1.Why AI Powered Applications Need a Different Kind of DevOps

Traditional DevOps was built around one idea, ship code faster and more safely. AI powered applications add a second moving part that most DevOps teams were never trained for, the model itself.

A model is not static code. It gets retrained, fine tuned, and sometimes replaced entirely based on new data. That means an AI DevOps company needs pipelines for model versioning, automated testing of model outputs, and rollback plans for when a new model version performs worse than the old one.

Then there is the cost side. GPU instances on AWS and Azure are expensive, and inference costs scale with usage in ways that standard web hosting never did. A good DevOps partner builds in autoscaling rules, spot instance strategies, and usage monitoring from day one, not after the first surprise invoice arrives.

Security is another layer worth thinking about. Training data, embeddings, and model weights are valuable assets, and they need access controls, encryption, and audit trails just like customer data does.

This is the real reason companies now search specifically for AI DevOps companies and DevOps implementation services built around machine learning workloads, rather than hiring a generalist DevOps engineer and hoping things work out.

2.What To Check Before You Sign Anything

Before you commit to any AI development company, a short checklist saves a lot of pain later.

• Cloud certifications that matter. Look for proven AWS development services and Azure development services experience, not just a mention on a homepage.
• MLOps tooling experience. Ask which tools they have actually deployed, examples include MLflow, Kubeflow, SageMaker pipelines, or Azure ML pipelines.
• Cost transparency. A serious partner can explain how they plan to control your cloud spend, not just increase your monthly retainer over time.
• Team structure. Find out if the same team that builds your AI features also handles DevOps, or if it gets handed off to a different department with different priorities.
• Support after launch. AI models need ongoing monitoring for accuracy drift. Ask what happens in month three, not just week one.

Keep this list handy while you go through the companies below. The right fit is rarely the cheapest option or the biggest name, it is the team whose answers to these questions actually match how your product works.

3.35 Best DevOps Companies for AI Software Development in 2026

With that checklist in mind, here are 35 companies worth putting on your shortlist if you want to hire DevOps developers, hire AI developers, or find a complete AI development company for your next AI powered application. The list mixes large global players with smaller, specialized teams on purpose, since the right fit depends heavily on your budget, industry, and how much hand holding you need after launch.

1. Accenture

Accenture remains one of the largest AI development company options for enterprises that need everything under one roof. Their AI DevOps companies division pairs generative AI build teams with dedicated cloud DevOps squads, making them a strong pick for organizations already running large AWS or Azure environments and looking for global scale support.

2. Backend Development Company

If your AI powered application needs rock solid backend infrastructure before anything else, this is a name worth knowing. Backend Development Company focuses on building the APIs, databases, and server architecture that AI models actually run on, then layers DevOps implementation services on top so deployments stay smooth as the product scales.

3. EPAM Systems

EPAM has built a strong reputation in regulated industries like finance and healthcare, where AI deployments need strict compliance alongside speed. Their teams combine AI engineering with cloud native DevOps services, which makes them a safe choice if your AI project touches sensitive customer data or operates under heavy audit requirements.

4. Infosys

Infosys brings massive scale and its own AI platform, called Topaz, into the picture. For companies that want an established AI development company with deep DevOps services experience across both AWS development services and Azure development services, Infosys offers the resources of a global player along with enterprise grade governance processes.

5. ThoughtWorks

ThoughtWorks helped popularize continuous delivery years ago, and now applies that same discipline to machine learning pipelines. If you want a DevOps implementation services partner that treats your AI model like any other piece of software that needs versioning, testing, and safe rollback plans, their consulting led approach fits very well.

6. Globant

Globant runs dedicated AI studios alongside DevOps pods, which means your AI features and your infrastructure get built by teams that talk to each other every single day. This setup works particularly well for companies in the United States and Latin America looking for AI software development companies with a creative, design forward edge.

7. Softserve

Softserve carries strong AWS and Azure partner status along with a long track record in healthcare and fintech AI projects. Their DevOps services cover everything from CI CD pipeline setup to ongoing cloud cost management, making them a solid mid sized option for AI products that need to grow steadily without surprises.

8. Intetics

Intetics leans heavily into data engineering, which matters a lot for AI powered applications since most model problems actually start with messy data pipelines. Their DevOps services focus on keeping data flowing cleanly from source to model to production, backed by experienced delivery teams across Eastern Europe.

9. HireFullStackDeveloperIndia

As the name suggests, this company makes it simple to hire DevOps developers and full stack engineers together as one package. HireFullStackDeveloperIndia offers flexible monthly hiring models, which suits startups that want dedicated talent without the overhead of a large agency contract or long term lock in.

10. Itransition

Itransition handles both new AI builds and the modernization of older applications to support AI features, with DevOps consulting woven through both types of projects. Their AI development company offering covers everything from initial architecture decisions to long term infrastructure management once the product is live.

11. Belitsoft

Belitsoft works on a long term staff augmentation model, which means you can hire AI developers and DevOps engineers as extensions of your own team rather than as a separate outside vendor. This setup works well for companies that want continuity across many years of product development.

12.Andersen

Andersen has a dedicated AI and machine learning competency center, and pairs it with full DevOps implementation services so AI features move from prototype to production without getting stuck in long handoff delays between separate development and operations teams.

13. Intellectsoft

Intellectsoft combines AI consulting with broader emerging technology work, including blockchain and IoT, all backed by solid DevOps support. For founders exploring AI development company options that also understand adjacent technologies, this breadth can save you from hiring multiple specialist vendors later on.

14. Appinventiv

Appinventiv has built a strong portfolio of generative AI applications, each shipped with CI CD pipelines built in from the start rather than added afterward. Their AI DevOps companies approach leans toward mobile first AI products, which is useful if your application lives primarily on phones.

15. Simform

Simform holds AWS Advanced Partner status and runs dedicated DevOps pods alongside its AI product engineering teams. Their AWS development services experience runs deep, covering everything from containerized deployments to serverless AI inference setups that keep costs predictable as usage grows.

16. HireAIDevelopers

HireAIDevelopers is built around exactly what its name promises, a straightforward way to hire AI developers on flexible terms, with DevOps add on packages available for teams that need infrastructure support without committing to a full project contract right away.

17.Maruti Techlabs

Maruti Techlabs built its name on chatbots, predictive analytics, and robotic process automation, all backed by automated DevOps pipelines. They are a good fit for companies whose AI powered application centers on customer facing automation tools rather than heavy backend infrastructure.

18. Brainvire

Brainvire sits among the well known AI software development companies focused on retail and ecommerce, where AI personalization features need to run reliably during high traffic events like sales seasons. Their DevOps maintenance plans are built with exactly that kind of traffic spike in mind.

19. Hidden Brains

Hidden Brains offers budget friendly, fixed cost packages for AI MVPs, which appeals to early stage founders. Their DevOps services are scoped to match the budget level, so you are not paying enterprise level prices for what is still a small pilot project.

20. Indium Software

Indium Software runs a separate quality assurance and DevOps wing specifically focused on validating AI model outputs before and after deployment, which is a detail many AI development company options skip entirely until something goes wrong in production.

21. Daffodil Software

Daffodil Software follows ISO certified processes and works frequently with healthtech and fintech clients, where compliance heavy AI deployments need careful DevOps services planned from the very first sprint rather than bolted on near launch.

22. Konstant Infosolutions

Konstant Infosolutions specializes in mobile first AI apps with continuous deployment setups already configured, which means updates to your AI features can go live without long manual release cycles slowing down your product roadmap.

23. HourlyDeveloper

HourlyDeveloper partners with startups and growing businesses to transform AI ideas into scalable products. The company specializes in building AI-powered MVPs and creating long-term DevOps roadmaps from the beginning of the development cycle. By focusing on scalable architecture, automation, and cloud-native practices early on, HourlyDeveloper helps businesses avoid costly infrastructure rebuilds as their products grow. Their practical approach to AI development makes them a strong choice for startups seeking a reliable technology partner from MVP launch to enterprise-scale deployment.

24. OpenXcell

OpenXcell has built a strong reputation for AI app development paired with cloud DevOps setup, particularly for healthcare and on demand service apps. Their teams manage deployments across both AWS and Azure depending on what fits a client’s existing setup best.

25. Net Solutions

Net Solutions focuses on long term product partnerships rather than one off projects, which means their DevOps implementation services are designed with years of iteration in mind, not just a single launch event that gets celebrated and then forgotten.

26. Mindbowser

Mindbowser specializes in healthtech AI, with HIPAA compliant DevOps pipelines built specifically for AI applications that handle patient data. This is a narrow but genuinely important specialty for founders building anything in the healthcare space.

27. Matellio

Matellio starts most engagements with a discovery workshop before committing to a DevOps roadmap, which helps founders who are still figuring out exactly what their AI powered application needs before signing a larger, longer contract.

28. Quytech

Quytech has carved out a niche in AI combined with augmented and virtual reality app development, backed by cloud infrastructure support for the often heavy data and processing demands that immersive applications bring.

29. Hyperlink InfoSystem

Hyperlink InfoSystem has built a large portfolio across many industries, with flexible engagement models that range from fixed price AI app development to ongoing DevOps services contracts depending on what stage your product is at.

30. TechAhead

TechAhead works with global brands on AI powered mobile experiences, and their DevOps services are built around the release cycles consumer facing apps need, including frequent updates, feature flags, and A or B testing infrastructure.

31. WebClues Infotech

WebClues Infotech is known for combining AI features, especially in ecommerce, with ongoing DevOps support contracts. They are a solid example of AI software development companies that think about maintenance from day one, not just initial delivery.

32. Cyber Infrastructure CIS

Cyber Infrastructure CIS maintains a large in house DevOps team that continues supporting AI projects well after launch, which is useful if you want one AI development company that does not disappear once the initial build is done.

33. Sigma Software

Sigma Software has a background working with aerospace and fintech clients, industries where security clearance and strict process requirements are standard. Their Artificial Intelligence DevOps practice carries that same level of rigor into commercial AI projects as well.

34. Algoworks

Algoworks focuses on AI integrations inside Salesforce and other enterprise software ecosystems, with DevOps services tailored to the specific deployment patterns these platforms require, which is a fairly specialized but valuable niche.

35. Technovert

Technovert centers its work around data platforms, with deep Azure development services expertise that supports AI products built on heavy data pipelines, from initial ingestion all the way through to model serving in production.

4.Final Thoughts

Going through 35 companies might feel like a lot, but the goal here was never to find one single perfect answer. It was to give you enough real detail, locations, team sizes, rates, and specialties, so you can actually compare options instead of guessing based on a polished sales call.

A few patterns are worth noticing. The companies that talk about model monitoring, retraining pipelines, and cloud cost control without being asked are usually the ones who have actually run AI applications in production, not just built demos. The companies that only talk about the AI model itself and treat infrastructure as an afterthought are the ones most likely to leave you with a surprise bill or a fragile deployment six months from now.

If you take one thing away from this list, let it be this. The best DevOps companies for AI software development are not necessarily the biggest names. Sometimes a smaller team that has shipped five AI products end to end with solid DevOps practices will serve you better than a massive firm where your project gets a junior team and a senior sales pitch.

Whichever company you shortlist, ask to see how they have handled a real production issue with an AI model before. The answer to that one question usually tells you more than an hour of polished slides ever will.