# Grafana Cloud vs Elastic Observability: A Complete Comparison for 2026
In 2021, both of these companies moved their flagship projects off the Apache 2.0 license within a few months of each other. Elastic switched Elasticsearch and Kibana to the Server Side Public License and its own Elastic License, a response to cloud providers selling hosted versions of its code. Grafana Labs relicensed Grafana, Loki, and Tempo under the AGPL. In 2024 Elastic added the AGPL back as an option, so today both stacks can be run under the same open-source license.
The starting points were never the same, though. Elastic began as a search engine and grew observability around it. Grafana began as a dashboard and grew observability backends behind it. That difference still shapes almost every part of the two platforms, from how they store logs to what their query languages are good at, and it is the most useful lens for this comparison.
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The results look like this. **Elastic Observability is a search-first platform where logs, metrics, and traces all live in Elasticsearch**, queried with ES|QL, with security analytics and SIEM on the same data. Grafana built outward from visualization. **Grafana Cloud is a dashboard-first platform that grew a separate open-source backend for each signal**, with on-call, synthetics, and AI around it.
This comparison covers architecture and licensing, collection, logs, APM, metrics and infrastructure, querying and dashboards, digital experience, AI, security, alerting and incidents, pricing, and deployment.
## Quick comparison
These are the rows most teams check first. Many of them trace back to search-first versus dashboard-first design.
| Category | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Origin** | Dashboarding | Search |
| **Storage** | Mimir, Loki, Tempo, Pyroscope | Elasticsearch for every signal |
| **Open-source license** | AGPL | AGPL, SSPL, or Elastic License |
| **Collection** | Grafana Alloy, OpenTelemetry | Elastic Agent, Elastic Distributions of OpenTelemetry |
| **Query languages** | PromQL, LogQL, TraceQL | ES\|QL, KQL, Lucene |
| **Full-text log search** | Label filtering, then scan | ✔, core strength |
| **APM** | ✔, Tempo and Application Observability | ✔ |
| **Continuous profiling** | ✔, Pyroscope | ✔, Universal Profiling |
| **Dashboards** | ✔, industry-leading | ✔, Kibana |
| **Security and SIEM** | ✘ | ✔, Elastic Security |
| **On-call and incidents** | ✔, Grafana IRM | Alerting with connectors |
| **AI** | Grafana Assistant and Sift | AI Assistant, Agent Builder, Streams |
| **Deployment** | Cloud or self-hosted | Serverless, Hosted, or self-managed |
| **Pricing unit** | Series, GB, and users | GB ingested and retained, or resources |
| **Users** | $8 per active user beyond 3 | Not charged per user |
## Architecture and licensing
Grafana Cloud keeps each signal apart on purpose, giving metrics, logs, traces, and profiles their own purpose-built store and their own query language. Grafana dashboards sit across all of them and can also read dozens of outside data sources, which is why Grafana is often the visualization layer for stacks it does not own.
Elastic stores everything in Elasticsearch. Logs, metrics, traces, profiles, and security events share one engine, one set of indices, and one query surface in Kibana, so correlating across signals happens inside a single system. Elastic's storage tiers, from hot to frozen with searchable snapshots on object storage, let you keep large volumes searchable for long periods without keeping them all on fast disks.
| Architecture | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Signal storage** | Four specialized backends | One search engine |
| **Cross-signal correlation** | Labels and trace IDs | Same engine and indices |
| **Long-term retention** | Paid retention per signal | Frozen tier with searchable snapshots |
| **Outside data sources** | Dozens, natively | Mainly Elastic data |
| **Self-hosting** | ✔, AGPL | ✔, AGPL or Elastic License |
## Collection
Both have moved toward OpenTelemetry, from different starting points.
Grafana Alloy is an OpenTelemetry-compatible collector that also speaks Prometheus, so teams can scrape existing exporters and receive OTLP in one agent. For code you cannot touch, Beyla instruments services from the kernel with eBPF, and prebuilt integrations handle common infrastructure.
Elastic Agent, managed centrally through Fleet, collects logs, metrics, and security data with hundreds of integrations. Elastic also ships its own Elastic Distributions of OpenTelemetry, so teams can standardize on OpenTelemetry SDKs and collectors while sending data to Elastic.
| Collection | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Main collector** | Grafana Alloy | Elastic Agent with Fleet |
| **OpenTelemetry** | ✔, native | ✔, Elastic Distributions of OpenTelemetry |
| **Prometheus scraping** | ✔, native | ✔, via integration |
| **eBPF instrumentation** | ✔, Beyla | ✔, Universal Profiling |
| **Central fleet management** | Fleet Management in Grafana Cloud | ✔, Fleet |
[summary]
### Collect every signal without a pipeline project
Grafana asks you to configure Alloy for each signal, and Elastic asks you to manage agents and policies through Fleet. Better Stack's eBPF-based collector finds your services, gathers their logs, metrics, and traces with no code changes, and ships them to one warehouse that also runs on-call and incidents.
**Instrumentation is only useful once it is actually deployed everywhere.** [See the Better Stack collector](https://betterstack.com).
[/summary]
## Logs
Logs are where the search-first origin shows most clearly.
Elastic indexes log content, so full-text search across huge volumes is fast, and Discover, ES|QL, and log pattern and rate analysis make exploring unfamiliar logs easier than in most tools. The logsdb index mode compresses log data substantially, and Elastic says it cuts storage by up to 65%. Streams, Elastic's agentic log processing, parses and structures incoming logs automatically. The serverless billing model counts ingested volume after enrichment, so billed gigabytes can exceed raw source size.
Loki takes the opposite approach. It indexes labels only and stores log content compressed in object storage, so ingesting large volumes is cheap, but searching for a string inside log bodies over a long range depends on labels narrowing the search first. Grafana's Adaptive Logs helps find and drop noisy patterns.
| Logs | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Indexing** | Labels only | Full content |
| **Full-text search at scale** | Slower without good labels | ✔, core strength |
| **Pattern and rate analysis** | Via Grafana Assistant | ✔, built in |
| **Automatic parsing** | Alloy processing | ✔, Streams |
| **Long-term searchable storage** | Extra retention | Frozen tier on object storage |
| **Price per GB ingested** | About $0.45 | From $0.09 on Serverless Complete |
## APM and tracing
Both offer full APM, with different strengths.
Elastic APM covers service maps, transaction breakdowns, dependency views, and error tracking, with trace data stored in Elasticsearch so you can search spans as freely as logs. Universal Profiling adds always-on, eBPF-based profiling across the fleet without instrumentation.
Grafana keeps traces in Tempo on object storage, which makes high trace volumes cheap to retain, and TraceQL can query by span structure as well as attributes. Application Observability builds service dashboards from OpenTelemetry, and Pyroscope provides continuous profiling as a separate signal.
For a view of Elastic's APM against a more automated enterprise platform, our [Dynatrace vs Elastic comparison](https://betterstack.com/community/comparisons/dynatrace-vs-elastic/) covers that matchup.
| APM | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Service maps** | ✔ | ✔ |
| **Trace storage cost profile** | Low, object storage | Elasticsearch tiers |
| **Trace query** | TraceQL | ES\|QL and KQL |
| **Continuous profiling** | ✔, Pyroscope | ✔, Universal Profiling |
| **Error tracking** | Via traces and Frontend Observability | ✔, APM errors |
## Metrics and infrastructure
Grafana's metrics heritage is the stronger one here.
Grafana's Mimir takes Prometheus remote write as is, stores Pro metrics for 13 months, and underpins both Kubernetes Monitoring and the integration catalog. PromQL is the standard most infrastructure teams already know. The cost to manage is active series at $6.50 per 1,000.
Elastic handles metrics through Elasticsearch with Kubernetes, host, and cloud integrations, and its time series data streams are optimized for metric storage. Elastic changed its serverless metrics pricing on July 1, 2026 to $0.023 per GB ingested and $0.005 per GB retained, which makes metrics much cheaper than logs on its platform. PromQL support exists, but ES|QL is the primary language, so Prometheus-native teams face more adjustment.
| Metrics | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Prometheus compatibility** | ✔, native | ✔, supported |
| **Primary query language** | PromQL | ES\|QL |
| **Kubernetes monitoring** | ✔, extensive | ✔ |
| **Pricing unit** | Active series | GB ingested and retained |
| **Serverless metrics price** | $6.50 per 1,000 series | $0.023 per GB ingested |
[summary]
### PromQL charts on volume-based pricing
Grafana keeps PromQL but prices by series, and Elastic prices by volume but centers on ES|QL. Better Stack ingests Prometheus metrics, lets you chart them in PromQL or SQL, and bills them by volume beside the logs and traces in the same warehouse.
**Your team should keep the query language it knows and lose the series meter it does not need.** [See Better Stack metrics](https://betterstack.com/infrastructure-monitoring).
[/summary]
## Querying and dashboards
Grafana's dashboards set the standard. Its panels, variables, transformations, and ability to mix many data sources on one screen are hard to match, and a large community library covers most integrations. Our [Grafana vs Kibana comparison](https://betterstack.com/community/comparisons/grafana-vs-kibana/) looks at the two visualization layers in more depth.
Kibana is stronger for exploration. Discover, ES|QL, and Lens make it easy to slice raw events, pivot from a log to its trace, and build visualizations from search results. ES|QL is a piped query language that works across all of Elastic's data and supports joins, enrichment, and aggregation in one statement, which reduces the need to learn several languages.
| Querying and dashboards | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Dashboard flexibility** | Extensive | Good, via Kibana Lens |
| **Ad hoc exploration of raw events** | Explore, per signal | ✔, Discover and ES\|QL |
| **One query language across signals** | ✘ | ✔, ES\|QL |
| **Outside data sources** | Dozens | Limited |
[summary]
### SQL across every signal
Grafana splits querying across three languages and Elastic centers on ES|QL. Better Stack uses SQL for logs, metrics, and traces alike, a language most engineers already know, and chart the results on the same dashboards that track your incidents.
**The best query language is the one nobody has to be trained on during an outage.** [Build charts with SQL](https://betterstack.com/dashboards).
[/summary]
## Digital experience
Elastic covers real user monitoring and synthetic checks, with synthetics priced per browser test run and per location for lightweight checks. Session replay is not part of the observability product.
Grafana's Frontend Observability measures Core Web Vitals, records browser errors and sessions, and follows page requests into Tempo, and its synthetics and k6 products add availability probes and load testing.
| Digital experience | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Real user monitoring** | ✔ | ✔ |
| **Synthetic checks** | ✔ | ✔ |
| **Load testing** | ✔, k6 | ✘ |
| **Session replay** | Check current plan | ✘ |
## AI and MCP
Both have built AI into investigation.
Elastic's AI Assistant answers questions about your data in natural language, writes ES|QL, and explains log patterns and errors. Agent Builder lets teams create custom agents grounded in their Elasticsearch data and expose them to outside tools, and Streams applies AI to log parsing. Elastic's machine learning jobs add anomaly detection across metrics and logs.
Grafana answers with a conversational Assistant that drafts queries and dashboards and digs into problems when asked, plus Sift, which fires off automated diagnostics the moment an incident is declared. Outside AI clients plug in through an open-source Grafana MCP server.
| AI and MCP | Grafana Cloud | Elastic Observability |
|---|---|---|
| **AI assistant** | ✔, Grafana Assistant | ✔, AI Assistant |
| **Automatic incident checks** | ✔, Sift | ML anomaly detection |
| **Custom agents** | ✘ | ✔, Agent Builder |
| **AI log parsing** | ✘ | ✔, Streams |
| **MCP access** | ✔, open-source server | Via Agent Builder |
## Security
This is a clear Elastic advantage. Elastic Security runs on the same Elasticsearch data as observability, with SIEM, endpoint protection, and extended detection and response, so security and operations teams can share one platform and one set of logs. Elastic was named a Leader in IDC's 2026 MarketScape for SIEM. If security is a deciding factor, our [Splunk vs Elastic Stack comparison](https://betterstack.com/community/comparisons/splunk-vs-elastic-stack-elk/) looks at Elastic against the other major SIEM option.
Grafana Cloud does not offer a SIEM or security product.
## Alerting and incidents
Elastic alerts on any query, metric, SLO, or anomaly and sends notifications through connectors to Slack, PagerDuty, ServiceNow, and others. It does not include on-call rotations or incident management.
Grafana pairs its alerting with IRM for rotations, escalations, call and text paging, and incidents launched from a panel; three active users cost nothing and each additional one is $20.
| Alerting and incidents | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Alert rules and SLOs** | ✔ | ✔ |
| **Anomaly-based alerts** | Via Sift | ✔, ML jobs |
| **On-call rotations** | ✔, Grafana IRM | Via connectors |
| **Phone and SMS paging** | ✔ | Via connectors |
| **Incident management** | ✔ | Via connectors |
| **Status pages** | ✘ | ✘ |
[summary]
### On-call that comes with the observability
Elastic relies on another vendor for paging, and Grafana stops at the pager with no status pages. Better Stack folds paging, Slack or Teams incident channels, and status pages into one product, with unlimited calls and texts included in each $29 responder seat and the logs and traces right beside the alert.
**When the alert fires, the right person should be paged from the same place the data lives.** [Explore Better Stack on-call](https://betterstack.com/incident-management).
[/summary]
## Pricing
Each charges by usage, measured in very different units.
### Grafana Cloud
Grafana Cloud Pro charges a $19 base each month and then meters usage: $6.50 for every 1,000 series past the first 10,000, about $0.45 per GB of logs or traces past 50 GB each, $8 per user past three, and $20 per IRM user past three.
### Elastic Observability
Elastic Cloud Serverless offers two observability tiers. Logs Essentials starts at $0.07 per GB ingested and $0.017 per GB retained each month. Complete, which adds APM, profiling, synthetics, and full observability features, starts at $0.09 per GB ingested for logs and traces and $0.023 for metrics, with retention from $0.019 and $0.005 per GB per month. Egress is free up to 50 GB, then $0.05 per GB, and support is billed separately. These are "as low as" rates, so smaller volumes cost more per GB, and billed volume is measured after enrichment. Elastic Cloud Hosted is priced by provisioned resources instead, and self-managed licenses are quoted. Users are not charged separately.
### A 15-engineer example
Model a team of 15 engineers that keeps 30 days of data and sends, each month, 300 GB of logs, 100 GB of traces, and metrics from 50,000 active series, about 100 GB at a one-minute scrape.
| Monthly cost | Grafana Cloud Pro | Elastic Serverless Complete |
|---|---|---|
| **Platform fee** | $19 | None |
| **Metrics** | 40,000 billable series, about $260 | About 100 GB, a few dollars at the floor rate |
| **Logs and traces** | 300 GB billable, about $135 | 400 GB, about $45 at the floor rate |
| **Users** | 12 beyond 3, $96 | Not charged per user |
| **On-call** | IRM, $20 per user beyond 3 | Separate tool |
| **Approximate total** | About $510 | Under $100 at floor rates, before support |
Elastic's floor rates come out far lower, but this is the scenario where "as low as" matters most. At modest volumes, real per-GB rates sit above the floor, enrichment raises billed gigabytes, and support adds a percentage on top. Model your own volumes in Elastic's calculator before relying on these numbers. For another full-platform comparison of Elastic's costs, see our [New Relic vs Elastic Observability comparison](https://betterstack.com/community/comparisons/new-relic-vs-elastic-observability/).
## Deployment and compliance
Both offer more deployment choices than most observability vendors. Grafana Cloud brings SOC 2 Type II and GDPR, regulated-industry options in FedRAMP and PCI DSS, and the fallback of running the open-source stack on your own hardware. Elastic offers Serverless, Elastic Cloud Hosted across AWS, Azure, and Google Cloud, and self-managed deployments including air-gapped installations, with a broad compliance portfolio. Have procurement verify the latest attestations with both vendors.
| Deployment | Grafana Cloud | Elastic Observability |
|---|---|---|
| **Managed SaaS** | ✔ | ✔, Serverless and Hosted |
| **Self-managed** | ✔, open-source stack | ✔ |
| **Air-gapped** | Via self-hosting | ✔ |
| **Cloud marketplaces** | ✔ | ✔ |
| **SOC 2 Type II** | ✔ | ✔ |
## Which one fits your team
Grafana Cloud is the better match when your teams think in Prometheus and dashboards, your data comes from many sources you want on one screen, and you want on-call in the same platform. Kubernetes-heavy platform and SRE teams, organizations that already use Grafana as their visualization layer, and anyone who wants per-signal control will get the most from it.
Elastic Observability is the better match when search over large volumes of logs is central to how you investigate, you want security and observability on one platform, or you need serverless, hosted, and air-gapped options from one vendor. It suits log-heavy operations, teams that already run Elasticsearch, and organizations consolidating SIEM and observability. Plan for a separate pager and time to learn ES|QL.
## Final thoughts
Both stacks came out of the same licensing fights with the same open-source license on the other side. **Elastic is still a search engine at heart**, and Grafana is still a dashboard with backends built behind it.
So think about how your team finds problems. If they start by searching for a string across billions of log lines, Elastic was built for that moment. **If they start by looking at a panel that turned red, Grafana is the better place to start**, and you can add the search later.
[summary]
### One MCP endpoint for telemetry and incidents
Elastic exposes its data to outside agents through Agent Builder, and Grafana's MCP server spans several backends, but neither connects that access to on-call and incidents. Better Stack's MCP server covers logs, metrics, traces, on-call, and incidents together, so Claude or Cursor can query logs with SQL, check who is on call, acknowledge an incident, and build a chart in one conversation.
**With everything behind one MCP endpoint, your assistant can investigate and respond without switching tools.** [Try Better Stack](https://betterstack.com).
[/summary]