Obseria vs Datadog

Feature comparison

Obseria as a Datadog alternative: the short answer

Datadog and Obseria both cover infrastructure monitoring, APM, log analytics, digital experience monitoring, security and AI-assisted operations in one SaaS product. The biggest differences are not in the feature list but in how the platforms are priced, how they store data and how tightly instrumentation is tied to the vendor. If you are evaluating a Datadog alternative, those three areas decide whether a switch is worth the effort. This page compares them as objectively as we can, based on Datadog's public pricing page and documentation as of October 2026.

Pricing model: per host and per product vs. per signal

Datadog prices each product on its own unit. According to its public pricing page, Infrastructure Monitoring and APM are billed per host per month, with allotments for containers, custom metrics and ingested and indexed spans that are pooled across the account. Log Management is split into an ingest charge per GB and an indexing charge per million log events, where the rate depends on retention. Flex Logs adds a lower-cost storage tier for long-term log data. Real User Monitoring, Synthetic Monitoring, security products and Bits AI credits use their own units, and annual commitments are discounted compared with on-demand rates.

This structure is transparent per product, and large customers can negotiate each building block. The difficulty for platform teams is forecasting: the monthly bill depends on host count, container density, custom metric cardinality, span volume and indexed log events, and each of those moves independently when clusters autoscale or a new team onboards.

Obseria uses a single usage-based model across signals and charges nothing for hosts or seats:

• Logs, spans and RUM web events: $0.060 per million ingested and $0.540 per million stored (30-day retention).
• Metric data points: $0.020 per million ingested and $0.180 per million stored (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.

Neither model is cheaper by definition. Workloads with a small number of large, stable hosts can fit host pricing well. Workloads with many short-lived containers, high log volume or many teams that need access usually benefit from pricing that follows data volume only.

Data model and storage

In Datadog, log data that should be searchable is routed into indexes, each with its own retention. Logs that are not indexed can go to archives in your own object storage and be rehydrated later, or be kept in Flex Logs for cheaper long-term querying. Traces are kept according to retention filters, while metrics live in Datadog's time series store. This gives fine-grained control, but it also means teams decide up front which data is indexed, sampled or archived.

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. You do not have to define indexes or rehydrate archives before an investigation, and field-level privacy controls govern who can see which attributes. Obseria Pipeline handles filtering, enrichment, PII masking and routing at ingest, so cost control happens before storage rather than through index design.

For querying, Obseria offers OQL, a pipe-based language that works across all signals, alongside PromQL and SQL:

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

OpenTelemetry and vendor lock-in

Datadog has invested significantly in OpenTelemetry. The Datadog Agent accepts OTLP directly, and the Datadog Distribution of the OpenTelemetry Collector (DDOT) embeds a Collector in the Agent. Many Datadog customers still use the Datadog Agent and Datadog tracing libraries, because they offer the deepest integration with Datadog features.

Obseria is OpenTelemetry-native. OTLP is the primary ingest protocol, semantic conventions are the data model, and you can export your data via OTLP at any time. Teams with existing OpenTelemetry SDKs typically see their first trace within 15 minutes. If you need more than application telemetry, the Obseria Agent is installed once per host and collects metrics, logs, traces, profiles and security signals. Obseria TopoGraph builds the topology of apps and infrastructure automatically from that data.

The practical lock-in question is where your instrumentation lives. Code instrumented with OpenTelemetry can point at any compatible backend by changing the exporter; code instrumented with a vendor-specific library needs to be re-instrumented before you can switch.

AI and root-cause analysis

Datadog offers Watchdog for automatic anomaly and outlier detection, and Bits AI as an assistant and agent layer for investigations, code fixes and chat. According to Datadog's pricing page, Bits AI usage is billed in AI credits, either as a committed monthly amount or on demand.

Obseria Intelligence combines causal, predictive and generative AI. Agentic root-cause investigations traverse the TopoGraph and the telemetry in Obseria Lake and return a chain of evidence, so engineers can verify each step instead of trusting a summary. Agent credits are $0.60 each. Customer data is never used to train AI models.

Data residency and compliance

Datadog operates several regional sites, including EU1 in Germany, and the site is chosen when the organization is created. It holds a broad set of security certifications that are listed on its trust center.

Obseria can store 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 requirements with automatic PII masking, field-level access control, audit logs and a 99.95% SLA.

Migrating from Datadog to Obseria

Most migrations run in parallel rather than as a hard cutover:

1. Dual-ship with OpenTelemetry: deploy an OpenTelemetry Collector or the Obseria Agent next to existing collection and send the same data to both platforms for a few weeks.
2. Move dashboards, monitors and SLOs: rebuild the views your on-call teams actually use first, in OQL or PromQL, and compare alerts side by side.
3. Re-instrument gradually: replace vendor-specific tracing libraries with OpenTelemetry SDKs service by service, starting with the most critical paths.

The 14-day free trial needs no credit card, which is usually enough for a proof of concept on one cluster or service group.

Which tool fits you?

Switching in three steps

1. Dual-ship telemetry. Run an OpenTelemetry Collector or the Obseria Agent in parallel and send the same data to both platforms during evaluation.
2. Rebuild critical views. Recreate the dashboards, monitors and SLOs your on-call teams rely on in OQL or PromQL and compare alert behavior.
3. Switch instrumentation. Replace vendor-specific tracing libraries with OpenTelemetry SDKs service by service, then retire the old pipeline.