Obseria vs Elastic Observability

Feature comparison

Obseria as an Elastic alternative: the short answer

Elastic Observability grew out of the ELK stack and remains one of the most widely deployed foundations for log analytics. Today it covers logs, metrics, APM, synthetics, real user monitoring and AI-assisted investigations on top of Elasticsearch. Obseria covers the same core areas with an OpenTelemetry-native platform built around an index-free lakehouse. If you are looking for an Elastic alternative, the decision usually comes down to four questions: how you want to pay, how much of the stack you want to operate yourself, how your data is stored and queried, and where it has to live. This article compares both platforms on those points, based on Elastic's public pricing pages and documentation as of October 2026.

Pricing model: per GB or per signal

Elastic offers three deployment options with different commercial models. Elastic Cloud Serverless is usage-based: according to Elastic's pricing page, you pay per GB ingested and per GB retained per month, plus egress beyond a free allowance. Observability Serverless comes in two tiers. Logs Essentials covers log analysis, dashboards, integrations and alerts; Complete adds SLOs, machine learning, AI-assisted parsing, agentic AI, workflow automation and private connectivity. Synthetic browser tests, the Elastic Managed LLM, cross-project search and workflow executions are billed as add-ons. Elastic Cloud Hosted is priced on the resources of your deployment, and self-managed subscriptions are licensed by nodes and RAM. Support levels are tied to the subscription tier.

The Serverless model is transparent and maps well to teams that think in GB of log volume. Hosted and self-managed pricing rewards teams that are good at sizing clusters, because cost follows the hardware you provision rather than the data you query.

Obseria prices per signal, not per GB, and charges nothing for hosts or seats:

• Logs, spans and RUM web events: $0.060 per million ingested and $0.540 per million stored, with 30-day retention.
• Metric data points: $0.020 per million ingested and $0.180 per million stored, with 13-month retention.
• Synthetic API check runs: $0.20 per thousand; Obseria Intelligence agent credits: $0.60 per credit.
• Data filtered in Obseria Pipeline only costs ingestion. Dashboards, alerting, SLOs, automation, API, CLI, MCP server and SSO/RBAC are included for unlimited users.

Because one model counts gigabytes and the other counts events, a fair comparison needs your own numbers: average log record size, span volume and metric cardinality. Large, verbose log lines tend to favor per-event pricing, while many very small records can favor per-GB pricing.

Data model, storage and query

Elastic stores telemetry in Elasticsearch indices organized as data streams, with index lifecycle management moving data through hot, warm, cold and frozen tiers and searchable snapshots on object storage. Mappings, ingest pipelines and the newer Streams feature control how raw data is parsed. This architecture is extremely capable for full-text search and gives experienced teams fine-grained control over performance and cost. It also means someone owns mappings, shard sizing and lifecycle policies, unless you run Serverless, where Elastic handles most of that for you.

For querying, Elastic offers ES|QL, a piped query language, alongside KQL and the Query DSL. ES|QL has made ad-hoc log analysis in Kibana considerably more approachable.

Obseria Lake is an index-free lakehouse with schema-on-read. Logs, metrics, spans, profiles, RUM events and security signals land in one store at petabyte scale, and fields are interpreted at query time, so there are no mappings or shards to plan. Field-level privacy controls govern who can see which attributes. Obseria Pipeline filters, enriches, masks PII and routes data at ingest. Queries use OQL, which works across all signals, plus PromQL and SQL:

fetch logs
| filter service == "payments"
| summarize count(), by:{level}

OpenTelemetry and portability

Elastic has embraced OpenTelemetry. Its documentation describes production-grade OTel support through the Elastic Distribution of OpenTelemetry (EDOT), which includes a Collector and language SDKs, and Elastic contributes to the upstream project. Elastic's own APM agents and Elastic Agent with Fleet remain available for teams that already use them.

Obseria is OpenTelemetry-native: OTLP is the primary ingest protocol, semantic conventions are the data model, and you can export data via OTLP at any time. Teams with existing OTel SDKs typically see their first trace within 15 minutes. The Obseria Agent, installed once per host, adds metrics, logs, traces, profiles and security signals, and Obseria TopoGraph maps apps and infrastructure automatically.

In practice, both platforms accept standard OTLP data. If your services are instrumented with upstream or EDOT SDKs, moving between backends is mostly a matter of changing exporter endpoints; dashboards, alerts and saved queries are what need real migration work.

AI and root-cause analysis

Elastic combines long-standing machine learning jobs for anomaly detection with the Elastic AI Assistant, which uses retrieval-augmented generation over your telemetry and internal knowledge. On Serverless, ML, AI-assisted parsing and agentic AI are part of the Complete tier, while the Elastic Managed LLM is billed per token.

Obseria Intelligence combines causal, predictive and generative AI. Agentic root-cause investigations traverse the TopoGraph and the data in Obseria Lake and return a chain of evidence that engineers can verify step by step. Agent credits cost $0.60 each, and customer data is never used to train AI models.

Deployment, data residency and compliance

Elastic's flexibility here is a real strength. You can run it self-managed on-premises or in any cloud, or use Elastic Cloud on AWS, Google Cloud and Azure in a large number of regions, including European ones. For organizations that must keep everything inside their own data center, self-managed Elastic is a proven option.

Obseria stores data in the EU (Frankfurt, Dublin), in the UK (London) or in your own cloud account (BYOC). It is certified to ISO 27001, 27017 and 27018, SOC 2 Type II, BSI C5 for the EU region and TISAX, and supports DORA, NIS2 and GDPR with automatic PII masking, field-level access, audit logs and a 99.95% SLA. Obseria does not offer an on-premises installation.

Migrating from Elastic to Obseria

1. Dual-ship via OpenTelemetry: add an OTLP exporter to your existing EDOT or upstream Collector and send the same data to both platforms during evaluation.
2. Translate saved searches and dashboards: start with the views on-call engineers use daily; ES|QL pipelines translate naturally into OQL pipes.
3. Retire shippers gradually: replace Beats or Elastic Agent inputs with the Obseria Agent or Collector receivers, service by service.

The 14-day free trial needs no credit card and is usually enough to compare cost and query behavior on one representative workload.

Which tool fits you?

Switching in three steps

1. Dual-ship telemetry. Point your EDOT or OpenTelemetry Collector at both backends and compare data, cost and query results side by side.
2. Rebuild key views. Recreate the saved searches, dashboards and alerts that on-call teams rely on in OQL, PromQL or SQL.
3. Switch collection. Replace Beats and Elastic Agent inputs with the Obseria Agent or Collector receivers, then retire the old cluster.