Elastic Observability is a full-stack observability platform for teams that want logs, metrics, traces, APM, infrastructure monitoring, synthetic monitoring, real user monitoring, dashboards, profiling, alerting, machine learning, and AI-assisted troubleshooting in the Elastic ecosystem. Elastic’s own documentation describes it as a unified observability platform across applications and infrastructure, combining logs, metrics, traces, user experience data, and more in one place.
Elastic is powerful, but its pricing needs careful review before teams scale usage. Buyers can choose Elastic Cloud Hosted, Elastic Observability Serverless, or self-managed Elastic. Hosted pricing is resource-based, while Observability Serverless pricing is mainly based on ingest, retention, egress, and optional add-ons.
In this guide, we break down Elastic Observability pricing, features, cost scenarios, review themes, pros, cons, and alternatives such as CubeAPM, Logz.io, Datadog, New Relic, Dynatrace, and Grafana Cloud.
What Is Elastic Observability?
Elastic Observability is Elastic’s monitoring and troubleshooting solution for applications, services, infrastructure, logs, traces, metrics, user experience data, and operational workflows. It is built on the Elasticsearch Platform and uses Kibana for searching, visualizing, alerting, and dashboarding.
In practice, Elastic helps teams answer questions such as: Which service is slow? Which log pattern explains this incident? Which Kubernetes pod, host, API, or trace is involved? Are real users seeing degraded performance? Are synthetic checks failing before customers notice?
Elastic is especially strong when search matters. Its observability docs highlight log analytics, APM, infrastructure monitoring, RUM, synthetic monitoring, uptime monitoring, LLMe;as observability, incident response, and Universal Profiling as key use cases.
Supported Languages and Frameworks
Elastic Observability is language-agnostic because teams can collect telemetry through Elastic Agent, Beats, Logstash, Elastic APM agents, OpenTelemetry collectors, and cloud integrations. Elastic’s APM documentation lists official agents for Java, Node.js, Python, Ruby, Go, .NET, and PHP.
Elastic also supports OpenTelemetry. Elastic’s documentation says the Elastic Stack natively supports OTLP, meaning trace data and metrics collected by OpenTelemetry Collectors or language SDKs can be sent to Elastic.
What Does Elastic Observability Monitor?
| Monitoring Area | Examples | Why It Matters |
| Logs | Application logs, Kubernetes logs, system logs, audit logs, cloud logs | Logs provide deep debugging and incident context. |
| Metrics | CPU, memory, latency, throughput, error rates, custom metrics | Metrics show system health and trends. |
| Traces and APM | Transactions, spans, dependencies, service maps, bottlenecks | Tracing helps teams find root cause across distributed services. |
| Infrastructure | Hosts, containers, Kubernetes, cloud resources, serverless workloads | Infrastructure monitoring connects resource pressure to app behavior. |
| Synthetics and uptime | Browser journeys, API checks, lightweight pings | Synthetic tests detect broken flows before users report them. |
| RUM | Page loads, frontend performance, user journeys | RUM connects real user experience to backend performance. |
| Profiling | Universal Profiling and code-level resource usage | Profiling helps identify CPU and performance waste. |
| AI and workflows | AI Assistant, Agent Builder, workflows, AI-assisted parsing | AI workflows can support investigation, triage, and automation. |
Key Features of Elastic Observability
Elastic’s core strength is search across large volumes of structured and unstructured data. Teams can collect logs, parse events, search with Kibana, build dashboards, and alert on patterns.
Elastic APM provides traces, service relationships, transactions, errors, latency context, and service maps. This is useful for Kubernetes, microservices, and cloud-native systems where root cause often spans many services.
Elastic supports infrastructure monitoring for servers, containers, Kubernetes, serverless workloads, and cloud environments. Elastic’s docs mention infrastructure monitoring with over 400 out-of-the-box integrations, including OpenTelemetry.
Elastic supports digital experience monitoring through RUM, synthetic monitoring, and uptime monitoring. Elastic describes RUM as a way to capture how real users interact with web applications, while synthetic monitoring simulates user journeys and API calls.
Elastic supports OpenTelemetry-native collection through OTLP. This helps teams standardize instrumentation across services instead of depending only on vendor-specific agents.
Elastic includes machine learning and AI-assisted workflows in higher tiers and serverless Complete. Observability Serverless Complete includes SLOs, AI-assisted pipelines and parsing, machine learning, workflow automation, private connectivity, and IP filtering.
Elastic supports Cloud Hosted, Serverless, and self-managed deployment paths. Elastic’s pricing page describes Hosted as resource-based, Serverless as usage-based, and self-managed as license-based pricing based on nodes and used RAM.
Elastic Observability Pricing: How It Works
Elastic Observability pricing depends on deployment model. The most important distinction is Elastic Cloud Hosted versus Elastic Observability Serverless.
| Pricing Path | How It Works | Best For |
| Elastic Cloud Hosted | Managed Elastic deployment with Standard, Gold, Platinum, and Enterprise tiers. Pricing is resource-based. | Teams that want managed Elastic with control over deployment resources. |
| Elastic Observability Serverless | Usage-based pricing for Observability projects, mainly based on ingest, retention, egress, and add-ons. | Teams that want less infrastructure management and simpler usage-based pricing. |
| Self-managed Elastic Stack | Customer runs Elastic in its own infrastructure. | Teams with internal Elastic expertise, data control needs, or custom deployment requirements. |
| Marketplace purchasing | Elastic can be purchased through cloud marketplaces. | Teams using AWS, Azure, or Google Cloud procurement. |
Elastic Cloud Hosted Pricing
Elastic Cloud Hosted has four public tiers. Elastic states that the listed starting prices are based on a cloud production configuration with 120 GB storage and two zones. These are starting prices, not guaranteed production totals for larger deployments.
| Hosted Tier | Starting Price | Best For |
| Standard | As low as $99/month | Small managed Elastic deployments |
| Gold | As low as $114/month | Teams needing more alerting and support |
| Platinum | As low as $131/month | Teams needing advanced observability features |
| Enterprise | As low as $184/month | Larger deployments needing advanced storage, AI, and support |
Elastic Observability Serverless Pricing
Elastic Observability Serverless has two main tiers: Logs Essentials and Complete. Elastic says these prices took effect November 1, 2025.
| Serverless Tier | Ingest | Retention | Egress | Best For |
| Logs Essentials | As low as $0.07/GB ingested | As low as $0.017/GB retained per month | 50 GB free, then $0.05/GB transferred | Teams focused mainly on log storage, search, dashboards, alerts, and analysis. |
| Complete | As low as $0.09/GB ingested | As low as $0.019/GB retained per month | 50 GB free, then $0.05/GB transferred | Teams needing logs, metrics, traces, synthetics, SLOs, ML, AI-assisted pipelines, workflows, private connectivity, and IP filtering. |
Serverless Add-ons and Support Charges
| Add-on or Support Item | Public Price or Rule | Important Note |
| Synthetic monitoring browser tests | $0.0123 per test run | Available only with Observability Complete projects. |
| Synthetic lightweight testing locations | $28/location/month | Available only with Observability Complete projects. |
| Elastic Managed LLM input tokens | $4.50 per million input tokens | Available only with Observability Complete projects. |
| Elastic Managed LLM output tokens | $21 per million output tokens | Available only with Observability Complete projects. |
| Cross-project search | Free during Technical Preview | Available only with Observability Complete projects. |
| Workflows | 10,000 executions free, then as low as $0.0108/execution | Workflows and Agent Builder prices are effective May 1, 2026. |
| Agent Builder | 10,000 executions free, then as low as $0.025/execution | Available only with Observability Complete projects. |
| Standard support | Included | Limited support. |
| Gold support | 5% of charge | Base support. |
| Platinum support | 10% of charge | Enhanced support. |
| Enterprise support | 15% of charge | Premium support. |
Important Billing Notes Buyers Should Not Miss
Serverless prices are listed as “as low as,” so they should be treated as floor rates rather than guaranteed costs for every buyer. Elastic also says Observability Serverless ingest and retention volumes are based on the fully enriched normalized data size at the end of the ingest pipeline, before Elasticsearch compression. That billed size can be higher than Elasticsearch index size or cloud provider proxy log volume.
Hosted starting prices are based on a 120 GB storage, two-zone cloud production configuration. Actual Hosted cost changes with cloud provider, region, instance type, storage, availability zones, autoscaling, support level, and architecture.
What Does Elastic Observability Really Cost?
⚠️ Disclaimer
The scenarios below are directional editorial estimates, not official Elastic quotes. They use public “as low as” pricing for planning only. Final pricing should be verified with Elastic’s calculator or sales team.
Pricing Assumptions Used
| Assumption | Detail |
| Month length | 30 days |
| Retention | 30 days |
| Retained data estimate | Daily ingest × 30 days |
| Egress | Excluded unless stated |
| AI tokens, workflows, Agent Builder | Excluded unless stated |
| Support | Excluded unless stated |
| Billing caveat | Elastic bills based on enriched normalized data size before Elasticsearch compression. |
Scenario Summary
| Scenario | Daily Ingest | Tier | Monthly Ingest | Retained Data | Estimated Monthly Cost |
| Small log analytics team | 100 GB/day | Logs Essentials | 3,000 GB | 3,000 GB | About $261 |
| Small full-stack team | 100 GB/day | Complete | 3,000 GB | 3,000 GB | About $327 |
| Growing SRE team with synthetics | 500 GB/day | Complete | 15,000 GB | 15,000 GB | About $3,152 |
| High-volume observability team | 2,000 GB/day | Complete | 60,000 GB | 60,000 GB | About $7,194 |
Scenario 1: Small Log Analytics Team
A team sends 100 GB/day into Logs Essentials and keeps 30 days of searchable data. Monthly ingest is 3,000 GB, and retained data is about 3,000 GB.
| Cost Item | Calculation | Estimated Monthly Cost |
| Ingest | 3,000 GB × $0.07 | $210 |
| Retention | 3,000 GB × $0.017 | $51 |
| Estimated total | Ingest + retention | $261 |
Scenario 2: Small Full-Stack Observability Team
A team sends 100 GB/day into Observability Complete and keeps 30 days of retained data. Monthly ingest is 3,000 GB, and retained data is about 3,000 GB.
| Cost Item | Calculation | Estimated Monthly Cost |
| Ingest | 3,000 GB × $0.09 | $270 |
| Retention | 3,000 GB × $0.019 | $57 |
| Estimated total | Ingest + retention | $327 |
Scenario 3: Growing SRE Team With Synthetics
A team sends 500 GB/day into Observability Complete, keeps 30 days of data, and runs 100,000 browser synthetic tests per month. This example includes Platinum support at 10%.
| Cost Item | Calculation | Estimated Monthly Cost |
| Ingest | 15,000 GB × $0.09 | $1,350 |
| Retention | 15,000 GB × $0.019 | $285 |
| Synthetic browser tests | 100,000 × $0.0123 | $1,230 |
| Subtotal | Ingest + retention + synthetics | $2,865 |
| Platinum support | 10% of subtotal | $287 |
| Estimated total | Subtotal + support | $3,152 |
Scenario 4: High-Volume Observability Team
A team sends 2 TB/day into Observability Complete and keeps 30 days of data. This example includes Platinum support at 10%.
| Cost Item | Calculation | Estimated Monthly Cost |
| Ingest | 60,000 GB × $0.09 | $5,400 |
| Retention | 60,000 GB × $0.019 | $1,140 |
| Subtotal | Ingest + retention | $6,540 |
| Platinum support | 10% of subtotal | $654 |
| Estimated total | Subtotal + support | $7,194 |
Scenario 5: Elastic Cloud Hosted Deployment
For Hosted deployments, the safest estimate should come from Elastic’s Hosted pricing calculator because Hosted pricing is resource-based. The public starting prices are useful for orientation, but real deployments can cost more depending on storage, hot/warm/cold tiers, ML nodes, availability zones, snapshots, and query load. Elastic’s Hosted page links to a calculator and detailed price list for provider, region, and instance-type estimates.
| Hosted Planning Question | Why It Matters |
| How much hot storage do we need? | Hot storage affects cluster sizing and cost. |
| Do we need ML, SLOs, service maps, or tail-based sampling? | These can require Platinum or Enterprise features. |
| How many availability zones do we need? | Multi-zone deployments improve resilience but increase resources. |
| Can older data move to cold or frozen tiers? | Tiering and searchable snapshots can reduce long-term retention cost. |
| How much Elastic expertise do we have? | Elastic still needs architecture, query tuning, lifecycle policies, and governance. |
Elastic Observability Cost Drivers
| Cost Driver | How It Affects Cost | How to Control It |
| Data ingest volume | Serverless charges per GB ingested; Hosted deployments scale with resource needs. | Filter noisy logs and avoid sending low-value debug data. |
| Retention | Serverless charges per retained GB per month; Hosted retention affects storage tiers. | Set clear hot, warm, cold, and archive policies. |
| Normalized data size | Elastic bills on enriched normalized size before compression. | Test real pipelines before committing to forecasts. |
| Synthetics | Browser tests and lightweight locations can add separate charges. | Run critical journeys often and lower-value checks less frequently. |
| Support package | Serverless support can add 5%, 10%, or 15%. | Choose support based on criticality and internal expertise. |
| AI and workflow usage | Elastic Managed LLM, Workflows, and Agent Builder have separate billing dimensions. | Track executions and token usage before broad rollout. |
| Egress | Serverless includes 50 GB free, then charges per GB transferred. | Avoid unnecessary exports and cross-environment transfers. |
| High-cardinality data | Too many labels, fields, or unique dimensions can increase storage and query pressure. | Govern metric labels, log fields, and trace attributes. |
Additional Costs and Operational Overhead
Elastic is flexible, but flexibility creates planning work. Teams should decide what to collect, parse, enrich, drop, sample, and retain before sending everything into production.
Kibana, ES|QL, KQL, dashboards, alerting, data views, lifecycle policies, and query tuning can also create a learning curve. That does not make Elastic a poor tool, but it means teams should budget time for training and operational design.
Self-managed Elastic can give teams control, but it also shifts responsibility for upgrades, scaling, snapshots, security, storage, shard planning, backups, and incident response to the customer.
Elastic Observability User Reviews in 2026
Elastic Observability has solid public review signals, but review counts change frequently across platforms. G2 lists Elastic Observability at 4.2/5 from 90 reviews and summarizes user praise around flexibility, ease of use, log management, visualization, and powerful search, while also noting learning curve and query-language challenges.
Gartner Peer Insights currently lists Elastic Observability at 4.5/5 from 291 ratings in its Observability Platforms view. SelectHub reports an 88% user satisfaction rating for Elastic Observability based on 361 reviews across four recognized review sources, but source-level counts should be treated as aggregate snapshots because review totals change.
Summary Rating Breakdown
| Platform or Signal | Rating or Signal | Notes |
| G2 | 4.2/5 from 90 reviews | G2 highlights flexibility, ease of use, log management, visualization, search, learning curve, and query-language challenges. |
| Gartner Peer Insights | 4.5/5 from 291 ratings | Gartner review counts vary by market view and date. |
| SelectHub aggregate | 88% user satisfaction from 361 reviews | Aggregates multiple review sources. |
| Software Advice | 4.3/5 from 25 reviews | Lists pricing as available upon request. |
What Users Praise
Users value bringing logs, metrics, traces, and infrastructure signals into one place. Elastic’s own product documentation supports this positioning by describing unified observability across applications and infrastructure.
G2’s review summary highlights powerful search and the ability to analyze large data volumes quickly. This is one of Elastic’s clearest strengths compared with lighter monitoring tools.
Users frequently mention dashboards and visualization as strengths. G2’s summary also highlights data visualization as a common positive theme.
Elastic’s Observability docs describe APM, infrastructure monitoring, traces, user experience data, and root-cause workflows as core use cases.
Elastic’s native OTLP support makes it attractive for teams standardizing collection through OpenTelemetry rather than relying only on vendor agents.
What Users Criticize
⚠️ Disclaimer
The following points reflect recurring public review themes and buyer considerations. They are not universal platform limitations.
G2’s pros-and-cons page says users often find Elastic Observability’s learning curve steep, especially during initial usage and advanced workflows.
G2’s review summary notes query-language challenges. This matters for teams without Elastic, Kibana, ES|QL, or KQL experience.
G2’s user feedback themes mention log management challenges, filtering issues, and difficulty for first-time users when large amounts of log data are available without good filtering.
Elastic pricing is transparent in several places, but buyers still need to model Hosted resources, Serverless ingest, retention, support, synthetics, egress, AI add-ons, and normalized billed data size. Elastic’s official billing documentation confirms that billed Observability data volumes can be higher than traditional Elasticsearch index size because billing uses enriched normalized size before compression.
Elastic Observability Alternatives: How it Compares to Competitors
Elastic Observability vs CubeAPM
Elastic Observability and CubeAPM are both full-stack observability options, but they are positioned differently. Elastic is strongest for search-powered analytics, log management, Kibana dashboards, OpenTelemetry ingest, and buyers already invested in the Elastic ecosystem. CubeAPM is stronger for teams that want OpenTelemetry-native full-stack observability, customer-controlled deployment, and simpler ingestion-based pricing. CubeAPM lists Pro pricing at $0.15/GB and includes APM, distributed tracing, logs, infrastructure monitoring, RUM, synthetics, error tracking, SLOs, dashboards, RBAC, SSO, MFA, and audit logs.
| Category | Elastic Observability | CubeAPM |
| Primary strength | Search-powered observability built around Elasticsearch and Kibana | OpenTelemetry-native full-stack observability and APM |
| Pricing model | Hosted resource pricing or Serverless ingest, retention, egress, and add-ons | Public Pro pricing at $0.15/GB |
| Deployment | Hosted, Serverless, and self-managed | Customer-controlled/self-hosted deployment with vendor-managed experience |
| Best for | Teams needing search, log analytics, Kibana, Elastic ecosystem features | Teams wanting predictable pricing, full-stack telemetry correlation, and data control |
| Tradeoff | Powerful but architecture and pricing can be complex | Simpler pricing, but not a replacement for every Elasticsearch, SIEM, or Kibana use case |
Elastic Observability vs Datadog
Datadog is a polished SaaS observability platform with strong infrastructure monitoring, APM, logs, RUM, synthetics, cloud monitoring, and security modules. Elastic may be stronger for search-driven analytics, Elasticsearch-based workflows, and deployment flexibility. Datadog can be easier for some teams to adopt, but costs can stack across modules.
| Category | Elastic Observability | Datadog |
|---|---|---|
| Core strength | Search-driven logs, traces, metrics, and Elastic ecosystem workflows | Polished SaaS observability across many modules |
| Pricing model | Hosted resources or Serverless ingest, retention, and add-ons | Modular SaaS pricing across infrastructure, APM, logs, RUM, synthetics, and more |
| Deployment | Hosted, Serverless, and self-managed | SaaS-first |
| Best for | Teams needing Elasticsearch, Kibana, and flexible deployment | Teams wanting broad plug-and-play SaaS monitoring |
| Tradeoff | More setup and governance may be needed | Costs can stack across multiple products |
Elastic Observability vs New Relic
New Relic is developer-friendly and strong for APM, digital experience monitoring, and SaaS full-stack observability. Elastic may be better for teams that prioritize log search, Elasticsearch, open standards, flexible deployment, and Kibana-based analysis.
| Category | Elastic Observability | New Relic |
|---|---|---|
| Core strength | Log search, Elastic analytics, and flexible observability | Developer-friendly SaaS APM and full-stack monitoring |
| Pricing model | Hosted or Serverless usage-based pricing | Usage-based ingest plus user-based pricing |
| Deployment | Hosted, Serverless, and self-managed | SaaS-first |
| Best for | Teams prioritizing search, Kibana, and open telemetry pipelines | Teams wanting quick APM, dashboards, and digital experience monitoring |
| Tradeoff | Can require more Elastic expertise | User-based pricing can matter for larger teams |
Elastic Observability vs Dynatrace
Dynatrace is strong for automated enterprise observability, dependency mapping, and AI-assisted root-cause workflows. Elastic is more search-centered and flexible, but may require more design and governance from the customer.
| Category | Elastic Observability | Dynatrace |
|---|---|---|
| Core strength | Search-powered observability and log analytics | Automated enterprise observability and AI-assisted root cause |
| Pricing model | Hosted resources or Serverless ingest, retention, and add-ons | Consumption-based enterprise pricing |
| Deployment | Hosted, Serverless, and self-managed | SaaS and managed enterprise options |
| Best for | Teams needing Elastic search, Kibana, and telemetry flexibility | Large teams wanting automation, dependency mapping, and enterprise governance |
| Tradeoff | More manual architecture and data design may be required | Can be complex and costly at enterprise scale |
Elastic Observability vs Grafana Cloud
Grafana Cloud is attractive for teams standardized on Grafana dashboards, Prometheus, Loki, Tempo, and Pyroscope. Elastic is a better fit when Elasticsearch search, Kibana, and large-scale log analytics are central.
| Category | Elastic Observability | Grafana Cloud |
|---|---|---|
| Core strength | Elasticsearch search, Kibana, logs, APM, and analytics | Grafana dashboards with Prometheus, Loki, Tempo, and Pyroscope |
| Pricing model | Hosted resources or Serverless ingest and retention | Usage-based pricing across metrics, logs, traces, profiles, and synthetics |
| Deployment | Hosted, Serverless, and self-managed | SaaS, with open-source components available separately |
| Best for | Teams centered on Elastic search and Kibana workflows | Teams standardized on Grafana and Prometheus-style observability |
| Tradeoff | Better for Elastic-native analytics | May require stitching together multiple open-source telemetry components |
Is Elastic Observability the Right Choice?
When Elastic Observability Works Best
Elastic Observability is a strong fit when:
- Your team already uses Elasticsearch, Kibana, Logstash, Beats, or Elastic Agent.
- Log search and analysis are central to troubleshooting.
- You want OpenTelemetry support and broad integration coverage.
- You need logs, metrics, traces, APM, RUM, synthetics, uptime, profiling, and infrastructure monitoring in one ecosystem.
- You value deployment flexibility across Hosted, Serverless, and self-managed models.
- You have engineering resources to manage telemetry pipelines, dashboards, alerts, and retention policies.
- You want advanced ML, SLOs, service maps, tail-based sampling, profiling, or AI-assisted observability.
When Elastic Observability May Not Be the Right Fit
Elastic Observability may not be ideal when:
- You want a very simple flat monthly price with minimal forecasting.
- Your team does not want to learn Elasticsearch, Kibana, ES|QL, KQL, or observability data modeling.
- You need a plug-and-play monitoring tool with very little configuration.
- You have low telemetry volume and only need basic uptime or cron monitoring.
- You want all advanced features without tier or add-on decisions.
- You need predictable ingestion-based pricing more than search-centered analytics.
Practical Buying Advice
- Choose the deployment model first: Hosted, Serverless, or self-managed.
- Estimate daily ingest after enrichment, not only raw source logs.
- Model retention as a separate line item.
- Separate logs-only workflows from full-stack observability workflows.
- Test OpenTelemetry, Elastic Agent, and Logstash pipelines during proof of concept.
- Model synthetics, egress, support, AI tokens, Workflows, and Agent Builder separately.
- Decide whether Standard, Gold, Platinum, or Enterprise support is needed.
- Set lifecycle policies before production usage grows.
- Compare Elastic against CubeAPM, Datadog, New Relic, Grafana Cloud, Dynatrace, Splunk, OpenSearch, and Logz.io using the same workload assumptions.
- Use Elastic’s official calculator or quote process before procurement.
Conclusion
Elastic Observability is a strong option for teams that want search-powered observability across logs, metrics, traces, APM, infrastructure, synthetics, RUM, profiling, dashboards, alerts, machine learning, and AI-assisted workflows. It is especially compelling for organizations already invested in Elasticsearch and Kibana.
The main buying challenge is cost modeling. Elastic now publishes clear Serverless pricing for ingest, retention, egress, synthetics, Elastic Managed LLM, Workflows, Agent Builder, and support. Elastic Cloud Hosted also publishes starting tier prices. Still, real cost depends on data volume, retained data, architecture, support level, synthetic tests, AI usage, region, and internal engineering effort.
For teams that value Elastic’s search and analytics strengths, Elastic Observability is worth serious evaluation. For buyers that want simpler ingestion-based pricing and customer-controlled full-stack observability, CubeAPM and other alternatives should also be compared using the same telemetry and retention assumptions.
Disclaimer: This is an independent editorial review based on publicly available Elastic pricing pages, Elastic documentation, public review-platform data, and the uploaded draft material. Pricing, packaging, review counts, and feature availability can change. Buyers should verify current terms directly with Elastic before making purchasing decisions.
FAQs
1. What is Elastic Observability?
Elastic Observability is Elastic’s platform for monitoring logs, metrics, traces, APM, infrastructure, RUM, synthetics, uptime, profiling, dashboards, alerts, ML, and AI-assisted troubleshooting.
2. How much does Elastic Observability cost?
Elastic Cloud Hosted starts as low as $99/month for Standard, $114/month for Gold, $131/month for Platinum, and $184/month for Enterprise, based on a 120 GB storage, two-zone cloud production configuration. Elastic Observability Serverless starts as low as $0.07/GB ingested for Logs Essentials and $0.09/GB ingested for Complete, with separate retention, egress, support, and add-on charges.
3. What is Elastic Observability Serverless?
Elastic Observability Serverless is a fully managed Elastic Observability option where pricing is mainly based on data ingested, data retained, egress, and optional add-ons. It has Logs Essentials and Complete tiers.
4. What is the difference between Logs Essentials and Complete?
Logs Essentials focuses on log storage, search, dashboards, alerts, and analysis. Complete adds full-stack observability features such as metrics, traces, synthetic tests, SLOs, AI-assisted pipelines, machine learning, workflows, private connectivity, and IP filtering.
5. Does Elastic charge for retention?
Yes. In Observability Serverless, retention is charged per GB retained per month. Logs Essentials is listed as low as $0.017/GB retained per month, while Complete is listed as low as $0.019/GB retained per month.
6. Is Elastic Observability good for Kubernetes?
Yes, Elastic supports Kubernetes and cloud-native monitoring workflows through logs, metrics, traces, APM, infrastructure monitoring, and OpenTelemetry. Buyers should still test data volume, dashboards, cardinality, and retention during proof of concept.
7. What are the best Elastic Observability alternatives?
Common alternatives include CubeAPM, Datadog, New Relic, Dynatrace, Grafana Cloud, Splunk, OpenSearch, Logz.io, Sumo Logic, and Azure Monitor.
8. How does Elastic compare with CubeAPM?
Elastic is stronger for Elasticsearch-based search, Kibana dashboards, and Elastic ecosystem workflows. CubeAPM is stronger when buyers want OpenTelemetry-native full-stack observability, customer-controlled deployment, and simpler ingestion-based pricing. CubeAPM lists Pro pricing at $0.15/GB.