MLOps & LLMOps Consulting for Reliable, Governed AI in Production

MLOps applies software engineering, automation, versioning, testing, deployment and monitoring practices to the machine-learning lifecycle. LLMOps extends the operating model for generative-AI applications, where teams also need to manage prompts, foundation-model changes, retrieval pipelines, evaluation datasets, traces, safety controls, token cost, latency and agent or tool behaviour. Many enterprises need a shared operating foundation with service-specific controls for both.

Common triggers include repeated manual deployments, inconsistent environments, models or prompts that cannot be reproduced, weak release evidence, production drift or regressions, unclear ownership, rising inference cost, fragmented monitoring, frequent foundation-model changes, or multiple AI teams using different deployment and governance practices. A smaller implementation may be sufficient when only one stable model is deployed infrequently.

Scope can include maturity assessment, target operating model, reference architecture, source and artifact versioning, CI/CD and continuous-training design, model and prompt registry practices, evaluation gates, deployment automation, rollback, observability, tracing, drift and quality monitoring, RAG or agent operations, cost telemetry, runbooks, governance controls, knowledge transfer and optional managed operational support. Final scope is agreed during discovery.

Yes. The service is intended to work with existing enterprise technology where practical. The design can use or integrate capabilities from AWS, Microsoft Azure, Google Cloud, Databricks, MLflow, Kubernetes and existing CI/CD, observability, identity and infrastructure-as-code tooling. Recommendations are requirements-led and vendor-neutral unless a specific platform is mandated.

It can. For RAG, operating controls may include source refresh, retrieval evaluation, access-aware indexing, citation checks, embedding or reranking changes and end-to-end answer evaluation. For agents, scope can include traces across model calls and tools, task-completion tests, permission boundaries, tool-call checks, human escalation, cost and latency monitoring, release gates and rollback.

A release process can combine code tests, data validation, model or application benchmarks, regression datasets, task-specific quality measures, safety and security tests, human review where required, latency and cost thresholds, and documented approval gates. The exact acceptance criteria should be linked to the use case and risk rather than relying on one generic score.

Useful signals depend on the system. Predictive ML may require service health, input drift, prediction quality, model performance and retraining triggers. Generative AI may also require traces, task success, groundedness or relevance, safety events, refusals, tool behaviour, latency, token usage, cost, user feedback and model or prompt version context. Monitoring should connect to owners, thresholds and response procedures.

The operating design can incorporate identity and access, secrets handling, data classification, environment separation, version and lineage evidence, change approval, evaluation records, exception handling, human oversight, incident response, rollback and third-party model or platform dependencies. Relevant requirements should be validated with the organisation’s authorised legal, privacy, security, risk and compliance specialists.

Typical deliverables can include an MLOps and LLMOps maturity assessment, target architecture, lifecycle workflow, repository and environment design, CI/CD or continuous-training blueprint, registry and lineage approach, evaluation and release-gate framework, observability design, infrastructure-as-code patterns, operational dashboards or specifications, runbooks, RACI, risk and dependency register, implementation backlog and handover documentation.

DataConsultant does not publish one fixed duration for this service. Timing depends on the number of models and applications, environments, cloud and security constraints, existing CI/CD maturity, data and model dependencies, evaluation requirements, integrations, production criticality, stakeholder availability and whether implementation or managed operations are included. A delivery plan is confirmed after discovery.

DataConsultant does not publish a fixed public fee for this service. Pricing is scope-led and can be affected by the number and type of AI workloads, environments, cloud accounts, deployment patterns, evaluation coverage, RAG or agent complexity, observability, security requirements, infrastructure automation, documentation, knowledge transfer and ongoing operating support. A written quote is prepared after scoping.

No. The indicative INR figure is public market guidance derived from current comparable India-based MLOps or MLOps-and-LLMOps service pricing and is included only to support early budgeting. It is not an official DataConsultant price, package or commitment. DataConsultant provides a separate scoped quote.

Not automatically. Cloud compute, accelerators, foundation-model or API usage, vector databases, observability platforms, third-party evaluation tools, security products and other licences are separate supplier costs unless the statement of work explicitly includes them. The service can help identify and model these cost drivers.

Yes, ongoing support can be scoped where appropriate. It may cover monitoring, evaluation maintenance, release support, model or prompt changes, incident and problem analysis, cost and performance optimisation, runbook updates, governance reporting and continual improvement. Service hours, responsibilities, escalation and any service levels must be agreed explicitly rather than assumed.