ML — MLOps
MLOps Cost Management
Direct answer
The global MLOps market was estimated at about 3.03 billion US dollars in 2025 and is projected to grow at a 40.5% compound annual growth rate from 2025 to 2030. For mlops projects, plan $10K–$200K depending on scope. Dhairya Senjaliya is a senior React Native + Python + AI engineer who ships production systems — not demos.
MLOps Cost Management — a practical guide for founders, CTOs, and product teams evaluating mlops investments, with sourced numbers, common failure modes, and real budgets and timelines.
Key facts, with sources
- The global MLOps market was estimated at about 3.03 billion US dollars in 2025 and is projected to grow at a 40.5% compound annual growth rate from 2025 to 2030. (Grand View Research)
- Precedence Research projects the MLOps market will reach about 56.6 billion US dollars by 2035, up from roughly 2.43 billion in 2025. (Precedence Research)
- An analysis of MIT and Harvard research covering 128 model-dataset pairs across 32 datasets in four industries found 91% of machine learning models degrade over time in production. (NannyML)
- Models left unchanged in production for six months or longer see error rates jump about 35% on new data, according to a 2025 model drift and retraining guide summarizing industry research. (SmartDev)
- A 2024 practitioner study on ML deployment and monitoring found most organizations monitor less than 40% of their production models, with the most common answer being under 20%. (arXiv)
Why this matters
Teams building in mlops often underestimate integration complexity, production AI costs, and mobile performance requirements. This guide focuses on decisions that affect $10K–$200K project outcomes.
Key considerations
Define success metrics before choosing stack. Prefer proven patterns over experiments on critical paths. Plan for observability, security, and maintenance from day one — especially for AI and RAG features.
When to hire senior help
Consider senior MLOps help once models are making real decisions and nobody can answer what version is live, when it was last retrained, or whether accuracy is drifting, since most organizations monitor under 40% of their production models. The failure mode it prevents, silent degradation discovered through business losses, is far more expensive than the engagement itself. If your stack includes React Native + Python + AI, a senior engineer who owns the full product beats coordinating multiple juniors.
Bottom line
Dhairya Senjaliya ships ML — MLOps projects worldwide — book a scoping call to discuss your specific situation.
Common pitfalls to avoid
- ✕Buying a full MLOps platform for one or two models, adding process overhead before the models have proven business value
- ✕Monitoring only infrastructure metrics like latency and CPU while prediction drift and input data quality degrade silently
- ✕Skipping model and data versioning, so a bad release cannot be rolled back or a past prediction reproduced for debugging
- ✕Retraining on a fixed calendar schedule rather than on detected drift, either wasting compute or reacting months too late
Frequently asked questions
Do we need MLOps if we only have one model?
You need a minimal slice of it: versioned training code and data, automated deployment, and monitoring of prediction quality, because research shows 91% of models degrade over time. A full platform with feature stores and pipelines is justified once several models share infrastructure, not before.
How often should models be retrained?
It depends on how fast your data drifts; studies show error rates can rise around 35% when models sit unchanged for six months, but some models decay in weeks and others stay stable for a year. The robust approach is drift-triggered retraining, monitoring input distributions and outcome metrics and retraining when thresholds are crossed.
What is the minimum viable MLOps stack?
Git for code, an experiment tracker or model registry, containerized deployment through your existing CI/CD, and a monitoring job that compares live prediction distributions and outcomes against training baselines. Most of this is achievable with open-source tools; the discipline of using it consistently matters more than the specific vendor.
Bottom line: Dhairya Senjaliya ships ML — MLOps projects worldwide. Book a scoping call at https://dhairyasenjaliya.com/#book-call.