AI companies to compare
Compare 2 companies tagged for AI. Listings are ordered by public client ratings weighted by review volume, not by an independent assessment of model quality. Filter by country, hourly rate and team size, then check what each company has running in production, who did the work, and how your data would be handled.
- Companies listed
- 2
- Countries
- 1
- Ordered by
- Rating weighted by reviews
- Last updated
- 4 Oct 2026
-
#1
AI profile for Anthropic. Compare listed services, team details, rates and relevant project evidence before shortlisting.
Free-$20+/mo 800+ staff San Francisco USA Founded 2021 -
#2
AI profile for OpenAI. Compare listed services, team details, rates and relevant project evidence before shortlisting.
Free-$200/mo 1700+ staff San Francisco USA Founded 2015
How the order works, and what to check
By client rating, weighted for how many reviews stand behind it. A company rated 4.9 across 149 reviews ranks above one rated 5.0 across 8, because the larger sample tells you more. Nothing else moves a company up or down. The criteria below are what to check on each profile before you shortlist.
Review evidence
Compare ratings and review counts together. A star score is one signal, not a technical assessment.
Relevant work
Open case studies and confirm the firm’s role, project scope and result.
Project cost
Compare published hourly bands and minimums; ask what is excluded.
Delivery team
Confirm who will work on your project, their availability and who remains accountable after launch.
Scope fit
Ask how the proposed service maps to your requirements and which dependencies or risks are outside scope.
Score = (reviews × rating + 25 × 4.5) ÷ (reviews + 25)
A company is treated as close to 4.5 stars until it has more than 25 reviews, after which its own rating takes over. Placement cannot be purchased.
Be specific about the problem before the technology
Write down the decision or task the system is meant to support, who relies on it, and how you would know it is working. Providers can then tell you whether the problem needs a model at all. A good partner will sometimes say that conventional software, or better data, solves it more cheaply.
Ask what is in production, not what demos well
A convincing demonstration is not the same as a system that holds up with real users and messy data. Ask what is live, who uses it, how long it has run, and what happens when the model is wrong. The answer to that last question separates teams who have shipped from teams who have prototyped.
Check team depth and data handling
Find out whether machine learning and data engineering sit in house or are subcontracted, because that affects who you can reach when something breaks. Confirm where your data is processed, whether it is used for training, what is retained, and how the company approaches evaluation, privacy and bias.
Budget for what comes after delivery
Data preparation usually costs more than model choice, and inference, monitoring and retraining continue once the project ends. Ask for those running costs in writing alongside the build price, and agree who is responsible for evaluating quality over time.
Before you shortlist
01How is this list different from the AI development directory?
This list is broader and includes consultancies and product companies. If you want a partner to build a specific system, the AI development directory is the closer fit.
02How can I judge an AI company quickly?
Ask for one system running in production, who uses it, and how they measure whether it is working. A vague answer to the third question is the clearest warning sign.
03What usually gets missed in an AI budget?
Everything after launch. Data preparation, evaluation, inference and monitoring continue long after the build, and they are often larger than the initial cost.
04Does MatchFast evaluate these companies’ models?
No. Directory ordering uses public review data. Verify claims, published work and team experience independently before shortlisting.
Is your company missing from this list?
Submit a company profile for consideration. This directory’s order follows its stated review-data method; a paid promotion does not buy a higher position.