Section 1 : Observability: Origins to Looking Ahead
Observability has evolved and found its way from its origins in control theory into the realm of IT and software engineering, particularly with the rise of complex, distributed systems and cloud-native applications. Today, observability encompasses a wide array of data points—including logs, metrics, and traces—to provide a comprehensive understanding of system behavior and performance.

Pioneering Days of Monitoring (Early 2000s) :
The monitoring landscape in the pre-2000s era was characterized by a rudimentary but essential focus on keeping tabs on system performance, which was entirely on-prem. During this time:
Internet traffic was low, and server infrastructures were limited, making telemetry data manageable.
Monitoring relied heavily on manual checks and simple logging to identify and address system issues on an ad hoc basis.
Key Milestone:
Splunk, founded in 2003, emerged as a leader in machine-generated data analysis, enabling organizations to make sense of log data efficiently (Splunk History)
Application performance monitoring (APM)'s ascent (Mid 2000s):
As software systems grew in complexity and cloud adoption began, the need for proactive monitoring tools became evident. The mid-2000s saw the rise of Application Performance Monitoring (APM), addressing:
Challenges posed by distributed systems.
Monolithic, 3-tier architectures were the norm, where software releases were infrequent (1-2 times per year).
Key Developments:
APM solutions offered developers real-time insights, tracking errors and application performance, representing a transformative leap in monitoring.
Major players emerged, including: Dynatrace, AppDynamics, New Relic
The Shift Towards Comprehensive Observability (Early 2010s):
The rise of microservices and cloud computing introduced unprecedented complexity and telemetry data growth. Observability expanded beyond traditional monitoring, integrating logs, metrics, and traces under frameworks like MELT (Metrics, Events, Logs, Traces).
Key Player : Datadog emerged as a leader, offering unified views of system performance.
Adapting to Cloud-Native Technologies (Late 2010s):
Cloud-native tools, containerization, and orchestration (e.g., Kubernetes) demanded observability solutions at scale. These systems evolved to provide actionable insights and analytics-driven workflows for IT operations.
Players :
Cribl: Focused on cost-optimized solutions.
Honeycomb.io: Prioritized high-fidelity, high-cardinality observability.
Open Source: Tools like Grafana and OpenTelemetry gained traction for data visualization and telemetry collection.
AI for Observability and Observability for AI (Early 2020s):
AI and observability are transforming IT and AI/ML systems by automating insights and ensuring model reliability.
AI for Observability
AI enhances system monitoring by processing vast telemetry data and automating workflows:
Anomaly Detection: Identifies outliers in real-time to prevent incidents (Datadog).
Root Cause Analysis: Pinpoints issues across distributed systems (Dynatrace Davis AI).
Predictive Analytics: Forecasts failures using historical data (Splunk).
Automated Workflows: Reduces manual effort with intelligent triage (PagerDuty AIOps).
Observability for AI
Observability ensures AI models are performant, transparent, and fair:
Performance Monitoring: Tracks metrics like accuracy and latency (Fiddler AI).
Bias Detection: Ensures fairness and explainability (Fiddler AI).
Drift Management: Identifies data/model drift for relevance (Evidently AI).
Standardization: Simplifies telemetry collection with OpenTelemetry.
Trends
LLMs in Observability: Automating workflows with tools like Moogsoft and BigPanda.
Vertical Solutions: Tailored observability for industries (Vunet, Helicone).
Open Standards: OpenTelemetry drives cost-efficient integration.
We take a detailed view of emerging themes in today’s world in a later section on Emerging Trends in Observability.

Section 2 : Why CFOs Hate Observability Tools
As observability becomes central to modern IT operations, enterprises face the challenge of managing the exponential growth in telemetry data volume. This surge, driven by the proliferation of microservices, cloud-native architectures, and the continuous expansion of digital ecosystems, has significant implications for observability costs.
Unprecedented growth in data:
The complexity of distributed applications and multi-cloud environments has led to an exponential increase in telemetry data, including metrics, traces, and logs. While this provides granular system insights, managing such vast datasets has become a formidable challenge.
Data Explosion: IDC projects global data volumes to reach 180 zettabytes by 2025 (IDC Report).
The adoption of multi-cloud strategies and the complexity of managing machine-generated data contribute to this surge, straining storage and processing infrastructure.

Burgeoning Observability Costs: A Growing Challenge
The exponential growth in telemetry data has led to soaring observability costs, making traditional approaches to data storage, analysis, and visualization increasingly unsustainable. Organizations must rethink strategies to manage and derive value from their observability data.
The financial success of leaders like Datadog ($2.5B TTM revenue) and Dynatrace ($1.56B TTM revenue) highlights the critical role of observability, but also reveals its significant financial burden:
Case Study: A crypto exchange’s $65M Datadog invoice showcases how observability costs can spiral out of control.
Operational Downtime Costs: Companies like Delta Airlines have faced losses exceeding $150M from just five hours of downtime.
Skyrocketing Expenses: Observability costs often match or exceed the expenses of the underlying infrastructure they monitor (source).
Strategies for Cost Optimization in the future:
As observability costs rise, companies are adopting innovative approaches to balance performance with affordability. Here’s an overview of key strategies and examples, supported by insights and external references:
Data Sampling Techniques
By collecting only subsets of telemetry data, organizations reduce storage and processing overhead while preserving critical insights.
Examples:
Honeycomb.io: Implements dynamic sampling for high-value data points.
Dynatrace: Uses AI to filter unnecessary telemetry data dynamically.
Impact: Reduces storage costs by up to 30% (Gartner Report).
Intelligent Data Retention Policies
Tiered storage prioritizes high-value data while archiving or deleting less-critical information.
Serverless Observability
Dynamically scales resources based on demand, avoiding over-provisioning and unnecessary expenses.
Examples:
AWS Lambda: Provides real-time monitoring with pay-as-you-go pricing.
Splunk Observability: Aligns costs with usage.
Impact: Reduces operational costs by 30–50% (AWS Whitepaper).
Open-Source Tools
Open-source frameworks minimize licensing costs while offering robust capabilities.
Examples:
Prometheus & Grafana: Popular for cost-efficient metric monitoring and visualization.
OpenTelemetry: Standardizes telemetry data collection across platforms.
Impact: Cuts costs by up to 50% compared to proprietary tools (Forrester).
Strategic Vendor Negotiations
Optimizing vendor contracts reduces waste and aligns spending with business needs.
Examples:
New Relic: Offers volume-based enterprise pricing.
Sumo Logic: Employs usage-based models to align with data volume.
Impact: Improves ROI and reduces waste through smarter spending (IDC Insights).
Cost-Aware Observability Platforms
Platforms with built-in cost analysis help optimize expenses without compromising visibility.
Examples:
Cribl: Efficiently routes and enriches data to cut ingestion costs.
Chronosphere: Tailors observability for high telemetry volumes.
CtrlB.ai: Uses AI-driven log correlation and automated root cause analysis to optimize observability costs and reduce MTTR.
Last9: Focuses on reliability insights and SLO-driven observability, helping enterprises reduce cloud expenditures while improving system resilience.
Impact: Provides real-time cost visibility and savings opportunities.
AI-Powered Efficiency
AI enhances observability by automating workflows, detecting anomalies, and streamlining data analysis.
Section 3 : Current Landscape
The convergence of APM, monitoring, and observability is redefining the market, expanding beyond core capabilities into AI-driven, analytics-rich workflows that resemble business intelligence for IT operations. Legacy players like Datadog and Splunk have embraced platform-based approaches, integrating diverse observability functionalities under one roof. Over the past decade, innovative challengers have emerged, targeting specific observability pain points with differentiated and scalable solutions.
Legacy Incumbents: Innovating to Lead
Established players are evolving to meet rising demands, leveraging AI and machine learning to enhance system visibility, performance optimization, and issue resolution.
Player | Year founded | Public/private | Valuation | ARR |
2010 | Public | $41.94B | $2.5B | |
2005 | Public | $14.88B | $1.56B | |
2003 | Acquired by Cisco | $28B (Acquisition) | $4.2B | |
2008 | Acquired by Francisco Partners | - | Acquired | |
2008 | Acquired by Cisco | - | Acquired | |
IBM APM (Instana) | 2015 | Acquired by IBM | - | - |
Datadog continues to lead the market by expanding its platform with Security Monitoring and Application Security features. This integration enables comprehensive visibility across IT infrastructure, combining performance metrics with security events. Datadog’s AI-driven anomaly detection helps organizations proactively identify and resolve issues before they impact users.
Dynatrace leads with its AI-powered Davis AI, automating root cause analysis and providing real-time insights across the application stack. The platform is optimized for cloud-native architectures, offering robust support for microservices and containerization with Kubernetes monitoring.
Splunk positions itself as a data-to-everything platform, empowering organizations with versatile observability, security, and analytics tools. Its powerful querying and visualization capabilities provide actionable insights into system performance.
New Relic delivers end-to-end visibility across applications, infrastructure, and user experiences. Its intuitive dashboards and analytics help unify observability data under a single pane of glass. Its open architecture allows seamless integration with a broad range of technologies.
AppDynamics focuses on business-centric observability by aligning IT performance with business outcomes. Its platform provides application performance monitoring (APM) and user journey analytics to detect anomalies and optimize performance.
Challengers:
Newer players redefined the observability landscape with innovative approaches to address the complexities of distributed systems and cloud-native architectures. These companies stand out by focusing on specific capabilities, cost efficiency, and open-source principles. Below are key challengers shaping the observability market:
Player | Year founded | Public/private | Valuation | Funding raised | ARR |
2016 | Private | ~$800M | $96.9M | ~$50M+ | |
2019 | Private | ~$1.6B | $343M | ~$100M+ | |
2014 | Private | $330M | ~$125M+ | ||
2015 | Acquired by ServiceNow | ~512M | $70M | - |
Honeycomb is an observability-driven development platform enabling engineering teams to debug, analyze, and enhance complex systems in real-time. Its event-driven observability approach provides granular insights into user interactions and system performance.
Key Features: Real-time debugging, iterative delivery process improvements, and granular user insights.
Use Case: Improves software delivery by enabling rapid response to issues.
Chronosphere focuses on cloud-native time-series data management, optimizing storage and analysis. Built on an open-source foundation, it supports scalability and cost-effective observability for data-intensive environments.
Key Features: Cost-efficient telemetry handling, scalability for distributed systems, and robust community support.
Use Case: Ideal for organizations seeking to manage high telemetry volumes cost-effectively.
Grafana A leader in open-source observability, Grafana provides tools for visualizing and analyzing metrics, logs, and traces. Its customizable dashboards and advanced querying capabilities make it a popular choice for cost-conscious organizations.
Key Features: Open-source flexibility, community-driven innovation, and comprehensive visualization tools.
Evolving Role: Transitioning into a full-platform solution to help enterprises consolidate tools while maintaining data ownership.
Lightstep (Acquired by ServiceNow) specializes in deep observability for microservices-based applications, focusing on precise tracing and root cause analysis. Its tools enable real-time understanding of system changes across distributed environments.
Key Features: Microservices observability, real-time performance insights, and proactive issue resolution.
Use Case: Helps teams address performance issues before they impact end-users.
OpenTelemetry is a collaborative open-source project that standardizes telemetry data collection and processing. Its vendor-neutral framework fosters interoperability, making it easier for organizations to integrate diverse observability tools.
Key Features: Standardized data collection, community-driven innovation, and wide tool compatibility.
Use Case: Simplifies observability adoption by providing a unified, interoperable framework.
Visionaries/Emerging:
A new wave of companies is redefining observability with specialized, targeted solutions addressing unique challenges such as cloud security, error tracking, user behavior analytics, and browser-based monitoring. These innovators deliver unique value by focusing on specific use cases, complementing the offerings of larger incumbents and resonating with customers seeking agility, cost efficiency, and precision.
Player | Year Founded | Public/Private | Valuation | Funding Raised | ARR (2024) |
2010 | Acquired by Francisco Partners | ~$1.7B | $29M | - | |
2018 | Private | ~$3.5B | $725M | ~$150M+ | |
2012 | Private | ~$3.1B | $217M | ~$125M+ | |
Checkly | 2018 | Private | ~$150M | $32M | ~$10M+ |
2015 | Private | ~$225M+ | $230M | ~$50M+ |
Sumo Logic A cloud-native observability platform that unifies logs, metrics, and traces while integrating security and observability for a holistic IT view. Leveraging machine learning, Sumo Logic delivers advanced analytics and anomaly detection to enhance reliability and performance.
Key Features: Unified observability, ML-driven insights, and proactive anomaly detection.
Use Case: Combines observability with security for comprehensive monitoring.
Cribl A vendor-agnostic observability pipeline focused on real-time data processing and cost efficiency. Cribl’s flexible pricing, based on event volume, enables scalability and adaptability while ensuring organizations retain full data ownership.
Key Features: Real-time data routing, efficient telemetry pipelines, and cost management.
Use Case: Optimizes telemetry for enterprises consolidating observability tools.
Sentry An open-source platform for error tracking and performance monitoring, designed for developers. Sentry integrates with multiple programming languages and frameworks, offering fast issue identification and resolution.
Key Features: Real-time error tracking, performance monitoring, and developer-focused tools.
Use Case: Simplifies debugging and enhances application reliability.
Checkly A browser-based monitoring solution that simulates real user interactions to provide actionable insights into the end-user experience. Its scriptable checks and advanced tools empower teams to proactively address issues.
Key Features: Real-time user simulation, advanced monitoring tools, and actionable insights.
Use Case: Proactively improves user experience by resolving potential bottlenecks.
Coralogix A log analytics platform optimized for scalability and cost efficiency. By avoiding raw data indexing, Coralogix reduces storage and processing costs while delivering real-time log clustering and pattern detection.
Key Features: Cost-efficient analytics, advanced visualization, and real-time insights.
Use Case: Ideal for organizations managing high telemetry volumes with tight budgets.
Market mapping: How they all stack up
The observability market is evolving from traditional infrastructure monitoring to encompass end-to-end solutions addressing modern IT complexities. By understanding how players stack up across feature sets and functionalities, we can map the diverse solutions shaping this space.


Traditional Feature Category:
Infrastructure & Network Monitoring
Tracks performance metrics (CPU, memory, bandwidth, etc.) for physical and virtual components.
Evolution: From basic availability checks to advanced metrics and proactive troubleshooting.
Key Players: Datadog, Chronosphere, SolarWinds, Prometheus.

Application Monitoring (APM)
Monitors software performance, providing insights into response times, transaction volumes, and code-level issues.
Evolution: Deeper integration with databases and third-party services.
Key Players: Dynatrace, AppDynamics, Sentry, Honeycomb, Instana.

Real User Monitoring (RUM)Analyzes user interactions to optimize web application performance and user experience.
Evolution: Advanced features like session replay and heatmaps.
Key Players: New Relic Browser, Dynatrace RUM, FullStory, Zipy.
Synthetic Monitoring
Simulates user interactions to identify performance bottlenecks.
Evolution: Includes transaction monitoring and ISP performance benchmarking.
Key Players: Pingdom, Checkly, BetterStack.
Log Management & Analytics
Collects and analyzes logs to detect anomalies and derive insights.
Evolution: Machine learning-driven log parsing and correlation.
Key Players: Splunk, Elastic, Sumo Logic, Graylog.
Incident Management
Automates incident resolution with ticketing, escalation, and retrospectives.
Key Players: Jira Service Management, PagerDuty, OpsGenie.
Visualization
Creates dashboards for actionable observability data.
SDLC Observability
Extends observability practices to the software development lifecycle (SDLC), covering code commits, builds, tests, deployments, and production releases. It also tracks developer productivity and provides actionable insights for improving engineering efficiency.
This category emerged to address the need for end-to-end visibility and traceability across the software delivery pipeline, integrating deeply with DevOps and CI/CD tools. Often referred to as software engineering intelligence, SDLC observability tools offer analytics to measure and improve team performance.
First Wave (Established Leaders):
Companies like Jellyfish, LinearB, and Pluralsight Flow are considered pioneers.
Second Wave (Challengers):
A newer wave of startups, often at the seed stage, such as Uplevel, Swarmia, Hatica, Hivel, and Allstacks, are disrupting the market by addressing gaps in first-generation solutions. These companies benefit from market awareness and education created by the earlier players, enabling them to win deals with differentiated approaches.
Relevant Players:
Jellyfish, LinearB, CodeClimate, Uplevel, Allstacks, Hatica, Hivel.
AIOps & LLM Observability
Combines AI/ML algorithms to analyze data at scale, identify patterns, anomalies, and trends, and automate processes such as log parsing, anomaly detection, and predictive analytics. LLM observability focuses on monitoring the performance, bias, and drift of large language models in production.
This category emerged as a response to the growing complexity of log data and AI/ML workflows. Tools now provide real-time anomaly detection, bias tracking, model performance monitoring, and root cause analysis for AI/LLM applications.
Pipeline Observability ensures the efficient collection, processing, enrichment, and routing of telemetry data. By normalizing and filtering data, it transforms raw telemetry into actionable insights for observability and SIEM tools. Modern pipelines optimize costs by discarding low-value data and enriching high-value data for better decision-making.
The Evolution of Pipeline Observability
Originally focused on basic ingestion and routing, pipeline tools now support:
ETL Processes: Advanced extraction, transformation, and loading for diverse data workflows.
Streaming Data: Real-time processing for instant insights (Apache NiFi).
Cloud-Native Integrations: Seamless compatibility with modern architectures.
AI-Driven Pipelines
Next-gen tools leverage AI for:
Key Players
Cribl: Vendor-agnostic routing and enrichment.
Coralogix Streama: Scalable and cost-effective pipelines.
Chronosphere: Cloud-native observability at scale.
Fluentd: Open-source data collection.
Apache NiFi: Streaming data automation.
Mezmo: Real-time streaming and transformation.
Vector (Acquired by Datadog): High-performance telemetry routing.
Section 4 : Emerging trends
Convergence of SIEM- Observability :
The integration of Security Information and Event Management (SIEM) and observability is transforming operational resilience and security. Observability tools provide critical telemetry data, empowering security teams to detect, analyze, and respond to threats effectively.
Access to Data: Telemetry pipelines, such as those from Cribl, enable efficient data sharing between security and IT teams. The Cribl-Securonix partnership demonstrates enhanced threat detection through seamless integration.
Managing Data Overload: AI and ML filter irrelevant data, prioritize high-value alerts, and reduce costs, boosting SIEM efficiency and preventing alert fatigue.
Anomaly Detection: Combining logs, metrics, and traces with security data accelerates root cause analysis and improves response times.
Real-Time Prevention: Deep observability integrated with security data enables SecOps teams to detect risks in real time, ensuring compliance and minimizing disruptions.
The convergence of observability and SIEM enhances threat detection, response times, and security posture, leveraging AI-driven insights and telemetry pipelines for unified, proactive operations.
AI for Observability (Increased GenAI, LLM use cases in observability solutions:
The integration of GenAI and LLMs into observability solutions is transforming how organizations monitor, debug, and optimize complex systems. Unlike traditional LLM observability, which focuses on monitoring AI models, these advancements enhance existing observability platforms to deliver smarter, more proactive insights. Four emerging micro-trends illustrate this evolution:
Automated Root Cause Analysis
LLMs analyze vast structured and unstructured datasets to pinpoint root causes of issues and provide actionable resolutions. This drastically reduces Mean Time to Resolution (MTTR), enhancing uptime and reliability. Example: Moogsoft and BigPanda leverage AI-driven incident correlation to identify and resolve root causes faster.
Predictive Maintenance
GenAI and LLMs predict potential issues before they occur by analyzing historical and real-time telemetry data (MELT). This proactive approach minimizes outages and ensures smooth operations. Example: AI tools analyze patterns and anomalies to detect impending failures, enabling preemptive action and cost savings.
AI Assistants: Personalized Insights and Recommendations
AI assistants enable simplified querying and deliver tailored insights to meet the unique needs of users. Examples:
Splunk SignalFlow: A real-time analytics engine that processes incoming data streams to generate actionable metrics, charts, and detectors.
New Relic AI: Uses OpenTelemetry (OTEL) agents for smarter data filtering, enabling cost-efficient observability without compromising insights.
Agents
AI Agents for Observability
The emergence of AI agents for observability marks a significant leap in how modern IT environments are monitored, optimized, and maintained. Unlike traditional observability tools that focus on collecting and presenting telemetry data, AI agents are designed to autonomously analyze, contextualize, and act on data. These agents are transforming observability by automating critical tasks, reducing noise, and enabling actionable insights in real time.
What Sets AI Agents Apart?
Proactive Optimization: AI agents use advanced algorithms to predict potential system issues, optimize workflows, and suggest preventive measures.
Autonomous Decision-Making: These agents leverage real-time telemetry and historical patterns to perform root cause analysis and implement fixes autonomously or recommend actions to teams.
Dynamic Telemetry Management: By prioritizing and enriching high-value telemetry data while filtering irrelevant noise, AI agents reduce observability costs and improve efficiency.
Examples:
PagerDuty AIOps: Prioritizes incidents based on business impact for quicker resolution.
NudgeBee: Provides intelligent alerts and real-time system optimization with AI-driven recommendations and seamless integration.
Moogsoft: Specializes in noise reduction, event correlation, and autonomous resolution workflows to reduce MTTR.
Grepr: Excels in dynamic telemetry optimization, intelligent filtering, and cost-effective real-time monitoring.
AI-driven observability, powered by GenAI and LLMs, is shifting from reactive monitoring to intelligent, proactive systems optimization. By enabling automated root cause analysis, predictive maintenance, and personalized insights, these advancements are dramatically reducing MTTR, preventing outages, and enhancing operational efficiency. The integration of AI assistants and agentic observability solutions is paving the way for a smarter, more resilient IT landscape.
Observability for Agents
The rapid adoption of agentic AI—where AI agents autonomously execute tasks and make decisions—demands a new layer of observability that goes beyond traditional metrics, logs, and traces. Unlike static applications, agentic systems rely on large language models (LLMs), reinforcement learning, and intricate decision-making processes that evolve over time.
Why Observability for Agents?
Dynamic Decision-Making: Agents continuously adapt based on evolving inputs and objectives. Understanding why an agent made a specific choice is crucial for debugging, optimization, and trust.
Contextual Awareness: Agents often rely on external data sources, knowledge graphs, or user interactions. Observability tools must capture these contextual dependencies to provide actionable insights.
High-Cardinality Data: Multiple agents operating in parallel can generate extremely high-cardinality telemetry, necessitating specialized data collection and storage solutions.
Tools like Moogsoft and BigPanda leverage AI-driven incident correlation to surface the root causes behind agent actions.
Focus on Specific Use Cases/Vertical Focus:
Transaction Observability:
VuNet: A DVC portfolio company providing AI-driven real-time transaction monitoring, cross-tier analytics, and actionable insights to reduce failures in financial services and other transaction-heavy sectors.
Highnote: Offers transaction observability tailored for fintech and payment processors to optimize end-to-end payment workflows.
Financial Services and Insurance:
E-commerce Observability:
Convictional: Provides supply chain and transaction observability for e-commerce platforms, helping businesses track performance and mitigate bottlenecks.
CommerceIQ: Delivers observability solutions for retail supply chains and online marketplaces to monitor fulfillment and inventory workflows.
Healthcare and Life Sciences:
Innovaccer: Provides observability solutions to streamline patient data, ensuring reliability and efficiency in healthcare workflows.
Clarify Health: Monitors performance and efficiency in healthcare delivery systems using observability-driven insights.
Standardization led by Open-Source Solutions:
The observability landscape is increasingly shaped by open-source solutions, driven by standardization efforts to streamline telemetry collection and processing. Projects like OpenTelemetry (OTel), under the Cloud Native Computing Foundation (CNCF), are leading the charge in creating unified frameworks for observability data, fostering accessibility and innovation.
Key Developments in Open-Source Observability
Standardization Efforts:
CNCF initiatives include query language standardization, enhanced data compression for the OpenTelemetry Protocol (OTLP), and CI/CD telemetry integration, making observability frameworks more efficient and consistent.
Open-Source Startups Driving Innovation:
Signoz: Provides an open-source observability platform as an alternative to Datadog, integrating metrics, traces, and logs in a single interface.
Elementary: Focuses on cost-effective, open-source data observability.
Evidently.ai: Offers open-source tools for monitoring machine learning models alongside its core features.
Vector: An open-source observability data pipeline, recently acquired by Datadog, optimizing telemetry data routing.
Popular OSS Tools:
Grafana: A widely adopted platform for visualizing observability data.
Prometheus: A leading open-source tool for metrics monitoring.
Jaeger: Open-source tracing for distributed systems.
eBPF: Redefining Observability
eBPF (Extended Berkeley Packet Filter) is revolutionizing observability by enabling deep, kernel-level insights with minimal overhead. Initially designed for network filtering, eBPF now powers real-time monitoring, profiling, and debugging across distributed systems, addressing the complexities of modern IT environments.
Why eBPF Matters
Kernel-Level Visibility: Offers unparalleled depth into system calls, process behaviors, and network events directly from the kernel.
Low Overhead: Operates efficiently, minimizing performance impact on monitored systems.
Secure and Sandbox-Friendly: Ensures system stability with isolated execution, avoiding kernel crashes.
Core Use Cases
Application Monitoring: Tools like Pixie leverage eBPF to capture telemetry without requiring code instrumentation.
Network Observability: Cilium uses eBPF for Kubernetes network insights and security.
Security Monitoring: Falco detects real-time anomalies for intrusion prevention.
Debugging and Profiling: BPFtrace enables dynamic tracing for performance analysis.
Infrastructure Monitoring: Platforms like Sysdig provide host-level insights using eBPF.
At Dallas Venture Capital (DVC), we are committed to investing in cost-optimization-focused observability solutions and observability for AI/agents, two areas we believe will shape the future of IT operations. With portfolio companies like VuNet, which specializes in AI-driven real-time transaction observability, and Fiddler AI, a leader in explainable AI and model monitoring, we’ve demonstrated our conviction in this space. If you’re a founder building an observability solution or disrupting an existing observability category with a new approach, we would love to engage with you! Feel free to reach us at: ravish@dallasvc.com, vinayd@dallasvc.com, investments@dallasvc.com
Sources:
Market map:
AI in obs, LLMS: