. This is particularly relevant when AI systems span multiple business units or must meet reliability and governance requirements while usage grows.
Key Facts
- Best for: Enterprises building a standardized operating foundation for AI at scale
- Core services: Managed AI services, AI platforms, monitoring, governance, access control, continuous optimization
- Specialization: Production AI infrastructure and enterprise operating models
- Notable strength: Focuses on the foundational platform and governance needed to scale AI beyond pilots
- Headquarters: Pune, India
- Founded: 1990
- Team size: 10,001+ employees
Contact Information
- Website: www.persistent.com
- Phone: +91-20-6746-2004
- Email: moc.tnetsisrep@ofni
- Address: Bhageerath, 402 Senapati Bapat Road, Pune 411 016, India
- LinkedIn: www.linkedin.com/company/persistent-systems
- Facebook: www.facebook.com/PersistentSystems
- Twitter: x.com/Persistentsys
- Instagram: www.instagram.com/persistent_systems
13. Gilzor
Gilzor helps teams turn AI-built applications and fast-moving prototypes into production-ready software. Its engineering work covers architecture, security, reliability, performance, compliance, cloud infrastructure, deployment, monitoring, and AI or API cost control. After launch, the company can continue with ongoing product support, feature development, and bug fixing.
This model fits organizations whose main managed AI challenge sits at the application layer: keeping an AI-enabled product stable, observable, secure, and economical as real users and new features arrive. Gilzor is particularly relevant when a product was built quickly with AI-assisted development and now needs stronger engineering discipline around deployment and operations. The focus is on the production system around AI as much as the AI feature itself.
Key Facts
- Best for: Teams hardening and operating AI-built or AI-enabled software products
- Core services: Production readiness, architecture, security, deployment, monitoring, cost control, ongoing product support
- Specialization: Moving AI-built applications from prototype quality to maintainable production software
- Notable strength: Strong application-layer focus on reliability, observability, security, and AI/API costs
- Headquarters: Warsaw, Poland
Contact Information
- Website: www.gilzor.com
- Email: moc.rozlig@stcatnoc
- Address: Poland, Warsaw, Office 58, street Adama Mickiewicza 37, 01-625
- LinkedIn: www.linkedin.com/company/gilzor-softwaredevelopment
14. KPMG
KPMG supports AI strategy, implementation, operations, and governance, including managed AI service models for ongoing operation and maintenance. Its AI technology work also covers AI operations and governance with continuous monitoring, giving enterprises a framework for keeping deployed systems controlled as models, data, and business requirements change.
The managed-services angle is strongest for organizations where governance and risk controls have to sit inside the operating model from the beginning. KPMG can connect technical AI operations with compliance, enterprise governance, and broader managed-services programs. That makes it relevant to regulated businesses and large organizations that need accountability around how AI is monitored, updated, and used after deployment.
Key Facts
- Best for: Enterprises where AI operations must be tightly connected to governance, risk, and compliance
- Core services: Managed AI operations, AI implementation, continuous monitoring, AI governance, managed services
- Specialization: Enterprise AI operating models with governance and oversight
- Notable strength: Combines technical AI operations with governance and risk-management capabilities
- Headquarters: Amstelveen, Netherlands
- Founded: 1987
- Team size: 10,001+ employees
Contact Information
- Website: kpmg.com
- Phone: +44 (0)20 7311 1000
- Address: 15 Canada Square, Canary Wharf, London, E14 5GL
- LinkedIn: www.linkedin.com/company/kpmg-uk
- Twitter: x.com/kpmguk
- Facebook: www.facebook.com/KPMG
15. Tata Consultancy Services (TCS)
Tata Consultancy Services supports enterprise AI at the platform and operations layer, with current work spanning MLOps, LLMOps, GenAIOps, ModelOps, AgentOps, AI observability, cloud operations, and managed AI infrastructure. Its AI operating model also addresses governance and the controls needed to move systems from experimentation into repeatable enterprise use.
TCS is a natural fit for very large organizations that need AI operations integrated with existing cloud, infrastructure, security, and service-management environments. Its work in managed AI inference infrastructure and agentic cloud operations is relevant when reliability, observability, and deployment control have to extend across many applications or locations. The breadth is useful for enterprises standardizing AI operations across a large technology estate.
Key Facts
- Best for: Large enterprises standardizing AI operations across complex infrastructure and cloud environments
- Core services: MLOps, LLMOps, GenAIOps, ModelOps, AgentOps, AI observability, managed AI infrastructure
- Specialization: Enterprise-scale AI platforms, governance, and operational integration
- Notable strength: Broad AI operations coverage connected to cloud and enterprise service management
- Headquarters: Mumbai, India
- Founded: 1968
- Team size: 10,001+ employees
Contact Information
- Website: www.tcs.com
- Email: moc.sct@gnitekram.aidni
- Phone: +91 22 6778 9284
- Address: 11th Floor, Maker Tower E ' Premises, Co-Operative Society Ltd., Cuffe Parade - 4000 005, Maharashtra
- Facebook: www.facebook.com/TataConsultancyServices
- Twitter: x.com/TCS
- LinkedIn: www.linkedin.com/company/tata-consultancy-services
- Instagram: www.instagram.com/tcsglobal
Comparison Table
| Company | Best for | Headquarters | Founded | Team size |
| Itexus | Fintech and financial-services teams operating AI inside production products | Dover, DE, USA | 2013 | 150+ engineers |
| Tredence | Enterprises running large portfolios of production ML models | San Jose, CA, USA | 2013 | 1,001–5,000 employees |
| Mission Cloud | Companies operating LLM workloads on AWS | Los Angeles, CA, USA | — | 201–500 employees |
| Provectus | Regulated firms wanting outcome-priced ongoing AI operations | Palo Alto, CA, USA | 2010 | 501–1,000 employees |
| AI Superior | Organizations seeking end-to-end management of deployed AI systems | Darmstadt, Germany | 2019 | 11–50 employees |
| PwC | Large organizations scaling AI across multiple business functions | London, UK | — | 10,001+ employees |
| OSKI Solutions | Product teams that need LLM, RAG, agent, or ML systems built and operated in production | Tallinn, Estonia | — | 51–200 employees |
| Mufasa Labs | Teams that need ongoing operations for production AI without building an internal MLOps bench | Peoria, AZ, USA | — | — |
| Accenture | Enterprises running AI, copilots, and agents across complex technology estates | Dublin, Ireland | 1989 | 10,001+ employees |
| Deloitte | Enterprises embedding GenAI into managed applications and business processes | London, UK | 1845 | 10,001+ employees |
| A-listware | Companies that want AI/ML engineering together with managed infrastructure and application support | London, UK | — | — |
| Persistent Systems | Enterprises building a standardized operating foundation for AI at scale | Pune, India | 1990 | 10,001+ employees |
| Gilzor | Teams hardening and operating AI-built or AI-enabled software products | Warsaw, Poland | — | — |
| KPMG | Enterprises where AI operations must be tightly connected to governance, risk, and compliance | Amstelveen, Netherlands | 1987 | 10,001+ employees |
| Tata Consultancy Services (TCS) | Large enterprises standardizing AI operations across complex infrastructure and cloud environments | Mumbai, India | 1968 | 10,001+ employees |
How to Choose a Managed AI Services Provider
A managed AI provider takes on the ongoing work of keeping models, LLM applications, and agents reliable after launch. The right choice depends on what you run in production, whether a regulated scoring model, a customer-facing RAG assistant, or internal agents, and on how much operational ownership you want to hand over.
Evaluation and quality monitoring. Ask how the provider measures output quality in production, not just at launch. For LLM systems, look for evaluation sets built from your real use cases, automated regression tests before each release, and tracking of hallucination, retrieval accuracy, and user feedback. For classic ML, ask about drift detection on inputs and predictions. A provider that only monitors uptime cannot tell you when answers get worse.
Cost visibility and control. AI spend can grow quickly through token usage, GPU instances, and vector database storage. Ask how costs are reported per application or feature, what alerts exist for spikes, and which optimizations they apply, such as caching, smaller models for simple tasks, prompt compression, or batch inference. Request an example monthly cost report from another client with sensitive details removed.
Release and change management. Models, prompts, and retrieval pipelines change often, and each change can alter behavior. Ask how prompts and model versions are versioned, how changes are tested against evaluation sets, and how rollbacks work. Check whether provider-side model updates, for example a new version of a hosted LLM, trigger re-evaluation. Documented change logs matter for audits and for explaining incidents later.
Incident response for AI failures. AI incidents look different from normal outages: harmful outputs, prompt injection, data leakage through responses, or agents taking wrong actions. Ask what counts as an incident, how quickly they respond, how they contain issues, for example by disabling a tool or switching models, and how root causes are reported. Request a sample post-incident report.
Governance and regulatory readiness. Ask how the provider supports your obligations under frameworks such as the EU AI Act, sector rules in finance or healthcare, and internal model risk policies. Useful evidence includes model documentation, access controls, audit logs of prompts and outputs, data retention settings, and bias testing for decision systems. Governance should be built into monitoring and escalation, not kept in a separate policy document.
Ownership and portability. Clarify who owns evaluation datasets, prompts, fine-tuned weights, pipelines, and infrastructure code. Prefer setups that run in your own cloud account or can be moved there, using standard tooling rather than proprietary platforms. Ask what a handover would include if you bring operations in-house. Clear ownership keeps your internal team able to improve the system and avoids dependency on one provider.
Conclusion
Managed AI is becoming an operating discipline rather than a support task added after deployment.
Buyers should look beyond model access and ask how a provider measures quality, detects drift, controls cost, manages releases, responds to incidents, and documents changes.
Governance also needs to be operational: policies matter only when they are connected to monitoring, access controls, evaluation, and escalation paths.
As agentic systems grow more autonomous, LLMOps and AgentOps will become as important as traditional application and cloud operations.
The strongest managed AI programs keep ownership transparent while giving internal teams a reliable way to improve production systems continuously.
Frequently Asked Questions
What does a managed AI service include?
A managed AI service usually covers running AI systems in production: infrastructure and model hosting, monitoring of quality and drift, LLM evaluation, MLOps or LLMOps pipelines, incident response, cost optimization, and security controls. Many providers also handle regular improvements such as prompt updates, retraining, and model upgrades. The exact scope varies, so confirm whether data pipelines and application code are included or remain your responsibility.
How much do managed AI services cost?
Managed AI services are usually priced as a monthly retainer for operations and engineering time, plus the underlying cloud, GPU, and model API costs, which are often passed through. The main cost drivers are the number of systems in production, required coverage hours, evaluation depth, regulatory documentation, and traffic volume. Ask providers to separate their service fee from infrastructure spend so you can compare proposals fairly.
What is the difference between MLOps and LLMOps?
MLOps covers the lifecycle of trained machine learning models: data pipelines, training, deployment, and monitoring for drift and accuracy. LLMOps applies similar discipline to applications built on large language models, adding prompt management, retrieval pipeline tuning, output evaluation, guardrails, and token cost control. Many production systems need both, for example when a classic scoring model sits alongside an LLM assistant.
Should we manage AI systems in-house or use a provider?
It depends on how central AI is to your product and whether you can hire people with production AI operations experience. Companies with a few critical systems often start with a provider and build internal skills over time, while those with AI at the core of their product usually keep ownership in-house. A co-managed model, where the provider runs operations and your team owns direction, is common.
What should we prepare before engaging a managed AI provider?
List the AI systems in or near production, their business purpose, users, and risk level. Gather architecture diagrams, data sources, current monitoring, known issues, and monthly costs. Define what good output looks like with examples, since this becomes the basis for evaluation. Note regulatory requirements and who internally approves changes. This lets providers scope accurately and start measuring quality from day one.