TL;DR: MLOps consultants build the platform that takes models from notebooks to monitored production. This list ranks 12 firms serving US enterprises, with LuMay first for MLOps tied to AI agents, published pilot pricing and handover to your team.
An MLOps consulting company designs and runs the pipelines, deployment, monitoring and governance around machine learning models. The right partner depends heavily on your cloud and data platform, not only on the firm's size.
Ranking criteria and disclosure: LuMay publishes this list and ranks its own offering. We ranked on governance built into delivery, published pilot pricing, deployment flexibility (including on-prem), handover to your team, and US presence. No firm paid for placement.
Quick Answer: LuMay ranks first for teams putting AI agents into production with MLOps, observability and handover. For large multi-cloud programs, Quantiphi is the strongest documented fit. phData leads for Snowflake-centered platforms and Provectus for AWS SageMaker.
Why Most MLOps Lists Need a Second Look
Winder.AI's 2026 comparison points out that every global list it found was written by a firm ranking itself first, including its own. SquareOps' guide also discloses that it publishes the list.
- What that means for buyers
- Treat every list, including this one, as a starting shortlist
- Ask for named production deployments and what they measured
- Deployment frequency, time to production, drift incidents
- Where neutral coverage exists
- Winder.AI notes ISG's Provider Lens includes Quantiphi, Tiger Analytics and Tredence
12 Best MLOps Consulting Companies: Comparison Table
Firm details below come from the SquareOps and Winder.AI comparisons and from each firm's own pages. Confirm cloud credentials and staffing directly.
| Provider | HQ (as published) | MLOps focus | Best fit |
|---|---|---|---|
| LuMay | US-focused delivery | Deployment fabric, MLOps and observability for AI agents | Agent and LLM workloads going live |
| Quantiphi | Boston, US | End-to-end AI programs; NeuralOps on SageMaker | Large enterprise AI programs |
| phData | Minneapolis, US | MLOps on Snowflake; managed DataOps/MLOps | Snowflake-centered platforms |
| Provectus | San Francisco, US | SageMaker MLOps platforms | AWS-native teams |
| Tredence | SF Bay Area, US | Advisory to managed model operations | Model operations at scale |
| Thoughtworks | Global | CD4ML and ML engineering practice | Ways-of-working change |
| Tiger Analytics | US / global | Large ML platform programs | Large enterprise ML programs |
| DataArt | New York, US | ML inside custom product engineering | ML-powered products |
| Winder.AI | UK / global | Engineering-led MLOps and LLMOps | Regulated industries |
| InfoObjects | San Jose, US | Data-engineering-led MLOps and GenAI | Databricks and pipeline-heavy ML |
| SquareOps | Gurugram, India | Kubernetes GPU platforms and serving | Kubernetes-native ML infrastructure |
| Datatonic | UK / EMEA | Vertex AI platforms | Google Cloud organizations |
The 12 Best MLOps Consulting Companies, Ranked
1. LuMay
LuMay's AI Engineering and Infrastructure service covers data pipelines, deployment fabric, MLOps and observability, engineered for enterprise scale. Deliverables include a model registry, canary deployments, A/B-tested rollouts and rollback workflows.
Its published plan ends with agents going live, MLOps and observability instrumented, and runbooks handed to your team. Deployment options include SaaS, private cloud or fully on-prem.
Buying is low-risk to test. LuMay's pricing page lists a Production Pilot over 4–8 weeks, states that governance and security are included, and offers SaaS, Azure, private cloud, hybrid or on-prem deployment.
- Best for: enterprises putting AI agents and LLM workloads into production
- Teams that want runbooks and handover, not a permanent retainer
- Strengths: MLOps tied to agent latency and cost monitoring
- Limits: fewer published classical-ML platform engagements than Quantiphi, phData or Provectus
2. Quantiphi
SquareOps describes Quantiphi as a Boston-headquartered, AI-first engineering company founded in 2013, with 4,000+ professionals. It packaged its delivery experience into NeuralOps, an MLOps platform built on Amazon SageMaker.
- Best for: enterprise programs across Google Cloud, AWS and NVIDIA
- Limits: built for enterprise scale, less suited to one or two models
3. phData
phData, in Minneapolis, is the Snowflake-centered choice. It runs Elastic Operations, a managed DataOps and MLOps service, and is notable as one of few firms publishing its prices, per Winder.AI.
- Best for: ML lifecycles that live where Snowflake data lives
- Limits: less focus on Kubernetes and GPU cluster engineering
4. Provectus
Provectus, in San Francisco, builds AWS-native MLOps on SageMaker. SquareOps highlights builds documented on AWS's own engineering blogs as third-party-hosted evidence.
- Best for: teams fully committed to AWS
- Limits: depth is tightly coupled to AWS and SageMaker
5. Tredence
Tredence covers MLOps advisory, implementation and managed model operations with 24/7 production support. Its ML Works accelerator adds drift detection, data quality checks and workflow monitoring on client platforms.
- Best for: enterprises running many models that need ongoing operations
6. Thoughtworks
Thoughtworks created CD4ML, continuous delivery for machine learning, which treats MLOps as a software engineering discipline. SquareOps notes it changes how teams work first, then installs matching tooling.
- Best for: organizations whose ML problem is really a ways-of-working problem
- Limits: engagements are transformation-shaped
7. Tiger Analytics
Winder.AI lists Tiger Analytics for large enterprise ML platform programs and notes its inclusion in ISG's specialist evaluation. It also observes that its published figures carry no named clients.
- Best for: large enterprises wanting an analytics partner at scale
8. DataArt
DataArt, in New York and founded in 1997, fits when a model is one component of a larger product build. It brings application engineering and domain knowledge that pure MLOps boutiques may lack.
- Best for: ML-powered products in finance, healthcare, media or retail
9. Winder.AI
Winder.AI is an engineering-led MLOps and LLMOps firm based in the UK, serving clients globally. It publishes its prices and names client engagements, and discloses that its own list ranks it first.
- Best for: regulated industries wanting senior engineers on the work
- Limits: smaller brand in formal procurement; not US-headquartered
10. InfoObjects
InfoObjects, in San Jose, approaches MLOps from data engineering, with Spark and Databricks pipelines and newer generative AI work. SquareOps notes less third-party-verified MLOps proof than higher-ranked firms.
- Best for: Databricks-heavy data and ML pipelines
11. SquareOps
SquareOps builds Kubernetes-native ML infrastructure with GPU cost attribution, MLflow tracking and serving through KServe, Triton or Ray Serve. It is headquartered in Gurugram, India, with US clients.
- Best for: platform teams on EKS, GKE or AKS
- Limits: owns the platform, not model development
12. Datatonic
Winder.AI calls Datatonic the strongest pure Google Cloud option, building Vertex AI platforms. It partners only with Google among hyperscalers and is based in the UK.
- Best for: US organizations fully on Google Cloud
How to Choose an MLOps Consulting Company: Checklist
- Evidence
- Which production deployments can you name, and what did they measure?
- Cloud reality
- Which clouds have your engineers actually run, not just partnered with?
- People
- Will the pitch team do the work?
- Exit
- Does the engagement end with handover and runbooks?
- How are LLM workloads run alongside classical ML?
These questions follow Winder.AI's published method. For LLM-specific operations, see our LLMOps practices guide and our LLM observability tools comparison.
If you are still choosing a build partner, our list of AI and machine learning development companies covers model development, and LuMay's managed AI services cover ongoing operations.
Next Step
Getting AI agents or models ready for production? Book a LuMay demo and bring your current deployment and monitoring setup.
