Trigger.dev Pricing Guide: How Much Do You Actually Pay?
In this article, we learn about all the different pricing plans Trigger.dev offers.
Jul 2, 202610 mins
23 posts with this tag
In this article, we learn about all the different pricing plans Trigger.dev offers.
Meet Kitaru — open source durable execution for Python agents, built by the ZenML team. Crash recovery, human-in-the-loop, and replay from any checkpoint.
I rebuilt zenml.io — 2,224 pages, 20 CMS collections — from Webflow to Astro in a week using Claude Code and a multi-model AI workflow. Here's how.
ZenML's new Quick Wins skill for Claude Code automatically audits your ML pipelines and implements 15 best-practice improvements (from metadata logging to Model Control Plane setup) based on what's actually missing in your codebase.
Analysis of 1,200 production LLM deployments reveals six key patterns separating successful teams from those stuck in demo mode: context engineering over prompt engineering, infrastructure-based guardrails, rigorous evaluation practices, and the recognition that software engineering fundamentals—not frontier models—remain the primary predictor of success.
Explore 419 new real-world LLMOps case studies from the ZenML database, now totaling 1,182 production implementations—from multi-agent systems to RAG.
ZenML's Pipeline Deployments transform pipelines into persistent HTTP services with warm state, instant rollbacks, and full observability—unifying real-time AI agents and classical ML models under one production-ready abstraction.
How to build a production-ready financial report analysis pipeline using multiple specialized AI agents with ZenML for orchestration, SmolAgents for lightweight agent implementation, and LangFuse for observability and debugging.
In this CrewAI vs n8n, we explain the difference between the two and conclude which one is the best to build AI agents.
Compare the best CrewAI alternatives for building production AI workflows, including LangGraph, AutoGen, Google ADK, OpenAI Agents SDK, Pydantic AI, Langflow, Flowise, and LlamaIndex.
In this CrewAI pricing guide, we discuss the costs, features, and value CrewAI provides to help you decide if it’s the right investment for your business.
This Langflow vs LangGraph article explains all the differences between these AI agentic systems.
Lessons from the Maven Evals course are combined with 50+ real-world case studies from ZenML's LLMOps Database to show how companies like Discord, GitHub, and Coursera implement the Three Gulfs model and Analyze-Measure-Improve lifecycle to transform failing LLM systems into production-ready applications.
In this LangGraph vs Autogen article, we explain the difference between these platforms and when to use which one for the best results.
287 latest curated summaries of LLMOps use cases in industry, from tech to healthcare to finance and more. This blog also highlights some of the trends observed across the case studies.
Discover the top 8 LangGraph alternatives for scalable agent orchestration.
Discover the new ZenML MCP Server that brings conversational AI to ML pipelines. Learn how this implementation of the Model Context Protocol allows natural language interaction with your infrastructure, enabling query capabilities, pipeline analytics, and run management through simple conversation. Explore current features, engineering decisions, and future roadmap for this timely addition to the rapidly evolving MCP ecosystem.
A comprehensive overview of lessons learned from the world's largest database of LLMOps case studies (457 entries as of January 2025), examining how companies implement and deploy LLMs in production. Through nine thematic blog posts covering everything from RAG implementations to security concerns, this article synthesizes key patterns and anti-patterns in production GenAI deployments, offering practical insights for technical teams building LLM-powered applications.
This comprehensive guide explores strategies for optimizing Large Language Model (LLM) deployments in production environments, focusing on maximizing performance while minimizing costs. Drawing from real-world examples and the LLMOps database, it examines three key areas: model selection and optimization techniques like knowledge distillation and quantization, inference optimization through caching and hardware acceleration, and cost optimization strategies including prompt engineering and self-hosting decisions. The article provides practical insights for technical professionals looking to balance the power of LLMs with operational efficiency.
Explore real-world applications of Retrieval Augmented Generation (RAG) through case studies from leading companies in the ZenML LLMOps Database. Learn how RAG enhances LLM applications with external knowledge sources, examining implementation strategies, challenges, and best practices for building more accurate and informed AI systems.
Explore key insights and patterns from 300+ real-world LLM deployments, revealing how companies are successfully implementing AI in production. This comprehensive analysis covers agent architectures, deployment strategies, data infrastructure, and technical challenges, drawing from ZenML's LLMOps Database to highlight practical solutions in areas like RAG, fine-tuning, cost optimization, and evaluation frameworks.
As organizations rush to adopt generative AI, several major tech companies have proposed maturity models to guide this journey. While these frameworks offer useful vocabulary for discussing organizational progress, they should be viewed as descriptive rather than prescriptive guides. Rather than rigidly following these models, organizations are better served by focusing on solving real problems while maintaining strong engineering practices, building on proven DevOps and MLOps principles while adapting to the unique challenges of GenAI implementation.
Playing around with some genAI services and tools to create a story and comic that showcases the journey of MLOps adoption for a small team.
