Best AI Observability Tools in South America

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

NeuBird AI is an agentic AI platform built for IT and SRE teams who are done fighting fires manually. It watches your entire stack around the clock and when something goes wrong, it does more than surface an alert. It investigates by pulling from your logs, metrics, traces, and incident tickets, and figures out what actually broke and why, and tells the team exactly what to do next or simply takes care of it. Hawkeye by Neubird connects to the tools your team already relies on including Datadog, Splunk, PagerDuty, ServiceNow, AWS CloudWatch, and more. It reasons across all of them the way a senior engineer would, at any hour, without the 2 AM wake-up call. Incidents that once took hours now close in minutes, with MTTR reduced by up to 90%. Hawkeye runs continuously, deploys as SaaS or inside your own VPC, and fits within your existing security controls. No rip and replace. Just faster resolution, less noise, and more time back for the work that actually matters - The on-call coverage your team deserves, without the 2 AM wake-up calls

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

Taam Cloud is a comprehensive platform for integrating and scaling AI APIs, providing access to more than 200 advanced AI models. Whether you're a startup or a large enterprise, Taam Cloud makes it easy to route API requests to various AI models with its fast AI Gateway, streamlining the process of incorporating AI into applications. The platform also offers powerful observability features, enabling users to track AI performance, monitor costs, and ensure reliability with over 40 real-time metrics. With AI Agents, users only need to provide a prompt, and the platform takes care of the rest, creating powerful AI assistants and chatbots. Additionally, the AI Playground lets users test models in a safe, sandbox environment before full deployment. Taam Cloud ensures that security and compliance are built into every solution, providing enterprises with peace of mind when deploying AI at scale. Its versatility and ease of integration make it an ideal choice for businesses looking to leverage AI for automation and enhanced functionality.

Aquarium's innovative embedding technology identifies significant issues in your model's performance and connects you with the appropriate data to address them. Experience the benefits of neural network embeddings while eliminating the burdens of infrastructure management and debugging embedding models. Effortlessly uncover the most pressing patterns of model failures within your datasets. Gain insights into the long tail of edge cases, enabling you to prioritize which problems to tackle first. Navigate through extensive unlabeled datasets to discover scenarios that fall outside the norm. Utilize few-shot learning technology to initiate new classes with just a few examples. The larger your dataset, the greater the value we can provide. Aquarium is designed to effectively scale with datasets that contain hundreds of millions of data points. Additionally, we offer dedicated solutions engineering resources, regular customer success meetings, and user training to ensure that our clients maximize their benefits. For organizations concerned about privacy, we also provide an anonymous mode that allows the use of Aquarium without risking exposure of sensitive information, ensuring that security remains a top priority. Ultimately, with Aquarium, you can enhance your model's capabilities while maintaining the integrity of your data.

Langtrace is an open-source observability solution designed to gather and evaluate traces and metrics, aiming to enhance your LLM applications. It prioritizes security with its cloud platform being SOC 2 Type II certified, ensuring your data remains highly protected. The tool is compatible with a variety of popular LLMs, frameworks, and vector databases. Additionally, Langtrace offers the option for self-hosting and adheres to the OpenTelemetry standard, allowing traces to be utilized by any observability tool of your preference and thus avoiding vendor lock-in. Gain comprehensive visibility and insights into your complete ML pipeline, whether working with a RAG or a fine-tuned model, as it effectively captures traces and logs across frameworks, vector databases, and LLM requests. Create annotated golden datasets through traced LLM interactions, which can then be leveraged for ongoing testing and improvement of your AI applications. Langtrace comes equipped with heuristic, statistical, and model-based evaluations to facilitate this enhancement process, thereby ensuring that your systems evolve alongside the latest advancements in technology. With its robust features, Langtrace empowers developers to maintain high performance and reliability in their machine learning projects.

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

Foxglove is a sophisticated platform designed specifically for the visualization, observability, and management of data in the robotics and embodied AI sectors, effectively centralizing various large and complex multimodal temporal datasets such as time series, sensor logs, imagery, lidar/point clouds, and geospatial maps within a unified workspace. It empowers engineers to efficiently record, import, organize, stream, and visualize both live and archived data from robotic systems through user-friendly, customizable dashboards that feature interactive panels for 3D scenes, plots, images, and maps, thereby enhancing the understanding of robotic perception, cognition, and actions. Furthermore, Foxglove facilitates real-time integration with systems like ROS and ROS 2 through bridges and web sockets, supports cross-platform operations (available as a desktop application for Linux, Windows, and macOS), and accelerates the processes of analysis, debugging, and performance enhancement by synchronizing disparate data sources in both time and spatial contexts. Additionally, its intuitive design and comprehensive functionalities make it an invaluable tool for researchers and developers alike, ensuring a streamlined workflow in the dynamic field of robotics.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

Anticipate issues before they arise by utilizing an Azure AI anomaly detection service. This service allows for the seamless integration of time-series anomaly detection features into applications, enabling users to quickly pinpoint problems. The AI Anomaly Detector processes various types of time-series data and intelligently chooses the most effective anomaly detection algorithm tailored to your specific dataset, ensuring superior accuracy. It can identify sudden spikes, drops, deviations from established patterns, and changes in trends using both univariate and multivariate APIs. Users can personalize the service to recognize different levels of anomalies based on their needs. The anomaly detection service can be deployed flexibly, whether in the cloud or at the intelligent edge. With a robust inference engine, the service evaluates your time-series dataset and automatically determines the ideal detection algorithm, enhancing accuracy for your unique context. This automatic detection process removes the necessity for labeled training data, enabling you to save valuable time and concentrate on addressing issues promptly as they arise. By leveraging advanced technology, organizations can enhance their operational efficiency and maintain a proactive approach to problem-solving.

Fiddler is a pioneer in enterprise Model Performance Management. Data Science, MLOps, and LOB teams use Fiddler to monitor, explain, analyze, and improve their models and build trust into AI. The unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. It addresses the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler seamlessly integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale and increase revenue.

Introducing your comprehensive insight management solution tailored for the performance of computer vision models. It enables users to accurately identify the specific factors behind model failures, facilitating effective communication between product managers and engineers through valuable insights. With Manot, product managers gain access to an automated and ongoing feedback mechanism that enhances collaboration with engineering teams. The platform’s intuitive interface ensures that both technical and non-technical users can leverage its features effectively. Manot prioritizes the needs of product managers, delivering actionable insights through visuals that clearly illustrate the areas where model performance may decline. This way, teams can work together more efficiently to address potential issues and improve overall outcomes.

Respan is an AI observability and evaluation platform designed to help teams monitor, test, and optimize AI agents at scale. It provides deep execution tracing across conversations, tool invocations, routing logic, memory states, and final outputs. Rather than stopping at basic logging, Respan creates a closed-loop system that links monitoring, evaluation, and iteration into one workflow. Teams can define stable, metric-driven evaluation frameworks focused on performance indicators like reliability, safety, cost efficiency, and accuracy. Built-in capability and regression testing protects existing behaviors while enabling controlled experimentation and improvement. A dedicated evaluation agent uses AI to analyze failed trials, localize root causes, and suggest what to test next. Multi-trial evaluation accounts for non-deterministic outputs common in modern AI systems. Respan integrates with major AI providers and frameworks including OpenAI, Anthropic, LangChain, and Google Vertex AI. Designed for high-scale environments handling trillions of tokens, it supports enterprise-grade reliability. Backed by ISO 27001, SOC 2, GDPR, and HIPAA compliance, Respan delivers secure observability for production AI systems.