--- title: MLOps Services description: "Choose MLOps Services for model deployment, monitoring, CI/CD, automation, data pipelines, and scalable machine learning operations." url: https://miracuves.com/service/mlops-services date_modified: 2026-09-29 author: miracuves language: en_US --- Available now · Models into production, and kept there # MLOps Services Model DeploymentDrift MonitoringLLMOps Miracuves provides MLOps services that move models out of notebooks and keep them accurate in production: automated training pipelines, a model registry, CI/CD for models, batch, real-time and GPU serving, drift monitoring with retraining triggers, one-step rollback, and LLMOps for products built on large language models. We work in MLflow, Kubeflow, SageMaker, Vertex AI or Azure ML, and every pipeline and config is yours. [WhatsApp - Talk to Our Team](https://wa.me/919830009649)[Explore AI Solutions](https://miracuves.com/solutions/) Reviewed on ClutchScoped MLOps module from $3,699[View delivered projects](https://miracuves.com/portfolio/) - Registry on every build - Rollback in one step - 100% IP Ownership - NDA Day One **2-8 Weeks**Custom MLOps build**$3,699**Scoped MLOps module, from**100%**Pipelines and configs yours**Drift-watched**Inputs and predictions, with alerts MLOps engineers online now MLflowKubeflowModel registryDrift alerts9,000+ deliveredNDA day one Related services[Machine Learning Development](https://miracuves.com/service/ml-development/)[DevOps Engineering](https://miracuves.com/service/devops-engineering/)[LLM Development](https://miracuves.com/service/llm-development/)[RAG Development](https://miracuves.com/service/rag-development/) - **Notebook to endpoint**Training, registry and serving as one pipeline - **Batch · Real-time · GPU**ML model serving picked per latency and cost - **Drift monitoring**Data drift and prediction drift, with alerts - **MLflow · Kubeflow · Airflow**Or SageMaker, Vertex AI and Azure ML - **LLMOps**Prompt versions, eval sets, token budgets - ### Runbooks Included Retrain and rollback written down - ### NDA Day One IP protected first call - ### Full Source Code Delivered at handoff - ### 60-Day Support Post-launch included - ### 100% IP Ownership Yours - always - [### Reviewed on Clutch Third-party client reviews](https://miracuves.com/reviews-awards/) More than 6,000+ Companies Trust us Worldwide [![Miracuves Ranked among top 1000 companies globally by Clutch](https://miracuves.com/wp-content/uploads/2024/03/Miracuves-Clutch-top-1000-company.webp "MLOps Services 1")](https://clutch.co/profile/miracuves-solutions#reviews) [![Miracuves is a top-minus service provider in the United States by Clutch](https://miracuves.com/wp-content/uploads/2024/03/Miracuves-clutch-top-managed-service-provider-US.webp "MLOps Services 2")](https://clutch.co/profile/miracuves-solutions#reviews) ![MediaCube is the best managed IT service provider in New York City by Expertise.com](https://miracuves.com/wp-content/uploads/2024/03/Miracuves-Expertise-Provider.webp "MLOps Services 3") ![MiraQs as an official member of Forbes Business Council ](https://miracuves.com/wp-content/uploads/2024/03/Mircuves-Official-Memeber-Forbes-Council.webp "MLOps Services 4") [![Miracuves is a verified agency with design rush. ](https://miracuves.com/wp-content/uploads/2024/03/DesignRush-Top-Design-Agencies-in-India.webp "MLOps Services 5")](https://www.designrush.com/agency/profile/miracuves#reviews) [![MiraQs is ranked as a trustworthy service provider with Trustpilot. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-TrustPilot-Top-Software-Development-Company.webp "MLOps Services 6")](https://www.trustpilot.com/review/miracuves.com) [![Miracuves is one of the top managed service providers, companies by themanifest. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-The-Manifest-Top-Managed-Software-Development-Company.webp "MLOps Services 7")](https://themanifest.com/company/miracuves-solutions) [![Miracuves ranked as a top software development company by Design Rush. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-Designrush-Top-Software-Development-Company.webp "MLOps Services 8")](https://www.designrush.com/agency/profile/miracuves#reviews) [![Miracuves is recognized as top software developed company by goodfirms. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-GoodFirms-Top-Software-Development-Company.webp "MLOps Services 9")](https://www.goodfirms.co/company/miracuves-solution) In short Miracuves provides MLOps services that get machine learning and LLM models into production and keep them accurate: training pipelines, a model registry, CI/CD for models, batch, real-time or GPU serving, drift monitoring, retraining triggers and rollback, on MLflow, Kubeflow, SageMaker, Vertex AI or Azure ML. A scoped MLOps module starts from $3,699; custom platforms take 2-8 weeks. You own every pipeline and config. Our MLOps approach ## How Miracuves runs MLOps - from the first deploy to the fifth retrain Most models that stall do not fail on accuracy. They fail in the gap between a data scientist's notebook and a service someone has to keep running at 3am. Miracuves closes that gap with a short list of parts: a training pipeline that runs from a commit instead of a laptop, an experiment tracker, a model registry that records what is live, serving sized to your latency budget, and monitoring that watches the inputs and the predictions, not only the servers. We add those parts in the order they pay off. One churn model scored nightly needs a scheduler, a registry and a drift report, not a Kubernetes cluster. Ten real-time models behind a checkout need autoscaled endpoints, canary releases and perhaps a feature store. If you want a plan before a build, MLOps consulting starts with the same one-model audit; wider AI strategy sits with our [AI consulting](https://miracuves.com/service/ai-consulting/) team. **Who this service is built for:** Product and data teams that already have one or more models and need them deployed, monitored and retrained without a data scientist watching over them; startups shipping an LLM feature that needs prompt versioning, evaluation and a ceiling on token spend; and engineering leads who have inherited models nobody can reproduce. If there is no model yet, start with [machine learning development](https://miracuves.com/service/ml-development/). If you only need CI/CD and infrastructure for ordinary application services, [DevOps engineering](https://miracuves.com/service/devops-engineering/) is the better fit. - **Training pipelines as code:** Kubeflow, Airflow or SageMaker Pipelines, fired by a commit, a schedule or fresh data - **Experiment tracking and a model registry:** MLflow or your cloud's registry, with staging, production and archived versions - **CI/CD for models:** data validation, tests and a head-to-head against the live model before any promotion - **Serving that fits the workload:** nightly batch jobs, real-time endpoints on autoscaled containers, or GPU inference for large models - **Monitoring and retraining triggers:** data drift, prediction drift and live accuracy, with alerts and a written rollback Written by the Miracuves MLOps Engineering Team · Updated September 2026[View delivered projects →](https://miracuves.com/portfolio/) **9,000+**Projects delivered since 2010**3,900+**Apps published by Miracuves**90+**Ready-made solutions to start from**6 days**Ready-made clone delivery**2-8w**Miracuves custom MLOps build timelines**100%**Source code ownership **Pipelines**Retrain on trigger**Serving**Batch or real time**Monitoring**Drift and accuracy ### Why MLOps at Miracuves - Custom MLOps build**2-8 weeks** - Model versions tracked**Every run, every release** - Serving modes**Batch · Real-time · GPU** - Drift monitoring**Inputs and predictions** - Platforms**MLflow · SageMaker · Vertex AI** - Source code ownership**100% yours** Example engagement: Taking over a fraud model, 6 weeks > A payments team runs its fraud model from a notebook and retrains it by hand each quarter. An engagement like this moves training into a scheduled pipeline, registers every version in MLflow, serves the model behind a real-time endpoint with the next version running in shadow, and alerts on drift in the inputs. The target: a retrain that ships in an afternoon, and a rollback that is one config change. What we set up ## What Miracuves builds - the MLOps pieces teams ask for most Six MLOps builds we scope most often. Each is custom work in your own cloud account, quoted in writing and delivered in 2-8 weeks. [Discuss Your MLOps Setup](https://miracuves.com/contact/) [*01*Model takeover2-8 weeks ### Notebook to production pipeline A model that lives in one person's notebook is rebuilt as a versioned training pipeline, registered in MLflow and served behind an API - with the original result reproduced first, so nothing changes silently. MLflowDockerReproducible](https://miracuves.com/contact/)[*02*Real-time2-8 weeks ### Low-latency model serving Models served on autoscaled containers with a latency budget per request, a fallback answer when the model is slow, and canary releases that send a slice of traffic to each new version first. KubernetesFastAPICanary](https://miracuves.com/contact/)[*03*Monitoring2-8 weeks ### Drift and performance monitoring Live input distributions compared with the training data, the prediction mix tracked daily, and true accuracy measured once outcomes arrive - with alerts routed to the person who can act on them. PrometheusGrafanaDrift alerts](https://miracuves.com/contact/)[*04*Retraining2-8 weeks ### Automated retraining loop Retraining fired by a schedule, a drift alert or a fresh batch of labels. The new model is scored against the live one on recent data and promoted only if it wins. AirflowKubeflowChampion vs challenger](https://miracuves.com/contact/)[*05*LLMOps2-8 weeks ### LLM evaluation and guardrails Prompts versioned like code, an evaluation set of real questions run on every change, input and output guardrails, and token spend tracked per feature so a prompt edit cannot double the bill unnoticed. Eval setsPrompt registryToken budgets](https://miracuves.com/contact/)[*06*GPU2-8 weeks ### GPU inference and cost control Large models served on GPU instances with request batching, quantized weights where accuracy allows, and scale-to-zero for bursty traffic, so idle GPUs stop appearing on the cloud bill. GPU servingAutoscalingCost reports](https://miracuves.com/contact/) **Honest note**MLOps pays off once a model matters to revenue or risk and changes more than once a quarter. A single model scored weekly by a stable process may need nothing more than a scheduled job and a drift report, and we will say so. We size the platform to the models you run, not to a reference architecture. Delivery comparison ## Miracuves MLOps vs a cloud ML platform alone vs an in-house team - which fits? Every major cloud sells an ML platform, and it is tempting to think that buying one is the MLOps work. The platform supplies the parts: pipelines, a registry, endpoints. Someone still has to wire them to your data, decide what fires a retrain and answer the drift alert at the weekend. The real choice is who does that wiring, and who owns it afterwards. MetricFive things that decide cost, speed and reachMiracuves default ### Miracuves MLOps Pipelines, registry, serving and monitoring wired in your cloud ### Cloud ML platform alone SageMaker, Vertex AI or Azure ML, assembled by your team ### In-house MLOps team Hire the specialists and build it yourself *01*Time to first automated deploy**2-8 weeks**Pipeline, registry, serving and alerts set up**Weeks to months**The services exist; wiring them is on you**Months**Hiring comes before building*02*Monitoring and retraining**Designed in**Drift, accuracy and retrain triggers from day one**Available, not configured**Thresholds and follow-up actions are yours to set**Depends on the team**Often added after the first silent failure*03*Lock-in**Low**Open tools such as MLflow where they fit; configs in your repo**Higher**Pipelines written against one vendor's SDK**Your choice**Whatever the team standardizes on*04*Ongoing cost**Cloud bill, optional retainer**From $2,299/month if you want us on call**Cloud bill plus staff time**Someone on your side still owns it**Salaries plus cloud bill**Cover for leave means more than one hire*05*Best for**Teams with models but no MLOps**One model to dozens, LLM features included**Cloud-fluent ML engineers**With time to assemble the parts**ML as the core product**Many models changing every week Choose Miracuves MLOps if… Your models work in a notebook but not reliably in production · retraining is a manual job · nobody is alerted when predictions drift · or you are adding an LLM feature and need evaluation and cost control. Consider an alternative if… You have no model yet - start with [machine learning development](https://miracuves.com/service/ml-development/) · or you need CI/CD and infrastructure for ordinary services rather than models. [See DevOps Engineering →](https://miracuves.com/service/devops-engineering/) MLOps guide ## What to know before you hire an MLOps services company The questions a product or data lead should settle before paying anyone to run models in production: what MLOps covers, whether you need it yet, which platform, and what it costs to keep running. ### What does an MLOps service actually include? MLOps is the engineering that turns a trained model into a service you can update safely. In practice that means five pieces: a training pipeline that runs from code on a schedule or trigger; experiment tracking, so any result can be reproduced; a model registry that records which version is live; a deployment path with tests and a comparison against the current model; and monitoring that watches the model's inputs, outputs and accuracy. What it does not include is building the model. If your team has no model yet, that is [machine learning development](https://miracuves.com/service/ml-development/) work. MLOps starts once there is something worth deploying, and it pays for itself the second and third time that model is retrained. ### When do you need MLOps, and when is it overkill? You need it when a model affects revenue or risk and has to keep changing: fraud scores, prices, recommendations, demand forecasts. The signs you are late are familiar. Nobody can say which data trained the live model, retraining means a person re-running a notebook, or you learned about a bad model from a customer. It is overkill for a single model scored once a month by a stable process. A scheduled job, a saved model file with a version number and a monthly accuracy check may be enough, and we will say so rather than sell a platform. The right amount of MLOps grows with the number of models and how often they change. ### SageMaker, Vertex AI, Azure ML or open-source tools - which should you use? Start with the cloud your data already lives in. Moving training data between clouds costs money and adds a security review, so a team on AWS usually lands on SageMaker, a Google Cloud team on Vertex AI and an Azure team on Azure Machine Learning. The managed platforms spare you from running servers for pipelines, registries and endpoints. Open tools - MLflow for tracking and the registry, Kubeflow or Airflow for pipelines, containers on Kubernetes for serving - make sense when you run on more than one cloud, must run on-premises, or want to avoid writing every pipeline against one vendor's SDK. Mixing is common, for example MLflow tracking on top of a managed training service. ### How do drift monitoring and retraining triggers work? Two kinds of drift matter. Data drift means live inputs no longer look like the training data: a new customer segment, a changed upstream field, a seasonal swing. Prediction drift means the outputs have moved, such as the share of transactions flagged as fraud doubling overnight. Both can be measured the moment predictions are made. Accuracy itself can only be measured once true outcomes arrive, which may take days or weeks. A retrain can be fired by any of the triggers below. Whatever fires it, the new model goes live only if it beats the current one on recent data. - A schedule, for data that moves at a known pace. - A drift threshold on key features or on the prediction mix. - A batch of new labels large enough to test on. - Measured accuracy falling below an agreed floor. ### What is LLMOps, and how is it different from MLOps? LLMOps applies the same discipline to products built on large language models, but the moving parts differ. You rarely retrain the model. Instead you change prompts, retrieval settings and the model version you call, and each change can alter answers in ways that are hard to spot. So prompts are versioned like code, and every change runs against an evaluation set of real questions with expected answers before it ships. Guardrails check inputs and outputs for leaked personal data, off-topic requests and unsafe replies. Token spend is tracked per feature and per customer, because a longer prompt or a switch to a larger model can multiply the bill. For retrieval-based products see [RAG development](https://miracuves.com/service/rag-development/); for the model work itself, [LLM development](https://miracuves.com/service/llm-development/). ### What does it cost to run models in production? Three lines make up the bill. Serving: a tabular model behind an API usually runs on ordinary CPU containers, while deep learning and LLM serving need GPUs, which cost far more per hour and should scale down when idle. Training: scheduled retrains are modest unless they need GPUs or very large data. Monitoring and storage: logs, prediction history and feature data grow every day unless retention limits are set. The main cost levers are batch scoring wherever real time is not needed, right-sized instances, request batching and quantized models on GPUs, and scale-to-zero for bursty traffic. The recurring cost teams forget is people: someone has to answer alerts and approve retrains. Our retainer from $2,299/month covers that after the 60 days of included support. ### How do you evaluate an MLOps vendor? Ask them to show rather than describe. These five questions separate a working platform from a slide deck. A vendor whose answers all depend on its own proprietary platform is selling lock-in along with the service. With Miracuves, the pipelines, configs and runbooks sit in your repositories and your cloud, and your own engineers can run them after handoff. - Can they reproduce your current live model from raw data before changing anything? - Will pipelines and infrastructure be written as code, in your repository and your cloud account? - How exactly is a new model promoted, and what happens step by step during a rollback? - What do they monitor besides CPU and memory - inputs, predictions, accuracy, cost? - Who answers a drift alert after handoff, and what does the runbook tell them to do? Technical architecture ## How Miracuves structures an MLOps platform Four choices decide whether a model can be retrained, released and rolled back on a Tuesday afternoon without a meeting. We settle them in the first week, before any pipeline is written. - *01* ### Lineage - every live model traces back to data, code and parameters Each training run logs its data snapshot, git commit, parameters and metrics to MLflow. The registry entry for a production model points at that run, so when a prediction is questioned you can say exactly which data and which code produced it. - *02* ### Promotion - CI/CD with a champion and a challenger A new model stays a challenger until it beats the live champion on recent holdout data and passes latency tests. Promotion runs through the pipeline, never by copying a file: shadow traffic first, then a canary slice, then everyone. Rollback re-points the endpoint at the previous registry version. - *03* ### Serving - ML model serving matched to the latency budget Batch scoring writes predictions to a table on a schedule and costs least. Real-time endpoints on Kubernetes or a managed endpoint answer in milliseconds and autoscale. GPU serving is kept for large or deep models, with request batching so each GPU stays busy. A feature store comes in only when training and serving must compute the same fresh features at request time. - *04* ### Observability - drift, accuracy and cost on one dashboard Prometheus collects latency and error rates, a drift job compares live inputs with the training distribution, and outcomes are joined back to predictions as they arrive. Cost per thousand predictions sits beside accuracy, so a cheaper model that is nearly as good is easy to spot. What most MLOps setups get wrong A registry nobody promotes through, so the live model is whatever file was copied last. Scheduled retraining with no comparison against the current model. Monitoring on CPU and memory but not on the predictions. A feature store bought before a second model exists to share it. Each is cheap to prevent in week one and expensive to unpick after a bad release. promote.py - MLflow champion vs challenger ``` # Promote a retrained model only if it beats production# Serving loads whichever version holds the "champion" aliasfrom mlflow import MlflowClient, register_modelclient = MlflowClient()MODEL = "fraud-scorer"def promote_if_better(run_id, metric="auc", margin=0.005): champ = client.get_model_version_by_alias(MODEL, "champion") old = client.get_run(champ.run_id).data.metrics[metric] new = client.get_run(run_id).data.metrics[metric] if new < old + margin: return champ.version # keep the live model mv = register_model(f"runs:/{run_id}/model", MODEL) client.set_registered_model_alias(MODEL, "previous", champ.version) client.set_registered_model_alias(MODEL, "champion", mv.version) return mv.version ``` Registers a retrained model and moves the champion alias only when it beats the live model by a set margin on the same metric. The serving endpoint loads whatever version holds that alias, so rollback is one alias change back to the previous version. Both scores must come from the same recent holdout data, or the comparison means nothing. Our service models ## Three ways Miracuves delivers MLOps Each option is a contract with Miracuves as a company: MLOps, data and platform engineers under one written scope, and one party answerable for the pipeline in production. Choose by how many models you run and how often they change. Most PopularCustomer appPartner appAdminFixed Scope · Fixed Price ### Scoped MLOps Module Miracuves takes one existing model to production on a fixed scope - training pipeline, model registry, serving endpoint and drift dashboard - in 2-8 weeks, in your cloud. The pipelines, configs and runbooks are yours. - From $3,699 - the published starting price - One model, served in batch or real time - Training pipeline, MLflow registry and monitoring included - Rollback runbook and on-call handover - Full source code · infrastructure as code · NDA from day one · 60-day support PlatformPipelinesRegistryServingData checksCI/CDMonitoringCustom Build · Full Platform ### Custom MLOps Platform Miracuves designs the platform around your model portfolio - several models, shared features, GPU serving or LLM evaluation - on SageMaker, Vertex AI, Azure ML or open tooling on Kubernetes. Team: MLOps engineer, data engineer, platform engineer, QA and PM. - Scoped and priced before work begins - Multi-model registry, CI/CD and canary releases - Feature store only where two models share features - LLMOps: prompt versions, eval sets, guardrails, token budgets - Full source code · infrastructure as code · IP 100% yours Wk 1Wk 2Wk 3Wk 4MLOps Retainer · Monthly ### Ongoing MLOps Miracuves runs the platform after launch - answering drift alerts, approving retrains, upgrading dependencies, tuning serving cost and bringing new models onto the pipeline - on a monthly retainer. - From $2,299/month - two weeks' notice to cancel, any time - Named MLOps engineers on your models - Direct access to the engineers who run your pipelines - no account manager relay - Monthly report - drift, accuracy, latency and serving cost per model - Scope moves up or down as models are added or retired Quality standards ## How Miracuves holds every MLOps delivery to a production standard Before a pipeline is handed over it passes the gates we would want if we were the ones paged at night. Each is checked against the running system, not against a slide. - Reproducible runs - data snapshot, commit and parameters logged for every modelLineage - Registry-only releases - production serves a registered version, never a copied fileRelease - Champion vs challenger - a new model goes live only when it beats the current onePromotion - Serving load-tested - latency and throughput profiled at production volumeServing - Data validation - schema and range checks stop bad inputs before training or scoringData quality - Drift and accuracy monitoring - every alert routed to a named ownerMonitoring - Rollback rehearsed - the previous version restored in staging before go-liveRecovery Enforced release gates ## Six release gates every pipeline passes Every pipeline, serving endpoint and monitoring rule clears all six gates before the repository and the cloud accounts are handed over. **01** #### Reviewed pipeline and infrastructure code A senior Miracuves MLOps engineer reviews every pull request - pipeline definitions, Terraform, serving code and alert rules. Infrastructure changes are planned and diffed before they apply, never clicked into a console. **02** #### Tests at three layers Unit tests on feature and validation code, an end-to-end pipeline run on a small sample dataset in CI, and contract tests that pin the serving API's inputs and outputs, so an app release and a model release cannot break each other. **03** #### Load and failure testing Endpoints are load-tested at the traffic production will send, and we deliberately kill a pod or a node to confirm that autoscaling and the fallback response behave as written. **04** #### A handoff your engineers can run Pipeline and infrastructure code, registry access, dashboards, alert routing, the retrain runbook and the rollback runbook, plus a recorded walkthrough for the people who will own the platform. **05** #### Rollback drill before launch Before go-live we promote a model and roll it back in staging with your team watching. If rollback is not a single documented step, the gate is not passed. **06** #### 60-day watch after go-live For 60 days after launch Miracuves watches drift, accuracy and serving cost, retunes alert thresholds that fire too often or too late, and recommends the first retrain from evidence rather than the calendar. Technology stack ## The MLOps tools Miracuves works with We start from what your team already runs: open tools where lock-in matters, a managed cloud platform where it saves your team time. *Mf***MLflow**Experiment tracking · model registry*Kf***Kubeflow**Pipelines on Kubernetes*Af***Apache Airflow**Scheduled data and retrain jobs*SM***Amazon SageMaker**Managed training · endpoints*VA***Vertex AI**Google Cloud pipelines · endpoints*Az***Azure Machine Learning**Azure registry · managed endpoints*Dk***Docker**Identical training and serving images*K8***Kubernetes**Autoscaled model serving*Tf***Terraform**Infrastructure as code*GH***GitHub Actions**CI/CD for pipelines and models*FA***FastAPI**Custom inference APIs*vL***vLLM**High-throughput LLM serving on GPU*Fs***Feast**Feature store, when it earns its place*Ev***Evidently**Data and prediction drift reports*Pr***Prometheus**Serving metrics · alerting*Gr***Grafana**Model health dashboards Our process ## From first call to a model you can retrain - what happens and when Whether you start with one model or a platform for many, the work follows the same five steps. At each one you know what Miracuves is doing, which cloud access we need from you and what you receive. Custom builds take 2-8 weeks; larger platforms are billed by milestone and pass the same checkpoints. 1. **Step 01** ### Brief & NDA Tell us on WhatsApp which models you run and where they live. NDA signed before any model or dataset is shared. 2. **Step 02** ### Audit & Plan We reproduce one model, map its data and serving path, and write the scope and price. No payment before scope is agreed. 3. **Step 03** ### Build & Wire Pipelines, registry, serving and alerts built as code in your repo, with a working demo every week. 4. **Step 04** ### Test & Drill Load tests on each endpoint, a failure drill, and a promote-and-rollback rehearsal in staging. 5. **Step 05** ### Launch & Handoff Production cut-over, runbooks and dashboards handed over, then 60 days of active support. **NDA First**Before any model or dataset is shared**2-8 Weeks**Custom MLOps build**Weekly**Working demo every week**60 Days**Post-launch support ### Our weeks count build time, not waiting time The weeks we quote are ours. What can stretch them sits on your side: cloud account access and IAM roles, your security team's review before we touch production data, and someone who can confirm what a correct prediction looks like. We list what we need on the first call, so those can start in parallel. [See what you provide](https://miracuves.com/facts/#fact-005)FACT-005, audited quarterly Transparent pricing ## What MLOps services cost at Miracuves MLOps prices are published here because a platform budget should not depend on who is asking. Nothing is added after the scope is agreed. ### Scoped MLOps Module $3,699 from Fixed scope · one model · 2-8 weeks · published price - One model taken to production - pipeline, registry and serving - Drift and latency dashboard included - MLflow tracking and model registry - Pipeline and infrastructure code on handoff - 60-day post-launch support - NDA protected from day one [Start an MLOps Project](https://wa.me/919830009649)Most Requested ### Custom MLOps Platform Custom Quote Scoped before build · milestone billing - MLOps, data and platform engineers on one team - Multi-model CI/CD, canary releases and retraining - Serving on SageMaker, Vertex AI, Azure ML or Kubernetes - LLMOps - eval sets, guardrails, token budgets - Full source code · infrastructure as code · IP transfer - Billed by milestone - nothing paid ahead of delivery [Get a Scope & Quote](https://miracuves.com/contact/) ### Ongoing MLOps $2,299 /mo Monthly retainer · two weeks' notice to cancel - Named MLOps engineers on your platform - Drift alerts answered, retrains approved - Dependency and security upgrades - Serving cost reviewed every month - Scales with the number of models - All pipelines and configs remain 100% yours [Discuss Ongoing MLOps](https://wa.me/919830009649) **Why Miracuves publishes prices**Knowing the cost upfront lets you decide which models deserve MLOps first. If your platform needs more - GPU serving, many models, strict audit trails - Miracuves explains exactly why before the scope changes, and puts the new figure in writing. ### What affects MLOps cost at Miracuves A scoped MLOps module keeps its fixed price when it covers one model on one cloud with batch or standard real-time serving. Custom platforms scale with: the number of models, how many data sources feed them, real-time latency targets, GPU serving, retraining frequency, whether a feature store is needed, LLM evaluation and guardrail scope, audit requirements, and how much existing infrastructure has to be migrated. ### Typical MLOps budget ranges - **Scoped MLOps module**from $3,6992-8 weeks - **Custom MLOps platform**$18,000-$80,0002-8 weeks; larger scopes are quoted in writing before work starts - **Ongoing retainer**from $2,299/month for platform care and new models Every MLOps quote is written before any payment, so there are no surprise invoices after kickoff. Example engagement ## What a typical MLOps project looks like at Miracuves An illustrative example of a typical project of this kind, with client details anonymized. Figures show what this kind of build targets, not a named client's results. The example: a marketplace runs a pricing model and a fraud model, both trained in notebooks and deployed by copying files to a server. Retraining happens when someone remembers, and nobody is alerted when the live predictions start to slide. 1. **01** ### The Challenge Two models, no registry and no record of which training data produced the versions in production. Features are computed one way in the notebook and another way in the app, so live predictions drift away from test results. The brief: automated retraining, safe releases and alerts before customers notice. 2. **02** ### What this kind of build delivers An Airflow pipeline that retrains both models weekly, MLflow tracking and registry, a champion-vs-challenger gate, one shared feature library for training and serving, real-time endpoints on Kubernetes with canary releases, and drift dashboards in Grafana. 3. **03** ### What it targets Retrains that ship without an engineer on the call, any release rolled back in one step, and a drift alert within a day of a shift in the inputs. These are the targets a build like this is scoped against and checked during the 60-day support window, not reported results. **6 Weeks**Typical build**2**Models on one pipeline**1 step**Rollback target [View All Case Studies](https://miracuves.com/portfolio/) Project Brief - Solution type**MLOps platform (example)** - Delivery timeline**6 weeks** - Infrastructure**Kubernetes + MLflow** - Key integrations**Airflow · Grafana · app API** - Models**Pricing + fraud** - Source code**100% client-owned** **Weekly**Automated retrain**1 step**Rollback target**< 1 day**Drift alert target Client Reviews ## What clients say about building with Miracuves Named Miracuves clients, in their own words. Each built a product with a ranking, recommendation or monitoring layer of their own on top - the kind of system MLOps exists to keep healthy - rather than buying this MLOps service. Read every testimonial on our [client testimonials page](https://miracuves.com/client-testimonials/). Client testimonial > "Player, CMS, subscriptions and the apps were all there. We brought the content pipeline, our recommendation logic and the parental-control layer." *BH***Ben Loyd Holmes**Co-founder & CTO, URView Media*OTT platform with the client's own recommendation logic on top*Client testimonial > "We added our tipping/collaborator-payouts module, our custom feed ranker, and a creator-tools dashboard. We launched the beta in 5 months and the full feature set in 10." *NO***Nico Orlando**Co-founder & CEO, Polyvibe Inc*Creator social app with a custom feed ranker, built on MXSocial*Client testimonial > "We were managing mining hardware across sites with scripts and spreadsheets. This was genuinely custom work: rig monitoring, hashrate reporting, payout accounting and alerting, built to how we actually operate. It came in under a month and the operations team stopped guessing." *GR***Gregnor Ratinik**Founder, Telcmining*Fully custom operations platform with monitoring and alerting* **6,000+**Clients served**3,900+**Apps published**35+**Industries served[Read All Reviews](https://miracuves.com/reviews-awards/) Why Miracuves ## Six places to check us before you ever call us Each one is either run by someone else or open to anyone. Check them in any order; the whole list takes about a minute. [Why clients choose Miracuves](https://miracuves.com/why-us/) [**01** ### Company registration Miracuves Solutions Pvt. Ltd., CIN U62099MH2023PTC406639. Search the CIN on the Ministry of Corporate Affairs portal. mca.gov.in](https://www.mca.gov.in/) [**02** ### Every number, sourced Projects, clients, prices and timelines, each one defined and sourced on our public facts ledger. miracuves.com/facts](https://miracuves.com/facts/) [**03** ### Reviews on Clutch Client reviews published by Clutch, an independent B2B review platform, not by us. clutch.co](https://www.clutch.co/profile/miracuves-solutions) [**04** ### Reviews on GoodFirms A second, separate review platform. Read what clients wrote there too. goodfirms.co](https://www.goodfirms.co/company/miracuves-solution) [**05** ### The product itself Web app, admin panel and APK with printed credentials. Try the real thing before a single call. miracuves.com/solutions](https://miracuves.com/solutions/) [**06** ### Clients, by name Named clients describing their launches, in their own words. miracuves.com/client-testimonials](https://miracuves.com/client-testimonials/) ### Three promises we would stake the company on Every promise on this site rests on these three. Each one is something you can check, not something you have to take on trust. - #### 01People you can name Our leadership is public, with real LinkedIn profiles, not a stock-photo team page. A named team works your build and sends you progress on WhatsApp every working day. [Meet the leadership](https://miracuves.com/leadership/) - #### 02Proof over promises Every number we publish, pricing, timelines, project counts, is defined and sourced on a public facts ledger. If we can't back a claim, we don't make it. [Read the facts ledger](https://miracuves.com/facts/) - #### 03A process with a deadline Ready-made platforms go from kickoff to live deployment in 6 working days, guaranteed: miss it for reasons on our side and we work free until launch. Custom builds get a fixed quote after a free feasibility study. [Get a feasibility study](https://miracuves.com/schedule-consultation/) Industries ## Industries we build MLOps for What drifts, how fast true labels arrive and how strict the audit trail must be all change by industry. These industry pages go deeper on each. [### Healthcare & Life Sciences Audit trails on every model version, and monitoring for shifts in patient mix or coding practice. View industry](https://miracuves.com/industries/telemedicine/)[### Telemedicine Triage and scheduling models watched for drift as visit volumes and case mix change. View industry](https://miracuves.com/industries/telemedicine/)[### Fintech Fraud and credit models retrained on late-arriving labels, with lineage for every decision. View industry](https://miracuves.com/industries/banks-insurance/)[### Retail & E-commerce Recommendation and demand models retrained around seasons, promotions and new catalog items. View industry](https://miracuves.com/industries/retail-ecommerce/)[### Media & Entertainment Ranking and tagging models served at peak traffic, with a canary release for every change. View industry](https://miracuves.com/industries/media-entertainment/)[### Creator Economy Feed rankers and moderation models kept current as content and creator behavior shift. View industry](https://miracuves.com/industries/creator-economy/)[### Transportation & Mobility ETA, pricing and demand models on real-time endpoints with tight latency budgets. View industry](https://miracuves.com/industries/transportation-mobility/)[### Food & Beverage Demand and prep-time forecasts retrained on daily data, with drift alerts around holidays. View industry](https://miracuves.com/industries/food-beverage/) Related Services ## Also building production AI systems with these services at Miracuves MLOps sits between the people who build models and the people who run infrastructure. These are the services on either side of it. [Model building**Machine Learning Development**](https://miracuves.com/service/ml-development/)[Generative AI**LLM Development**](https://miracuves.com/service/llm-development/)[Retrieval**RAG Development**](https://miracuves.com/service/rag-development/)[Infrastructure**DevOps Engineering**](https://miracuves.com/service/devops-engineering/)[Pipelines**Data Engineering**](https://miracuves.com/service/data-engineering-app-development/)[Containers**Docker & Kubernetes**](https://miracuves.com/service/docker-kubernetes-development/)[Strategy**AI Consulting**](https://miracuves.com/service/ai-consulting/)[Talent**Hire AI Developers**](https://miracuves.com/service/hire-ai-developers/) [Further reading**Self-Hosted LLM vs API Cost: An 85% Savings Benchmark***The in-depth version, on the Miracuves blog.* Read the guide](https://miracuves.com/blog/self-hosted-llm-vs-api-cost-benchmark/) Frequently Asked ## Questions about MLOps services at Miracuves Something not covered here? Ask on WhatsApp and you will usually have an answer within two hours. [Ask us directly](https://wa.me/919830009649) We already have models in production. Can you add MLOps around them? Yes, and most engagements start this way. We first reproduce the live model's result from the data and code you have, which shows whether the version in production can be rebuilt at all. Then we move training into a versioned pipeline, register the current model as the baseline and add monitoring before changing anything else. Your data scientists keep the modeling decisions. How much do MLOps services cost at Miracuves? A scoped MLOps module - one existing model taken to production with a training pipeline, model registry, serving endpoint and drift dashboard - starts from $3,699 and takes 2-8 weeks. A custom MLOps platform typically runs $18,000-$80,000 and takes 2-8 weeks, with larger scopes quoted in writing before work starts; the number of models, real-time and GPU serving, retraining frequency, a feature store and LLM evaluation scope decide where it lands. Ongoing MLOps is available from $2,299/month. Every quote is written before payment, with no surprise invoices after kickoff. Do we need a feature store? Usually not at first. A feature store earns its place when several models share features, or when a real-time model needs fresh values - counts over the last hour, say - computed identically in training and serving. With one or two batch models, shared feature code in a library does the same job for far less effort. We add one, such as Feast or your cloud's managed store, when the second model needs it. Will you work inside our own cloud account? Yes. We build in your AWS, Google Cloud or Azure account with roles you grant and can revoke, and everything is defined as infrastructure code in your repository. Nothing runs on a Miracuves-owned platform, so the day the engagement ends your team keeps the pipelines, the registry, the endpoints and the dashboards exactly as they were. How do you roll back a bad model release? The serving endpoint always loads a model by its registry version or alias, never a loose file. Rolling back points it at the previous version, which the registry keeps with its artifacts, and takes minutes. New versions also go out gradually - shadow traffic first, then a canary slice - so most bad releases are caught before full traffic reaches them. We rehearse a rollback with your team before go-live. Can models run on-premises or inside a private network? Yes. When data cannot leave your network, we deploy open tooling - MLflow, Kubeflow or Airflow, and containerized serving on your own Kubernetes cluster - inside it. GPU serving works the same way on your hardware. Pipeline changes arrive as reviewed code, so nobody outside needs standing access to production data. How is MLOps different from DevOps? DevOps ships code: if the tests pass, the release behaves as it did in testing. A model can pass every test and still get worse in production, because the data changes under it. MLOps adds what DevOps lacks - data validation, experiment tracking, a model registry, evaluation against the live model and monitoring of predictions. For CI/CD and infrastructure for ordinary services, see our [DevOps engineering](https://miracuves.com/service/devops-engineering/) service. What do we own when the engagement ends? Everything. The pipeline and infrastructure code, the model registry with every version and its lineage, the serving configuration, dashboards and alert rules, and the runbooks for retraining and rollback all sit in your repositories and cloud accounts. You get 100% source code ownership, and 60 days of post-launch support are included. Can you add LLMOps to a product built on your ChatGPT clone? Yes. Our ready-made [ChatGPT clone](https://miracuves.com/chatgpt-clone/) ships in 6 working days, and its admin panel already includes usage analytics and moderation tools. Prompt versioning, an evaluation set run on every prompt or model change, output guardrails and per-feature token budgets on top of it are custom LLMOps work, scoped and quoted separately and delivered in 2-8 weeks. How do you handle audit trails for models in regulated products? Every production model links back to the run that trained it: the data snapshot, the code commit, the parameters, the evaluation results and who approved the promotion. Prediction logs can be stored with the model version that produced them, so a single decision can be traced later. We build to the controls frameworks such as SOC 2 and HIPAA require; certifying your system remains your auditor's call. Get Started ## Ready to put your models into production with Miracuves? Tell Miracuves which models you run, where they are trained and how they reach users today. We will tell you how much MLOps you actually need, which service model fits and how long it takes - in writing, before you commit to anything. **9,000+**Projects since 2010**2-8 Weeks**Custom MLOps build**100%**Source code yours**NDA First**Before any model or dataset is shared [WhatsApp - Start Now](https://wa.me/919830009649)[Book a Free Consultation](https://miracuves.com/schedule-consultation/)[Contact & Brief Form](https://miracuves.com/contact/) NDA signed before you share a single model or dataset Page reviewed by the Miracuves MLOps Engineering Team · Last updated September 2026 · [Clutch & Google Reviews](https://miracuves.com/reviews-awards/) ## Disclaimer **Miracuves is an independent software development company.** We are not affiliated with, connected to, sponsored by, or endorsed by any of the brands or platforms named on this page. Names of the form “Brand Clone” are used descriptively: it is how the software industry refers to building a platform with functionality comparable to a known service, and how clients search for it. The entire design and codebase of our products is built by our own team and contains no code, design, graphics, or content originating from any third-party website or application. All third-party names and marks are the property of their respective owners. [Full Legal Notice & Disclaimer](https://miracuves.com/disclaimer/)