Elastic named a Leader in the IDC MarketScape: Worldwide Observability Platforms 2025 Vendor Assessment

With the introduction of Streams, we're redefining observability. With Streams, SREs no longer need to spend time wrangling data before they can be investigators. Logs are the primary investigation signals because Streams provides SREs with the ability to:

• Log everything in any format and stop worrying about pipelines: Stop wasting time building and maintaining complex ingestion pipelines. Send logs in any format, structured or unstructured, from any source directly to a single Elastic endpoint without needing specific agents. Use OpenTelemetry collectors or any other data shipper to send logs to Elastic. Streams’ AI-driven processing parses and structures your log data, making it immediately ready for investigation. This means you can adapt to new log formats on the fly without the need to maintain brittle configurations. Streams ensures you always have the data you need at the moment you need it.

• Not only collect logs but also get answers from them: Streams analyzes your data to surface “Significant Events,” proactively identifying critical errors, anomalies, and performance bottlenecks like out-of-memory exceptions. Instead of manually sifting through terabytes of data, you get a clear, prioritized starting point for your investigation. This allows you to go from symptom to solution in minutes, fixing issues before they impact users.

• Achieve complete visibility at a lower cost: Get comprehensive visibility across all your services without the expected expense. By intelligently structuring data and surfacing only the most critical events, Streams reduces operational complexity and dramatically cuts down root cause analysis time. This efficiency allows you to store all relevant log data cost-effectively, ensuring you never have to sacrifice crucial visibility to meet a budget. Get clearer answers faster and lower your total cost of ownership.

Streams revolutionizes observability by transforming logs from a noisy and expensive data source into a primary investigation signal. Through AI-powered capabilities like automatic partitioning, parsing, retention management, and the surfacing of Significant Events, Streams empowers SREs to move beyond data management and directly pinpoints the root cause of issues. By reducing operational complexity, lowering storage costs, and providing complete visibility, Streams ensures that logs, enriched by AI, become the fastest path to resolution by answering the why? for observability.

A core differentiator of Elastic is our relentless commitment to innovation in observability. We're constantly pushing boundaries and developing capabilities that transform how teams monitor, analyze, and optimize their systems. Here are some Elastic innovations over the last year:

• Streams: A rethinking of how observability is done by organizing and finding meaning and problems in your data automatically with AI

• Elastic AI Assistant: Fully agentic and retrieval augmented generation (RAG)-based using your own GitHub repos and Wikis for specific and relevant troubleshooting and remediation insights

• EDOT: A fully scalable and supported OTel distribution

• Elastic Cloud Serverless: A new completely stateless storage architecture

• LLM observability: To understand if your models are performing optimally and hallucination-free

• Elasticsearch logsdb index mode: Saving up to 70% of log storage costs

• Elastic Agent Builder: Build your own AI Agents to use observability data for your domain-specific use cases, and empower enterprise large language models (LLMs) via model context protocol (MCP).

• Universal Profiling: Donated to the OpenTelemetry project for lightweight code-level profiling

We don't just ship innovations like those above; we also share our thinking. Elastic Observability Labs is a dedicated space where we expose our technical decisions, experiments, and early innovations through transparent development and active community engagement. It's working ideas, engineering stories, and lessons learned as we build. We help customers and practitioners see what's next by providing:

• Insider views on how the OpenTelemetry project is stewarded and evolving

• Guidance on bridging between log data and OTel context, making observability more holistic

• Features like Elastic’s Managed OTLP endpoint — a serverless, scalable ingestion path offering native OTLP support, reducing the barrier to instrumentation

• Capabilities like Logs Essentials that lets users ingest, store, and query logs without the overhead of managing infrastructure

• Technical previews like ES|QL LOOKUP JOIN that lets users enrich logs with metrics and traces dynamically — no need to denormalize at ingest time

Elastic delivers a unified AI-powered platform that brings together observability, security, and search so that organizations can detect, analyze, and act on relevance across all their data in real time. Within observability, Elastic’s differentiated approach is grounded in being:

• Open and extensible: OTel-first and Prometheus-compatible ensures interoperability across diverse environments.

• Scalable without limits: A stateless SaaS architecture that decouples compute from storage and indexing from search that scales seamlessly. Using cost-effective, cloud-native object storage while retaining Elasticsearch’s fast low-latency querying.

• Native in AI and relevance: Elastic AI Assistant answers natural language questions using your internal knowledge like runbooks and past incidents. Our Elastic Inference Service (EIS) provides the relevance models. Combined with ES|QL and machine learning, it helps your team pivot from what happened to why it happened and how to fix it.