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AI use in software development has grown significantly over the last few years. According to the Stack Overflow 2025 developer survey 78.5% engineers already use AI tools in the software development process, with almost one in two (47.1%) using them daily. We can confidently say that AI is arguably the broadest “top software development technology” for 2026 and beyond.
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It’s clear from the growing popularity of AI coding assistants and the use of LLMs in development projects. However, these are not the only AI-related trends worth noting in the development industry.
Top software development technologies of 2026
| Technology | Description | Tools |
| LLMs and AI coding assistants | Assist with code generation, code completion, debugging, documentation, DevOps, and refactoring. |
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| AI-augmented development tools | Improve specific aspects of Software development lifecycle (SDLC) like QA, project management, application security, and compliance. |
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| Low-code and no-code | Allow non-coders like managers and analysts develop applications and automate processes by using GUI-based tools to implement programming logic and build interfaces. |
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| Microservices and containers | Support a modular architecture of applications where individual services can be built, maintained, executed, and scaled independently, while existing within self-sufficient containers. |
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| Programming and Scripting Languages | Core technologies to build software, process data, automate tasks, and define the logic and structure of applications. |
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| Databases | Systems that store, manage, and query structured, semi-structured, or unstructured data to support applications, analytics, and business operations. |
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For software development, the two most common categories of AI tools that developers use are LLMs like ChatGPT, Gemini, Claude, and coding assistants like Co-pilot, Codex, and Opus. LLMs are trained on broad data sets, including code, which allows them to generate and debug code, assist with refactoring, and even set up DevOps pipelines.
Coding assistants are specialized LLMs or LLM-powered tools (wrappers) designed for programming assistance. They are trained and sometimes fine-tuned on coding data, more context aware, and often directly integrated with development environments (IDEs), making them better suited for code completion and project-level programming than plain LLMs.
Together, they help development teams through the entire development cycle – ideation, design, code generation, optimization, integration, testing, and deployment. Also, despite the “purer” alignment of coding assistants, developers, architects, QA professionals, and other professionals use both vanilla LLM and dedicated coding assistants. The exact mix may vary by role and project requirement.
The top LLMs and AI coding assistants of 2026 are:
ChatGPT and Codex: With over 700 million monthly active users, ChatGPT is the most widely used LLM for various purposes, including programming. Codex is the coding assistant within the OpenAI umbrella, quite powerful and reasonably priced.
GitHub Copilot: It can be considered a pure-breed coding assistant trained on massive code repositories and tightly integrated with IDEs and code repositories (GitHub natively). It’s not powered by a single underlying LLM and can switch between multiple available options.
Claude and Opus: Claude is marketed heavily for programming, and the coding assistant Opus ranks very high on relevant benchmarks. Though its pricing tiers are comparatively more expensive.
Gemini code assist: Gemini Pro and Gemini code assist benefit from native integration in the Google ecosystem and with Google’s agent-first IDE Antigravity. They also score high on coding benchmarks and offer massive context windows, but the strengths may seem less pronounced outside the Google ecosystem.
We may also see assistants powered by Chinese models like the DeepSeek coder and environment-specific AI-powered tools like the Amazon Q Developer gain more users and traction in 2026.
A wide range of specialized AI-augmented development tools is emerging that focus on specific stages or aspects of the software development lifecycle. They may also take a different, AI-first approach to conventional development tasks like code review, QA, integration, and analytics. Two types of trends are emerging for such tools – one is the new category of tools, and the other is specific tools gaining more traction within that category.
Agentic IDEs: Agentic IDEs like Cursor, Windsurf, and Antigravity go beyond programming assistants and help with project management. These tools can make changes in repositories, memorize context across the project, infer intent, and more. Some, like Antigravity, help you manage multiple bots that serve as members of your development team.
Autonomous QA: AI-based QA tools like Functionize and Applitools may start displacing conventional market leaders, at least in specific QA domains. Functionize focuses on automated self-healing test development, deployment, and maintenance. Applitools allows for functional, visual, and compliance testing of websites and applications and has been trained on over 4 billion app screens.
Application security (AppSec): Many traditional AppSec security tools have evolved to adopt and integrate AI heavily in their core build and leverage it to identify and fix security vulnerabilities. This includes Snyk by Deepcode AI, which focuses on security risk identification and prioritization. Veracode offers both security flaw identification and a remediation engine. Checkmarx One Assist is expanding (at the time of writing this) the security posture beyond development to include policy checks as well.
Replit: As an AI tool that aims to consolidate the bulk of the software development lifecycle in one umbrella and expand the “prompt-to-code” paradigm to “prompt-to-software,” Replit stands apart from coding assistants, LLMs, and even agentic IDEs. It combines software design, code generation, debugging, hosting, and deployment in one environment. It’s highly likely that we may see more tools like Replit entering the market in the future.
Low-code and no-code platforms have been democratizing software development long before AI. They aim to make software creation accessible to citizen developers, i.e., professionals and hobbyists without a background in IT. This is made possible through a Graphical User Interface (GUI) based approach to development, where coding is replaced with visual elements and logic is abstracted to graphical flow charts. Users can drag-and-drop these objects to develop full applications as per their needs. These tools significantly speed up development cycles and provide less technical stakeholders with greater agency over the features they use.
It’s worth noting that the distinction between no-code and low-code has become exceedingly blurred now that LLMs can generate and explain code for non-coders. While some platforms do market themselves as exclusively no-code or low-code, and others, like Kissflow, offer separate low-code and no-code platforms, the two categories have practically collapsed into one.
For low-code and no-code development platforms, the market leaders expected to remain on top are:
Mendix: The Mendix low-code platform allows for the building of enterprise-grade applications and allows for easy integration with other internal and external systems via both pre-made connectors and native integration to multiple ecosystems. They support governance-by-design and simplify cloud deployments.
OutSystems: It’s another AI-powered low-code platform leaning heavily on agentic AI. They have simple visuals/symbols, built-in security and compliance capabilities, pre-built connectors for over 400 systems, and a virtual data layer. These enterprise-grade capabilities are one reason why it’s favored by regulated industries like banking.
Appian: Appian focuses on business processes, their automation, and orchestration, making them a bit different from other platforms with a pure development focus. They also have a data fabric for easy unification, simplified AI integration (custom models mostly), and process intelligence features.
Microsoft Power Platform: This low-code platform is ideally suited for businesses within the Microsoft ecosystem and is natively integrated with Power BI, Virtual Agents, etc. Their unified data layer is called Dataverse, and it supports over a thousand different connectors. Another differentiator is how it balances the needs of professional and citizen programmers.
Other leaders and main contenders include the low-code development platforms offered by ServiceNow, Salesforce, Oracle, Zoho, and SAP.
Microservice development is the practice of breaking down an application or system into individual services that collectively make it function. These microservices are developed and deployed as individual, functioning units. This type of build makes the application or system more resilient, easier to modify and scale, though performance relies on seamless communication and orchestration of these microservices.
Microservices can be packaged into containers, which are self-contained runtime environments with only the specific resources needed to run each microservice. Containers are extremely efficient and resilient because each one can be taken offline without impacting any of the others.
Microservices and associated tools are evolving to meet the growing needs of the market, especially now that AI capabilities are readily becoming part of the core operational architecture of many systems. These trends include service meshes, a dedicated communication layer for microservices, and becoming a standard part of the architecture. The addition of AI and ML in microservice architectures is expected to improve overall observability, reliability, and optimization. The practice of building hybrid architectures that include both monolithic elements and microservices for an optimal balance of performance and security is also becoming more common. Event-Driven Architecture (EDA) is becoming the norm for real-time systems.
Microservice, containers, and supporting tools expected to remain at the top or climb the ranks include:
Dockers: It’s easily the most widely used containerization tool in the world, with a majority share of this market segment. It’s open source, cross-platform, simple, and easy to scale, and has become the industry standard for containerization. We don’t see it losing its top spot in 2026.
Kubernetes: As a container orchestration platform, Kubernetes sits a layer above tools like Docker and has become a de facto standard for orchestration, as Docker has for containerization. It’s open-source and cloud agnostic, which has contributed to its rise to the top in its domain.
Podman: Podman is an alternative to Docker. It’s another containerization tool with one critical difference – it’s daemonless. Unlike Docker, where a centralized layer sits above all containers, Podman containers are independent units that can run without root permissions or access for their tasks. This makes them inherently more secure, though a bit more difficult to orchestrate. They are becoming common in security-sensitive Linux environments.
Terraform: Infrastructure-as-code (IaC) tools like Terraform sit a level above container orchestration and help manage cloud environments. They allow easy configuration and management of the underlying cloud or on-prem resources to power an application, including distributed containers and Virtual Machines (VMs).
Other relevant tools and technologies like Ansible, Prometheus, and Amazon Redshift also have a strong momentum and see increasing adoption rates in 2026.
For the following three categories, we evaluate the momentum of top development technologies from the Stack Overflow survey of their usage and desirability for the last three years: 2023, 2024, and 2025.
Programming, scripting, and markup languages are the basic building blocks behind all the software that we interact with, and despite new languages being introduced almost every year, some have endured and remained on or near the top. JavaScript, HTML/CSS, SQL, Python, and TypeScript have remained in the top five spaces for the last three years, as per the StackOverflow surveys, with the only difference being that Python was pushed to fourth spot by SQL. While Python remains one of the top programming languages developers are still learning towards, the following four may experience more expedited growth.
Rust: As a security-focused and memory-safe alternative to C/C++, Rust is gaining significant traction for systems development where performance and security are top priorities.
Go: Performance, simplicity, and built-in concurrency (running multiple tasks at once) are three core strengths of Go. It’s already become a go-to language for cloud-native, microservice, and API development, and this trend may solidify further in 2026.
Kotlin: Official Google support for Android-native development pushed Kotlin up in popularity, and it has been gaining ground ever since. The Multiplatform stabilization back in 2023 also gave it a push as a viable cross-platform choice, and in the coming years, its use is expected to expand in AI development as well.
Zig: As the potential C successor that offers a simpler way to program performance-driven, low-level systems, Zig is rapidly being picked up by a wide range of developers. Though 2026 may accelerate its adoption significantly because Zig 1.0, the first stable release after its experimental status, is expected to launch this year.
The database trends in 2026, like most other programming technology trends, will be dominated by AI. The most actively used databases have remained the same for the last three years at least, with PostgreSQL, MySQL, and SQLite in the first, second, and third positions. This is unlikely to change in the next few years, but due to AI requirements, pure vector databases may climb the ranks faster than others. Also, existing databases, both SQL and NoSQL, that offer vector database and AI-relevant capabilities may experience higher adoption rates than others.
Some of the top database enterprises might consider in 2026 are:
Snowflake: As one of the most rapidly growing data platforms in the world, Snowflake is on many enterprises’ radars. It’s driven by its core engineering capabilities, as well as the AI capabilities integrated into the platform and database.
Pinecone: It’s one of the most widely used vector databases and is already used by multiple leaders in the space, including OpenAI and Microsoft. It’s a serverless, performance-focused database with low query latency and embedded security.
Redis: It’s a fast, multi-model database that combines in-memory data structures for speed with conventional data storage for a robust data management system without compromising performance. It’s open source and increasingly geared towards AI development.
MongoDB: MongoDB remains the top choice as a NoSQL database, but it’s still maturing its vector database capabilities. Improvements in that area may accelerate MongoDB adoption in 2026.
Implementing new software development technologies can be disruptive and, in some cases, require specialized skills that your existing team might lack. Outsourcing the work to a trusted partner like 10Pearls can significantly reduce headaches because we can ensure you have sustainable solutions that meet your specific requirements and seamlessly integrate with your current tech stack. In addition to our technology consulting, implementation, and integration services, we also offer custom software development services using the latest AI-powered tools to deliver exceptional results in less time and at a lower cost.
Reach out for a risk-free consultation to learn how we can transform your software development pipeline with the latest technologies.
The most optimal way to select the best technology for software development at any given time is to look at available technologies through the lens of your software development needs. A technology-first approach to development may undermine your project’s goals and lead to misaligned solutions. It’s also important to account for not just existing performance and governance needs but long-term scalability and maintainability as well.
There are no universally accepted best software development tools for enterprises, and the exact tech stack for each enterprise and project changes based on several factors. They include the scope of the project, performance requirements, security and governance needs, scalability, maintainability, and several other factors.
It’s not a simple yes or no question because development partners that use AI without a strong engineering foundation, guardrails, mature processes, and architectural understanding can deliver suboptimal and vulnerable software. In contrast, seasoned software development companies like 10Pearls augment their time-tested development processes with AI to enhance their agility and software quality. They can leverage AI assistants, LLMs, and other AI tools to make your software more secure than was possible with conventional builds because it accounts for the new generation of threats as well.
No. Even if you are outsourcing your software development to an engineering partner, understanding different technologies and stacks can help you make informed decisions about your project and the partners. You can determine whether they are urging you towards technologies they have expertise in or selecting the best stack for your needs.
Yes. The DevOps practices have always evolved, though for the last couple of years and in 2026 as well, AI will set the trajectory for evolution. DevOps and its variants, like AIOps, will not just focus on AI projects but will also include AI tools to make DevOps automation more nuanced, granular, and secure.
