Machine learning consulting: the 10 best firms in 2026
We compared machine learning consulting firms on published prices, client reviews, named results and what happens after the model ships.
The short answer, by profile. You need a budget figure and a named reference first: ScienceSoft, which publishes $30,000 to $600,000+ by scope. You have one ML problem and a five-figure budget: InData Labs (from $15k, often $40-90k). You want one vendor for months, with a maintenance plan, at a low hourly band: Itransition ($25-49/h on Clutch). You run a US enterprise and want models run like software: Forte Group. You are a regulated giant already on IBM: IBM Consulting; a global rollout across countries: Accenture.
Machine learning consulting means a firm that helps you pick the problems a model can solve, prepares your data, builds and trains the model, puts it into production and keeps it accurate. If what you want is a chatbot or an assistant on top of GPT or Claude, our generative AI ranking fits better; if you are still deciding where AI pays off at all, start with our AI consulting ranking. We compared 20 firms and ranked 10 on the same five criteria. The order follows our scores; a sponsored spot is always labeled "Sponsored".
The ranking is editorial: a sponsored spot is always labeled on its card, and no affiliate link changes a score.
The right machine learning consulting firm for your situation
A published range, from $15k and often $40-90k, and a team whose core business is data science and AI.
Best for startupsA $10,000 minimum on Clutch, and the data pipeline and the model from one small team.
Best for enterprisesMLOps with drift detection and automated retraining, and named clients such as Walgreens and Nasdaq.
Best for B2B mid-marketA first model in production on a Microsoft Fabric or Databricks platform, with bias and drift monitoring.
Best for a tight budget$25 to $49 an hour on Clutch, the lowest band in this ranking, with a maintenance plan after launch.
Best for international programsA 200-person data team inside a global engineering company, with a US office in Florida.
Machine learning consulting firms: budget, rate, score
Prices come from each firm's site, or from its Clutch profile when the site publishes none, in US dollars. Each card scores five criteria out of 5, from facts we read: expertise and focus (main offer, named projects, partner tier read in a vendor's directory), track record (named clients with measured results in machine learning), client reviews (count on independent platforms, or a Leader rating from an analyst firm for the large consultancies), transparency (prices, method, commitment) and team (size, published support after launch, a team in the US). The overall score is their average.
Also compared and left out: McKinsey QuantumBlack and BCG X, strategy firms that sell AI inside CEO-led transformation programs rather than as a machine learning project you can scope and price on its own; DataRobot, which sells an AI platform rather than consulting; LeewayHertz, now part of The Hackett Group, which presents itself as a generative AI development firm and puts itself first in its own ranking; Fresh Consulting, whose Clutch profile shows no client review and which has no analyst rating to replace it; AltexSoft, whose home page leads with travel and hospitality technology rather than ML across industries; and Serokell, whose home page now sells Haskell and Rust engineering for fintech and blockchain rather than ML. Saviant Consulting, also on the first page of Google, serves only industrial companies (machinery makers, energy, utilities) and publishes no price; Beacon Hill (BHSG) sells ML mostly as staff augmentation and managed services from an IT staffing group; and RTS Labs, ranked by two of the top guides, shows no client review yet on its Clutch profile and lists 10 to 49 people.
| Best for | Hourly rate | Ideal client | Visit | ||||
|---|---|---|---|---|---|---|---|
| 1 | ScienceSoft | Best for a budget figure by type of ML system | $50-99/h | 4.0/5 | $30,000 to $600,000+ by scope (its site) | US mid-market · budget-led buyers | Visit → |
| 2 | InData Labs | Best for a scoped ML project with a published budget range | $50-99/h | 3.8/5 | From $15k, often $40-90k (its site) | SMBs and mid-market · one use case | Visit → |
| 3 | Itransition | Best for a long ML program with a maintenance plan | $25-49/h | 3.7/5 | $25,000 minimum | Mid-market to enterprise · long programs | Visit → |
| 4 | Forte Group | Best for ML treated as production engineering in a US enterprise | $50-99/h | 3.7/5 | $50,000 minimum | US enterprises | Visit → |
| 5 | N-iX | Best for a large engineering bench with senior ML staff | $50-99/h | 3.6/5 | $100,000 minimum | Large enterprises | Visit → |
| 6 | Kanerika | Best for mid-market teams on Microsoft and Databricks data stacks | $100-149/h | 3.5/5 | Quote only | Mid-market · Microsoft and Databricks stacks | Visit → |
| 7 | Accenture | Best for multi-year enterprise AI rollouts across many countries | Quote | 3.5/5 | Quote only | Global enterprises | Visit → |
| 8 | IBM Consulting | Best for large regulated enterprises already on IBM | Quote | 3.4/5 | Quote only | Regulated enterprises | Visit → |
| 9 | DataForest | Best for a small budget that also needs data engineering | $50-99/h | 3.3/5 | $10,000 minimum | Startups and SMBs | Visit → |
| 10 | Deloitte | Best for an ML program tied to an audit, risk or compliance agenda | Quote | 3.1/5 | Quote only | Large regulated companies | Visit → |
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ScienceSoft
ScienceSoft says it has worked in data science and AI since 1989 and is headquartered in McKinney, in the Dallas area. Its ML page publishes cost ranges by type of solution, lists use cases from predictive maintenance to quality monitoring, and includes support and maintenance of models: continuous monitoring and tuning for accuracy. Its case studies name clients such as bioAffinity Technologies, Cerulean and Dwayne's Photo. It has been an AWS partner since 2017.
- Published ranges: $30,000-$200,000 for one ML-powered component, $200,000-$600,000 for an end-to-end ML solution
- A lung cancer detection app for bioAffinity Technologies: 92% sensitivity and 87% specificity
- Model monitoring and tuning sold as a service
- ✓Price ranges you can take to a budget meeting
- ✓A $5,000 minimum on Clutch for a small first step
- ✓A US headquarters
- ✗Few of its named ML cases publish a business result: ask for the ROI behind them
- ✗ML is one of many services: ask who leads data science on your project
The safest pick when budget comes first. The grid tells you whether your idea is a $50,000 or a $500,000 project before any sales call, and a named client backs its measured result.
InData Labs
InData Labs is an AI and data science company that has built production AI systems for 12+ years, with its headquarters in Vilnius on Clutch and a Miami address on its site. Its ML consulting work starts with exploratory data analysis (EDA: a first look at your data to see what it can predict), then moves to model development, training and production deployment. After launch it sets up MLOps (the tooling that versions, deploys and monitors models), monitoring and ongoing support, and its home page says you own the code, the model and the architecture.
- A published price range for ML projects: from $15k, often $40-90k
- Freight rate prediction software for AsstrA, the logistics group
- Code, model and architecture owned by the client, written on its home page
- ✓A specialist: ML and data science are what it sells
- ✓A budget range before you talk to sales
- ✓Ownership of the model stated up front
- ✗Its measured results do not name the client
- ✗Its only listed delivery location on Clutch is Vilnius: Lithuania runs on Eastern European time, seven hours ahead of New York
A strong first call for a mid-sized company with one ML use case and a five-figure budget. Ask which of its anonymous results is closest to your problem, and ask to speak to that client.
Itransition
Itransition starts with discovery workshops, interviews and process observation to select viable use cases, then advises on the stack, architecture and model training. After launch it sets up a maintenance plan, monitors model performance, advises on retraining and trains your users. You can buy a full project, a dedicated team or one or more ML specialists who join your team.
- An ML-powered retail BI platform: 8% better buyer conversion (client unnamed)
- A computer vision proof of concept for plankton detection: 98% detection accuracy
- A written post-launch plan: monitoring, retraining advice, user training
- ✓$25 to $49 an hour on Clutch, the lowest band in this ranking
- ✓Three ways to hire: project, dedicated team, staff augmentation
- ✓A clear lifecycle up to maintenance
- ✗No price on its site
- ✗Measured ML cases do not name the client
A strong choice when you need capacity over months, not a one-off model. Ask for the names behind the 8% and 98% results.
Forte Group
Forte Group has been in business since 2000 and lists its headquarters in Chicago on Clutch, with delivery teams in Latin America and Eastern Europe. Its ML engineering and MLOps line covers model development, CI/CD for ML, containerized deployment, feature stores, drift detection (spotting when a model's accuracy slips as real data changes) and automated retraining. It also publishes one of the rankings in the top 10 for this search, and puts itself first in it.
- 96% of emails automated with AI replies for a national law firm (unnamed)
- 70% cost reduction with AI-powered custom design automation
- Named clients across regulated and consumer industries
- ✓MLOps, drift detection and retraining in its service line
- ✓Named enterprise clients
- ✓US headquarters, nearshore delivery
- ✗A $50,000 minimum rules out small pilots
- ✗It ranks itself first in its own ML list: judge it on references, not that list
A good fit once you have a model worth running in production. For a first experiment, its $50,000 minimum is high.
N-iX
N-iX describes itself as a global software engineering company with 24 years of experience in manufacturing, logistics, fintech and telecom. Its data team counts 200 specialists in AI strategy, ML model implementation, ML pipelines and model governance. It publishes what seniority means: a middle specialist has at least four years of experience, a senior more than five. It has a US office in Plantation, Florida.
- A 200-person data team for AI strategy, ML pipelines and model governance
- Seniority defined in years on its ML page
- Long-term clients named: Gogo, AVL, Lebara, cleverbridge, Ringier
- ✓Depth for large programs
- ✓Published seniority levels
- ✓A US office
- ✗A $100,000 minimum on Clutch
- ✗Its ML page names no measured ML result
Pick N-iX when the project is big enough to need many hands. Ask which of the 200 would lead yours, and what support it offers after launch.
Kanerika
Kanerika is headquartered in Austin and sells ML consulting alongside data platform migrations (to Microsoft Fabric, Databricks or Power BI) and AI agents. Its IMPACT method runs from planning and development to deployment and optimization, and covers data preparation, model training and production deployment. After launch it monitors model performance, bias and drift. It also publishes one of the rankings in the top 10 for this search, and puts itself first in it.
- 85% accurate sales forecasts for a manufacturer (unnamed)
- 33% fewer fraud losses with AI modeling and forecasting (unnamed)
- Monitoring of bias and drift after launch
- ✓A clear method from planning to optimization
- ✓Strong on the data platform under the model
- ✓5.0 on Clutch
- ✗No price on its site: Kanerika quotes per use case
- ✗The highest hourly band here on Clutch, $100 to $149
- ✗It ranks itself first in its own ML list
A good fit when your data still needs to move to a modern platform before any model can work. Compare its hourly band with the $25 to $99 bands above.
Accenture
Accenture sells data and AI work through its Data and AI services and its own AI Refinery platform, and runs operations for clients after launch through its managed services. It serves 9,000+ clients in more than 120 countries, with offices in 52 countries. Forrester names it a Leader in its evaluation of AI consulting services. Its Data and AI page names large clients such as NATO, without a measured ML result next to the name.
- A Leader in a Forrester Wave on AI consulting services
- Managed services to run what it builds
- 9,000+ clients in more than 120 countries
- ✓Reach across countries and industries
- ✓Can run the system for years after launch
- ✓An independent analyst rating
- ✗No price, method or minimum published
- ✗No named and measured ML case on its Data and AI page
For a global, multi-year program, Accenture is a natural shortlist name. For one model, the firms above give you prices and a smaller team.
IBM Consulting
IBM Consulting builds and operates enterprise AI platforms, with a prebuilt agent catalog and its own delivery platform, IBM Consulting Advantage. It says it has trained over 75,000 consultants in generative AI. Forrester names it a Leader in its AI consulting services evaluation and calls it a good fit for enterprises in regulated industries. Its page names the State of Arizona, where caseworkers gained 40% productivity from generative AI tools.
- A Leader in a Forrester Wave on AI consulting services
- Build and operate: it can run the platform after launch
- State of Arizona: 40% productivity gains for caseworkers
- ✓Scale and governance for regulated industries
- ✓Operation of the platform after launch
- ✓An independent analyst rating
- ✗No price, no method, no minimum published
- ✗Its measured case is generative AI, not classic ML
Choose IBM when governance, scale and an existing IBM estate matter more than price. Ask for an ML reference in your industry, not a generative AI one.
DataForest
DataForest, with offices in Kyiv and Tallinn on Clutch, sells data engineering, MLOps (deploying, monitoring and maintaining models in production) and AI, and stays on to support what it builds. Its home page shows measured results without client names, such as a US SaaS platform that analyzes contracts 70% faster with LLMs, and a US medical lab that cut compute costs by about 50% with a Databricks migration. It says 92% of its clients come back.
- A $10,000 minimum on Clutch
- 5.0 out of 5 from 29 Clutch reviews
- Data pipelines and models from one team
- ✓Low entry point
- ✓Data engineering and ML together
- ✓Support after delivery
- ✗Its measured results do not name the client
- ✗ML is not its only focus
- ✗No US office
A sensible pick for a first model on a tight budget when your data is not ready yet. Ask for a reference whose project was mainly ML, not data engineering.
Deloitte
Deloitte's AI and Data practice works across strategy, data, human capital, regulatory, legal and reputation topics, and uses its own delivery platforms (Deloitte Ascend, Intela, Omnia). IDC named it a Leader for the fourth time in its worldwide AI services assessment, citing its ability to deliver across the life cycle of AI services and to help clients set up their own AI centers of excellence. It did not take part in the full Forrester evaluation of AI consulting services; Forrester still placed it among the Contenders in that evaluation.
- A Leader in the IDC MarketScape for worldwide AI services, for the fourth time
- Help to set up your own AI center of excellence
- Regulatory, legal and risk teams in the same firm
- ✓One firm for the model and the compliance around it
- ✓Global reach
- ✓An independent analyst rating
- ✗No price, method or named ML case published
- ✗Model monitoring and retraining after launch are not described: ask for them in the contract
Pick Deloitte when regulators will review the model. For a single model on a budget, the firms above publish far more.
How to choose your machine learning consulting firm, step by step
The method we used to rank these firms, turned into steps you can follow for a $15k pilot or a seven-figure program.
Write one problem with one number to beat
Before the call"We want to use machine learning" gets you a slide deck. "Cut stockouts by a third" or "flag 80% of fraudulent orders before shipping" gets you a scope, a data list and a price. Add the number you get today without a model: it is the baseline every firm must beat. If you are not sure ML is the right tool at all, an AI consulting firm is a better first stop.
- Brief
- 1 page
- Baseline
- 1 number
Check your data before you check the vendors
Before the callA model is only as good as the history it learns from. List where the data lives, how many months you have and who can export it. If it sits in ten tools, the first project is a data platform, not a model: compare the options in our data warehouse ranking and our ETL tools ranking. Firms such as DataForest and Kanerika sell both steps.
- Data sources
- listed
Ask for two references you can call
ShortlistMost ML firms show results without the client's name: "85% accurate forecasts for a manufacturer". That is a claim, not a reference. Ask for two clients in your industry, with the problem, the baseline, the result and a person you can call. A firm that cannot name one has probably not done your kind of project.
- References
- 2, named
Settle who owns the model, the data and the accounts
Before signingWrite down that you own the code, the trained model weights, the feature pipelines and any labeled data, and that the cloud accounts are in your name. InData Labs states client ownership of code, model and architecture on its home page; ask every other firm to put the same sentence in the contract.
- Owned by you
- code, weights, data
Buy a priced proof of concept with a go or no-go point
First purchasePay for a short proof of concept with written success criteria (the number from step one), then decide. It caps your risk and shows how the team works with your data. If you want your own staff to understand the basics first, our machine learning training ranking lists courses that fit managers and engineers.
- Pilot
- weeks, not months
Price what happens after launch
Before signingModels drift: the data changes and accuracy slips without anyone noticing. Ask who monitors the model, how often it is retrained, what that costs per month and on which infrastructure it runs (see our AI infrastructure ranking). Itransition, Forte Group, Kanerika and InData Labs describe this work; get it in the contract, with a monthly price.
- Monitoring
- in the contract
What machine learning consulting costs, with real numbers
Few firms publish prices. These figures come from the ranked firms' sites or their Clutch profiles, in US dollars.
Price by size of project
InData Labs says its ML projects start from $15k and can reach $40-90k and more. ScienceSoft publishes the widest grid: $30,000 to $200,000 for its first service option and $200,000 to $600,000 for an end-to-end ML solution, with retail price optimization at $150,000 to $300,000 for a solution of average complexity.
Hourly rates
On Clutch, the ranked firms list $25 to $49 an hour (Itransition), $50 to $99 (ScienceSoft, InData Labs, N-iX, Forte Group, DataForest) and $100 to $149 (Kanerika). IBM, Accenture and Deloitte quote per engagement.
Minimum projects
Minimums on Clutch go from $5,000 (ScienceSoft) and $10,000 (InData Labs, Kanerika, DataForest) to $25,000 (Itransition), $50,000 (Forte Group) and $100,000 (N-iX). A firm with a $100,000 minimum will not take a two-week experiment.
Ways to hire
You can buy a fixed-scope project, a dedicated team or staff augmentation, where engineers join your team under your lead; Itransition sells all three. A fixed scope suits a proof of concept; a dedicated team suits a model that keeps changing; augmentation suits a company that already has an ML lead.
The cost after launch
Budget for cloud compute and for monitoring and retraining every month. ScienceSoft puts the yearly cost of support at 15 to 25% of the initial build. Ask for that monthly figure in writing before you sign: it is the line most quotes leave out.
Where ML pays off first
The firms' own published results point to a few use cases: demand forecasting, fraud and document processing. InData Labs shows 60% fewer stockouts per quarter in retail, 54% lower advertising costs in martech and 4x faster document processing in logistics, all without client names, so treat them as claims to check. ScienceSoft's named case, a lung cancer detection app for bioAffinity Technologies, reached 92% sensitivity and 87% specificity. Start where you already have a clear baseline number and enough history.
When to skip a firm
If the job is a chatbot or a writing assistant, you do not need custom ML: a hosted model through an API may be enough (see our LLM API comparison). If it is search over your documents, start with a vector database. And if your team runs on Databricks, our Databricks training may close the gap without a vendor.
Machine learning consulting: your questions answered
What does a machine learning consultant do?
A machine learning consultant finds the business problems a model can solve, checks whether your data is good enough, builds and trains the model, and helps you put it into production. Most firms in this ranking also monitor the model after launch and retrain it when accuracy drops, the step buyers most often forget to budget for.
Which machine learning use cases are the most expensive to build?
Fraud detection and end-to-end optimization systems sit at the top. On ScienceSoft's grid, a classification component costs $20,000 to $70,000, a fraud detection component $70,000 to $200,000, predictive maintenance around $250,000 and inventory optimization $200,000 to $350,000 at average complexity, up to $600,000 for a complex one. The price follows the number of data sources and the systems the model must plug into.
How long does it take to see results from a machine learning project?
Count in months, not weeks, for a model in production. DataForest, for example, publishes that a simple development takes a few days to a month and a complex solution three to six months. Put a go or no-go date after the proof of concept, and measure against the baseline number you wrote in step one.
What questions should I ask a machine learning consulting firm?
Five questions the sales deck rarely answers: who wrote the code on your last comparable project, and are they on my team? Which part of the work do you subcontract? Whose name is on the cloud account? What does retraining cost per month? What happens, and who pays, if the model misses the target accuracy?
Is machine learning consulting worth it for a small company?
Yes, if you have one problem with a baseline number and enough history in your data. If not, a hosted model through an API often costs less than a custom one. To limit the risk, ask for a fixed-price proof of concept with a cap: DataForest lists a $10,000 minimum on Clutch and InData Labs starts from $15k.
What is the difference between machine learning consulting and machine learning development?
Consulting decides what to build and whether your data supports it; development builds, trains and deploys the model. Most firms here sell both. If you already know what to build, our AI development ranking compares firms on delivery.
Do I need MLOps consulting?
You need MLOps (the tooling that versions, deploys and monitors models) as soon as a model runs in production, because accuracy drifts as real data changes. The bill matters: ScienceSoft estimates that support can reach 70% of a solution's total cost over its life, so the MLOps line often outweighs the build.
Should I hire an ML consulting firm or build an in-house team?
Hire a firm for a first model or a skill you lack, and build in-house once ML becomes part of your product. A middle ground is staff augmentation, where the firm's engineers join your team: Itransition sells it. Either way, ask for knowledge transfer in writing.