MLOps & LLMOps Services

KriraAI provides end-to-end MLOps services and LLMOps services that turn experimental models into dependable, monitored and cost-efficient production systems. From MLOps consulting and automated pipelines to LLM fine-tuning, deployment and observability, we build the operational backbone your AI needs to deliver measurable business value.

MLOps and LLMOps services for production machine learning and LLM systems.

Years of AI & Software Delivery

100+

Enterprise and Startup Projects

In-House

AI Research Team

Global

Clients Across India, USA and Global Markets

Trusted by Bajaj, Indian Navy, Kriam Pharma, SRK, Mittsure, PM KUSUM Scheme, Tale Logistics, SURGE and More

Why Most ML and LLM Projects Never Reach Production

Building a model is the easy part. Running it in production, day after day, is where most AI initiatives stall. Common blockers include:

Models stuck in notebooks
No repeatable path from experiment to ML model deployment.
Manual, error-prone releases
Every update needs hand-offs between data science and engineering teams.
Silent model drift
Accuracy drops as real-world data changes, and nobody notices until customers do.
Unpredictable LLM costs and quality
Token spend grows, hallucinations slip through and prompts break without warning.
No visibility
Without ML model monitoring and LLM observability, teams cannot prove that AI is working.

MLOps and LLMOps solve these problems by bringing software engineering discipline (automation, versioning, testing and monitoring) to the full model lifecycle.

Our MLOps & LLMOps Services

KriraAI covers the full lifecycle of machine learning operations and large language model operations. Start with one service or hand us the complete stack, including the custom ML models, generative AI apps and AI agents we build for you.

MLOps Consulting & Strategy

Our MLOps consulting team audits your current data, model and infrastructure setup, then designs a practical roadmap. You get a clear MLOps maturity assessment, architecture recommendations and a prioritized plan that matches your budget and team skills.

MLOps Pipeline Development (CI/CD for ML)

We build automated MLOps pipelines that handle data validation, model training, testing, versioning and release. Every model change moves through the same tested path, cutting release time from weeks to hours.

ML Model Deployment

We deploy models as scalable APIs, batch jobs or edge services using containers, Kubernetes and serverless options. Our ML model deployment approach includes A/B testing, canary releases and one-click rollback.

ML Model Monitoring & Drift Detection

We set up ML model monitoring for accuracy, latency, data drift and concept drift. Automated alerts and retraining triggers keep models accurate long after launch.

MLOps Platform Setup

We implement and customize the MLOps platform that fits your stack, using MLflow, Kubeflow and Weights & Biases for experiment tracking and model registries, on AWS SageMaker, Google Cloud or Azure with Docker and Kubernetes.

LLM Fine-Tuning Services

Our LLM fine-tuning services adapt open-source and commercial models to your domain data using Hugging Face, PyTorch, LoRA and instruction tuning. Our in-house AI research team tests which fine-tuning and RAG approaches actually reduce hallucinations before we recommend them.

LLM Deployment Services

We deploy LLMs, RAG pipelines and multi-agent systems (LangChain, LangGraph, CrewAI, AutoGen, MCP) on AWS, Google Cloud, Azure or on-premise NVIDIA GPU infrastructure, with autoscaling, guardrails and secure API access from day one.

LLM Observability & Evaluation

We implement LLM monitoring and LLM observability that track prompts, responses, latency, token cost and hallucination rates. Automated LLM evaluation pipelines test every prompt or model change before it reaches users.

MLOps vs LLMOps: What's the Difference?

MLOps (Machine Learning Operations) is the set of practices that automates how machine learning models are built, deployed, monitored and retrained in production. LLMOps is a specialized branch of MLOps for large language models. It adds prompt management, retrieval-augmented generation (RAG), fine-tuning, guardrails, LLM evaluation and token cost control.

Area

MLOps

LLMOps

Models

Classical ML and deep learning

Large language models and GenAI apps

Training

Train from scratch on your data

Fine-tune or prompt a pre-trained model

Key assets

Data, features, model weights

Prompts, embeddings, vector databases, adapters

Evaluation

Accuracy, precision, recall

Relevance, faithfulness, hallucination rate, safety

Monitoring

Data drift, model drift, latency

Response quality, token usage, cost per query

Main cost driver

Training compute

Inference and API token spend

Most businesses running AI in 2026 need both: MLOps for predictive models and LLMOps for chatbots, copilots and AI agents.

Our MLOps Implementation

Process

Every engagement follows a proven six-step process that delivers working results early and improves them continuously.

Assessment

We review your models, data pipelines, infrastructure and team workflows to identify gaps.

Architecture Design

We design a target MLOps or LLMOps architecture with the right tools, cloud services and security controls.

Pipeline Build

We automate data ingestion, training, testing and packaging in a version-controlled MLOps pipeline.

Deployment

We release models and LLM applications to production with staged rollouts and rollback safety.

Monitoring

We add dashboards and alerts for model performance, drift, latency and cost.

Continuous Optimization

We retrain, fine-tune and tune infrastructure so your AI gets better and cheaper over time.

MLOps Tools & Platforms We Work With

We are tool-agnostic and choose the MLOps tools that fit your existing stack, not ours.

DevelopmentMLflow

DevelopmentKubeflow

DevelopmentWeights & Biases

Benefits of Our MLOps Solutions

MLOps & LLMOps Use Cases Across Industries

1 of 7

Logistics

Route optimization, ETA prediction and shipment anomaly alerts.

Education

AI tutors and content generation on fine-tuned LLMs.

Hospitality and Telecom

AI chatbots, voice agents and churn prediction.

Finance

Real-time fraud detection, credit scoring and compliant document AI.

Healthcare

Clinical prediction models and secure medical LLM assistants.

Retail and E-commerce

Recommendation engines, demand forecasting and AI shopping assistants.

Manufacturing

Predictive maintenance and computer vision quality inspection.

Logistics

Route optimization, ETA prediction and shipment anomaly alerts.

Education

AI tutors and content generation on fine-tuned LLMs.

Hospitality and Telecom

AI chatbots, voice agents and churn prediction.

Finance

Real-time fraud detection, credit scoring and compliant document AI.

Healthcare

Clinical prediction models and secure medical LLM assistants.

Retail and E-commerce

Recommendation engines, demand forecasting and AI shopping assistants.

Why Choose KriraAI as Your MLOps Consulting Partner

5+ Years of AI Delivery

100+ enterprise and startup projects shipped to production, not just demos.

Flexible Engagement Models

Project-Based

A fixed-scope MLOps implementation with clear deliverables and timelines.

Dedicated MLOps Team

Engineers who work as an extension of your in-house team.

MLOps as a Service

A monthly managed service where we run, monitor and improve your ML and LLM systems for you.

Ready to Take Your AI to Production?

Whether you are deploying your first ML model or scaling a GenAI product to millions of users, KriraAI's research-backed MLOps and LLMOps team can help. Get a free review of your current setup and a clear roadmap to reliable, cost-efficient AI in production.

Frequently Asked

Questions

MLOps services help businesses deploy, monitor and maintain machine learning models in production reliably. They include MLOps consulting, automated CI/CD pipelines for ML, model deployment, ML model monitoring, drift detection and retraining. KriraAI provides MLOps and LLMOps services to startups and enterprises across India, the USA and global markets.

LLMOps (Large Language Model Operations) is the practice of deploying, evaluating, monitoring and optimizing large language models in production. It covers prompt management, RAG pipelines, fine-tuning, guardrails, LLM evaluation and token cost control for applications like chatbots, copilots and AI agents.

MLOps manages the lifecycle of traditional machine learning models, while LLMOps is a specialized extension for large language models. LLMOps adds prompt versioning, vector databases, hallucination tracking and inference cost management, which classical MLOps does not usually cover.

DevOps automates the delivery of application code, while MLOps also manages data and models, which change over time. MLOps adds data validation, experiment tracking, model versioning, drift monitoring and automated retraining on top of standard DevOps practices.

Your business needs MLOps if you want machine learning models to reach production faster and stay accurate over time. Without it, models are deployed manually, break silently when data changes and are hard to scale, audit or improve.

KriraAI works with MLflow, Kubeflow and Weights & Biases for MLOps, and PyTorch, TensorFlow and Hugging Face for model development. For LLMOps and agentic AI we use LangChain, LangGraph, CrewAI, AutoGen and MCP, deployed on AWS SageMaker, Google Cloud, Azure, Kubernetes and NVIDIA GPUs.

A typical MLOps implementation takes 4 to 12 weeks, depending on the number of models, data complexity and existing infrastructure. A focused pilot, such as automating one model's deployment and monitoring, can often go live in 3 to 4 weeks.

MLOps services cost depends on project scope, number of models, cloud platform and engagement model. KriraAI gives a transparent, scope-based quote after understanding your requirements, so you pay only for what your business needs. Book a free strategy call to get an estimate.

Yes, KriraAI provides LLM fine-tuning and deployment services for open-source and commercial models. We fine-tune models on your domain data using LoRA and instruction tuning, then deploy them securely on your cloud or on-premise GPU servers with guardrails and monitoring.

We monitor ML models for accuracy, latency, data drift and concept drift, with alerts that trigger retraining. For LLMs, we track response quality, hallucination rate, latency, token usage and cost per query using LLM observability tools and automated evaluation pipelines.