Runpod
DevOpsExperiment, train, deploy with one platform. Go from idea to production without replatforming.
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Tick Compare on any two to four cards to put them side by side.
Experiment, train, deploy with one platform. Go from idea to production without replatforming.
Suits regulated enterprises, does not fit small startups.
Great for quick test automation but lacks detailed code maintenance tools.
Build a private website without sharing content.
Limited to AI simulations, not full cloud or data science development.
Rent GPUs for AI and ML at real-time, transparent prices.
Pairs quiz generation and a learning chatbot with a built-in bug-reporting sidebar.
Suits industrial AI, does not excel at consumer use.
Puts a sidebar of R&D tools, GPT-4 and Midjourney access into one internal browser panel.
Answers plain-language questions about building codes, trained specifically for structural engineering.
Adds one-click buttons that post pipeline-trigger comments on GitHub pull requests.
NopeCHA for Chrome solves all CAPTCHA types.
Improves Tet by identifying poor data usage.
Two-factor auth, quick login.
Automates code reviews with AI, saving time and improving quality.
Translates natural language to Linux commands.
Turns a GitLab issue title into a branch name, using your own OpenAI key.
Join us to help ensure AI wins for democracy. Challenges we face are redesigning compute infrastructure and deploying gigawatts of solar.
Online assessment and proctoring solutions.
Unified platform for app and AI testing.
Achieve near zero false positives with over 99% defect detection accuracy, deploy in just a few days.
Too rigid for manual workflows.
Enables AI and autonomy in various industries.
All Quiet streamlines on-call management and incident response for engineering teams.
Provides ticket suggestions for MSPs.
A token-powered marketplace connecting AI developers with GPU compute, backed by its own data centre.
Manage AI prompts with version control and testing.
Specialized language models for real-world business decisions. All data stays in Europe.
Free to self-host, but lacks hosted option.
Runs AI workloads locally but lacks built-in Kubernetes management.
Generates test ideas from a webpage's elements, then turns them into ready-to-run automation scripts.
Monitor uptime, performance, and incidents.
Antibot solution, prevents automated attacks.
An open-source Python framework for building and deploying real-world ML workflows at scale.
Delivers unparalleled inference capability.
Suits engineering leaders; not for those looking to manage projects.
Kubernetes GUI for managing multiple clusters.
K8sGPT automates Kubernetes management with AI.
Capture bugs, feedback, and ideas in one Jam that keeps the moment and available technical context together for teammates or agents.
IDB CRUD simplifies IndexedDB management with an intuitive drawer interface.
Java developers in large enterprises should use it, others might find better fits.
Suits teams with fast-moving codebases; may not be ideal for static documentation needs.
CryptoDo simplifies smart contract creation with a visual builder, but it's limited to multichain projects and lacks detailed customization.
Charm turns your terminal into a glamorous coding space.
Open-source AI gateway, tracks and caps LLM spend.
Builds test cases from your own clicks and flags when app changes break existing tests.
Distribute AI workloads across clouds and GPUs.
AI detector for precise text checking. Less than 0.5% false positives.
Three worth starting with
Terms to know
DevOps tools cover the work between writing code and keeping it running: deploying, monitoring, managing containers and cloud workloads. Listings here include K8sStudio for Kubernetes and Autobot AI for workflow automation, alongside some general browser utilities. Look at which part of the pipeline a tool touches and whether it needs write access to production systems.
They can explain cluster state, suggest commands and draft configuration, which helps with debugging. Letting an AI apply changes directly to production is a bigger step. Most teams keep a human approval step and review generated manifests before they run.
Common uses are writing and explaining pipeline files, reading logs, summarising incidents and drafting runbooks. The tools save time on repetitive text and configuration, but they do not know your environment, so every suggested command still needs checking.
DevOps is about delivering and operating software reliably. Network security focuses on protecting systems from attack, and API development on building the interfaces between services. The three overlap in practice, especially around secrets and access control.