# Grafana Cloud vs Coralogix: An Observability comparison for 2026
Before you sign an observability contract, it is worth pricing the exit. Grafana Cloud and Coralogix both say they protect you from lock-in, and both are telling the truth, but they protect different things, and the difference shapes everything from your log bill to what your team has to rewrite if you ever leave.
**Grafana Cloud is a hosted version of Grafana Labs' open-source stack**: Mimir for metrics, Loki for logs, Tempo for traces, and Pyroscope for profiles, all under Grafana dashboards. Because each piece is open source, you can walk away and run the same software yourself, keeping your PromQL and LogQL queries and your dashboards intact. The stored telemetry is the part that stays behind, and moving it takes an export or a long overlap.
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Coralogix guards the data instead. **Coralogix is an in-stream observability platform** whose Streama engine parses, enriches, and alerts on telemetry as it arrives, before anything is indexed, then writes long-term data to an S3 or GCS bucket in your own cloud account as Parquet. Years of logs stay in your account whatever happens to the contract. The cost of leaving shows up elsewhere: every query and dashboard written in DataPrime, Coralogix's proprietary language, has to be rebuilt.
## Quick comparison
The rows that tend to settle this decision are log cost control, on-call, SIEM, and what is open.
| Category | Grafana Cloud | Coralogix |
|---|---|---|
| **Architecture** | Separate backends: Mimir, Loki, Tempo, Pyroscope | Streama in-stream processing, then tiered storage |
| **What is open** | The software, open-source projects | The data, stored in your bucket as Parquet |
| **Self-hosting** | ✔, open-source stack | ✘, SaaS with customer-owned storage |
| **Alerting timing** | After ingestion into each backend | In-stream, before indexing |
| **Query languages** | PromQL, LogQL, TraceQL | DataPrime, Lucene, PromQL |
| **Log cost control** | Adaptive Logs, label design | TCO Optimizer pipelines |
| **APM** | ✔, Tempo and Application Observability | ✔, service catalog, profiling, SLOs |
| **Dashboards** | ✔, industry benchmark | ✔, Custom Dashboards |
| **Session replay** | Public preview, waitlist | ✔, web, iOS, Android |
| **Security and SIEM** | ✘ | ✔ |
| **On-call and incidents** | ✔, Grafana IRM | ✘ |
| **AI** | Grafana Assistant and Sift | Olly, AI Center, MCP server |
| **Users** | $8 per active user beyond 3 | Unlimited |
| **Pricing unit** | Series, GB, and users | GB by data type, expressed in units |
| **Billing commitment** | Pay as you go on Pro | Units bought per term, unused units expire |
## Where your data lives
When you send data to Grafana Cloud, Alloy or any OpenTelemetry collector fans it out by type: metrics to Mimir, logs to Loki, traces to Tempo, profiles to Pyroscope. Explore and dashboards query each store in its own language and join the results through labels and trace IDs. On Pro, all four stores run in Grafana's cloud. Enterprise customers can instead choose Bring Your Own Cloud, where Grafana operates the stack inside a cloud account you own, which closes much of the data-ownership gap with Coralogix at an enterprise price.
Coralogix does its work while the data is still in motion. Streama evaluates alert rules on the stream, so a rule can fire before the event is stored anywhere. You then choose a pipeline per kind of data: Frequent Search for data you query often, Monitoring for data that only feeds dashboards and alerts, and Compliance for data that goes straight to your bucket. Coralogix compresses logs and traces about fivefold before archiving them, and the archive stays queryable from the Coralogix UI without a rehydration job.
| Architecture | Grafana Cloud | Coralogix |
|---|---|---|
| **Processing model** | Ingest, store, then query | Process in-stream, then route to tiers |
| **Storage location** | Grafana's cloud, or your account via Enterprise BYOC | Coralogix hot tiers plus your own bucket |
| **Long-term retention** | $0.10 per GB for each extra 30 days | Your bucket, compressed about 5x, at your storage cost |
| **Exit path** | Run the same open-source stack | Data already in your account |
## What you will pay
The two vendors meter almost nothing the same way, so the list prices only make sense next to a real workload.
### How each vendor charges
Grafana Cloud's free tier gives you 10,000 metric series, 50 GB apiece of logs, traces, and profiles, three users, and 14-day retention. Pro keeps those allowances for a $19 monthly platform fee and stretches retention to 13 months for metrics and 30 days for the rest. Past the allowances, metrics cost $6.50 per 1,000 billable series, while logs, traces, and profiles cost $0.05 per GB to process plus $0.40 per GB to write. Users are metered three separate ways: $8 per dashboard user, $20 per IRM user, and $20 per Grafana Assistant user beyond the three included in each.
Coralogix publishes blended rates of $0.42 per GB for logs, $0.16 per GB for traces, and $0.05 per GB for metrics, with AI telemetry at $1.50 per million tokens. Spend is counted in units worth $1.50 each, and the pipeline decides what a unit buys: about 1.3 GB of Frequent Search logs, 3 GB of Monitoring logs, or 30 GB of metrics. Users and hosts are unlimited. Units are bought for a contract term and anything unused expires at the end of it, so overestimating your volume costs money as well.
### A 15-engineer example
Take a team of 15 engineers sending 300 GB of logs and 100 GB of traces a month, plus 50,000 active metric series scraped every 60 seconds. All 15 use dashboards and take on-call shifts.
| Monthly cost | Grafana Cloud Pro | Coralogix |
|---|---|---|
| **Platform fee** | $19 | None |
| **Metrics** | 40,000 billable series after the free 10,000, $260 | About 100 GB at $0.05, $5 |
| **Logs** | 250 GB after the free 50 GB at $0.45, about $113 | 300 GB at $0.42 blended, $126 |
| **Traces** | 50 GB after the free 50 GB at $0.45, about $23 | 100 GB at $0.16, $16 |
| **Users** | 12 beyond 3 at $8, $96 | Unlimited, $0 |
| **Observability subtotal** | About $510 | About $147 |
| **On-call for 15 responders** | IRM, 12 beyond 3 at $20, $240 | Separate tool, priced by that vendor |
| **Total with on-call** | About $750 | About $147 plus a paging tool |
### What moves the numbers
The metric line for Coralogix rests on an assumption worth showing. Fifty thousand series sampled once a minute produce about 2.16 billion samples a month, and 100 GB assumes roughly 46 bytes per sample as Coralogix measures it. Halve that and metrics cost about $2.70; double it and they cost about $10.80. The answer barely moves, because Coralogix's metric rate is low enough that the assumption hardly matters.
The assumptions that do matter sit elsewhere. Coralogix's $0.42 blended log rate presumes most logs go to cheaper pipelines. Send all 300 GB to Frequent Search at $1.15 per GB and logs alone cost about $345, lifting the Coralogix subtotal to around $366. On the Grafana side, metrics are billed on whichever is higher, active series or data points per minute, so scraping every 15 seconds instead of every 60 turns the same 50,000 series into 200,000 billable ones and the metric line into roughly $1,235. Turning on Grafana Assistant for the whole team adds another $240. Run your own split through both calculators, and check your scrape interval before you migrate.
[summary]
### Route data before it reaches the bill
Coralogix prices each log by the pipeline you pick, and Grafana prices metrics by series and data points per minute. Better Stack works with the pipelines you already run, including Vector and OpenTelemetry, to filter and shape data before ingestion, then stores everything in one warehouse with an optional bucket of your own for long-term retention.
**Cost control works best before the data lands, not after the invoice arrives.** [Explore Better Stack](https://betterstack.com).
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## Logs and pipeline routing
Loki indexes labels, not log text. A query first narrows the search to streams that match labels such as `app` or `namespace`, then scans the compressed chunks in object storage for whatever you are filtering on. With good labels that is fast and cheap. Hunting for one order ID across 30 days of a busy service is where it slows down. Adaptive Logs reports which log patterns nobody queries and lets you drop or sample them before they are billed.
Coralogix makes you decide what each log is for before it arrives. The TCO Optimizer assigns each application and severity to Frequent Search at about $1.15 per GB, Monitoring at about $0.50 per GB, Compliance, or Blocked, and Coralogix advertises savings of up to 70% for teams that route aggressively. Logs in Frequent Search are fully searchable, and DataPrime copes well with nested JSON and drifting schemas. Logs routed only to Monitoring still drive alerts and dashboards but cannot be searched freely afterward, which is a fine trade until an incident needs exactly those lines.
| Logs | Grafana Cloud | Coralogix |
|---|---|---|
| **Indexing** | Labels only | Depends on pipeline |
| **Search on any field** | Scan after label filtering | ✔, in Frequent Search |
| **Cost lever** | Adaptive Logs, labels, retention | Pipeline routing via TCO Optimizer |
| **Long-term storage** | Extra retention in Grafana Cloud | Your bucket, queryable |
| **Published log rate** | $0.45 per GB: $0.05 process, $0.40 write | $1.15 Frequent Search, $0.50 Monitoring, $0.42 blended |
| **Risk to watch** | Slow body searches across long ranges | Data routed where you cannot search it |
[summary]
### Every log searchable, without picking a tier
Loki makes body searches slow without good labels, and Coralogix makes some data unsearchable once it is routed to monitoring. Better Stack indexes every log line, queries it with SQL, streams it live, and prices ingestion at $0.10 per GB plus $0.05 per GB per month retained.
**During an incident, the log you need is usually the one nobody thought to keep searchable.** [See Better Stack log management](https://betterstack.com/log-management).
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## The same question in LogQL and DataPrime
Most comparisons warn that DataPrime has a learning curve. It is easier to judge by looking. Say your checkout service writes JSON logs like `{"user": {"plan": "enterprise"}, "region": "eu-west-1", "duration_ms": 412}` and you want p99 latency for enterprise customers, by region, over the last hour.
In Grafana Cloud, that is a LogQL metric query:
```logql
quantile_over_time(0.99,
{app="checkout"}
| json
| user_plan="enterprise"
| unwrap duration_ms [1h]
) by (region)
```
In Coralogix, it is a DataPrime pipeline:
```dataprime
source logs
| filter $l.applicationname == 'checkout'
| filter $d.user.plan == 'enterprise'
| groupby $d.region aggregate percentile(0.99, $d.duration_ms) as p99_ms
```
A few differences stand out. LogQL parses the JSON at query time and flattens nested keys, so `user.plan` becomes `user_plan`, while DataPrime reads `$d.user.plan` directly because Coralogix parsed the log on the way in. LogQL returns a time series per region, ready to drop into a panel, whereas DataPrime returns one row per region, which feels closer to SQL. DataPrime's prefixes, `$d` for the log body, `$l` for labels such as application name, and `$m` for metadata such as severity, are what newcomers trip over, but they are also what lets one language span logs, spans, and metrics.
Neither language is hard for someone who uses it every week. Portability is the real difference: the LogQL query runs unchanged on self-hosted Loki, and the DataPrime query runs only on Coralogix. Olly can draft DataPrime from a plain-language prompt, which softens the learning curve, although generated queries consume AI units while hand-written ones are free.
Dashboards follow the same pattern. Grafana's remain the benchmark: templated variables, transformations, and dozens of data source plugins let one board mix Prometheus metrics with a SQL database and a cloud billing API, and the community library has a starting point for most common stacks. Coralogix's Custom Dashboards cover the usual panel types over its own data, with far less room to customize.
| Querying and dashboards | Grafana Cloud | Coralogix |
|---|---|---|
| **Main query languages** | PromQL, LogQL, TraceQL | DataPrime, Lucene, PromQL |
| **One language across signals** | ✘ | ✔, DataPrime |
| **Nested JSON fields** | Flattened at query time | Native paths such as $d.user.plan |
| **Query portability** | High, open languages | Low for DataPrime, high for PromQL |
| **AI query drafting** | Grafana Assistant | Olly, billed in AI units |
| **Dashboard flexibility** | Extensive | Common panel types |
| **External data sources** | Dozens | Mainly its own data |
[summary]
### Dashboards built by dragging, not by learning syntax
Grafana asks your team to learn three query languages, and Coralogix asks them to learn a proprietary one. Better Stack lets anyone drag fields onto a chart, with SQL and PromQL available for deeper work, so dashboards are not limited to the people who know the syntax.
**A dashboard only one engineer can edit becomes a liability during an outage.** [Build a Better Stack dashboard](https://betterstack.com/dashboards).
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## Services, hosts, and browsers
### APM and tracing
Coralogix ships APM as one bundle on OpenTelemetry. It opens on a service catalog showing health, request volume, error rate, and P95 latency per service, and adds continuous profiling, Lambda tracing, database query monitoring tied to traces, and SLOs with burn-rate alerts, all inside its data-based pricing. Span metrics have a per-service, per-metric cardinality limit, 100,000 series by default in its Kubernetes integration, which you can raise, lower, or disable, and sanitizers that scrub high-cardinality span names are on by default.
Grafana assembles APM from parts. Traces go to Tempo, which keeps them in object storage and searches them with TraceQL, so `{ resource.service.name = "checkout" && span.http.status_code >= 500 }` pulls every failing checkout span. Application Observability turns spans into per-service rate, error, and duration views, and Pyroscope adds profiling as a separate signal billed per GB. Every part can also run self-hosted, which is what you get in exchange for wiring them together.
### Infrastructure and Kubernetes
Prometheus users will feel at home in Grafana Cloud. Mimir accepts remote-write and answers PromQL unchanged, keeps Pro metrics for 13 months, and backs the Kubernetes Monitoring app. Billing is where the care goes, because both label cardinality and scrape interval feed the billable series count.
Coralogix's Infrastructure Explorer shows fleet health across accounts and regions with Kubernetes relationships already mapped, so you can follow a problem from cluster to node to pod. Metrics are billed by the gigabyte at a rate low enough that series counts stop being a budgeting topic.
### Frontend
Grafana's Faro Web SDK feeds Frontend Observability with Web Vitals, JavaScript errors, and user sessions tied to backend traces, at $0.75 per 1,000 sessions beyond the free allowance. Session Replay entered public preview in August 2026 as a waitlisted add-on. Synthetic Monitoring and k6 cover scripted checks and load tests natively.
Coralogix RUM pairs Web Vitals and network-level request detail with backend APM, and its Session Replay is generally available for web, iOS, and Android. Synthetic checks come through a Checkly integration rather than a native product, and there is no load testing.
| Services, hosts, browsers | Grafana Cloud | Coralogix |
|---|---|---|
| **Service overview** | ✔, Application Observability | ✔, service catalog |
| **Continuous profiling** | ✔, Pyroscope, billed separately | ✔, included |
| **Database query monitoring** | Via integrations | ✔, linked to traces |
| **Trace price** | $0.45 per GB | $0.16 per GB |
| **Metrics billing** | Active series or DPM, whichever is higher | GB ingested |
| **Prebuilt integrations** | Large catalog, including Kubernetes and major clouds | 300+ |
| **Session replay** | Public preview, add-on | ✔, web, iOS, Android |
| **Synthetic checks** | ✔, native | Via Checkly |
| **Load testing** | ✔, k6 | ✘ |
## AI and MCP
Olly is Coralogix's multi-agent investigator. You describe a problem in plain language, and Olly picks the relevant data, queries logs, metrics, and traces, and shows each reasoning step as it works. Coralogix's MCP server goes further than most: from Cursor or Claude Code you can query telemetry, create and edit alerts across 12 alert types and parsing rules, and export them as Terraform, Kubernetes Operator YAML, or OpenAPI definitions. Since June 2026 you can also hand investigations to Olly through MCP, an opt-in feature because it consumes AI units. The AI Center, built after Coralogix bought Aporia in December 2024, watches production LLM applications for hallucinations, data leaks, and cost, and can block unsafe responses.
Grafana Assistant turns plain-language prompts into PromQL, LogQL, and TraceQL, builds and edits dashboards, and runs Investigations that chain several queries together. Sift runs its own checks for error spikes, latency shifts, and noisy neighbors as soon as an incident is declared. Billing is per active AI user: the Pro platform fee covers three, each with 40 million tokens, and every additional user costs $20 a month, with Investigations metered separately since October 1, 2026. Grafana offers a hosted Cloud MCP server alongside the open-source mcp-grafana project, and connecting through the hosted one counts as an active AI user even if you spend no tokens, so giving a whole team MCP access has a per-seat price.
| AI and MCP | Grafana Cloud | Coralogix |
|---|---|---|
| **AI investigator** | Grafana Assistant | Olly, multi-agent |
| **Automatic incident checks** | ✔, Sift | Initiated by the user |
| **LLM application monitoring** | Via AI Observability integrations | ✔, AI Center with guardrails |
| **MCP server** | ✔, hosted Cloud MCP and open-source mcp-grafana | ✔, alerts, parsing rules, Olly |
| **Infrastructure as code from MCP** | ✘ | ✔, Terraform, Kubernetes YAML, OpenAPI |
| **AI pricing** | 3 AI users included, then $20 per user | AI units per use, AI Center at $1.50 per million tokens |
## Alerts, on-call, and security
Grafana handles the response itself. Alert rules can query any connected data source, and IRM adds schedules, escalation chains, phone and SMS paging, and incidents you can declare from a dashboard panel. Three IRM users are included, and each additional active user costs $20 a month.
Coralogix's advantage is timing, because rules evaluate on the stream before indexing. What happens next belongs to another product: Coralogix sends notifications to PagerDuty, Opsgenie, Slack, and similar tools, with no on-call schedules or incident management of its own. It makes up ground on the security side, where its SIEM runs on the same telemetry your engineers already send, so one platform can serve both your SRE and security teams.
| Alerting, incidents, security | Grafana Cloud | Coralogix |
|---|---|---|
| **Alert timing** | After ingestion | In-stream, before indexing |
| **On-call and escalation** | ✔, Grafana IRM | ✘ |
| **Phone and SMS paging** | ✔ | Via integrations |
| **Incident management** | ✔ | ✘ |
| **SIEM** | ✘ | ✔ |
| **Status pages** | ✘ | ✘ |
[summary]
### The alert, the page, and the status update in one place
Coralogix's in-stream alerts leave the platform the moment they fire, and Grafana's incidents have nowhere public to report status. Better Stack opens a Slack or Teams incident channel with the triggering telemetry already attached, calls and texts responders at $29 per responder with no message caps, and posts updates to your status page from that same incident.
**Shaving seconds off detection is wasted if the handoff to a human takes minutes.** [Explore Slack-based incident management](https://betterstack.com/incident-management).
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## Compliance
Regulated buyers will find more certifications on the Coralogix side, with one major exception. Coralogix holds SOC 2 Type II, ISO 27001 along with 27701, 27017, 27018, and 42001, PCI DSS, HIPAA, and GDPR, and keeps long-term data in your own bucket on every plan. Grafana Cloud covers SOC 2 Type II, GDPR, and PCI DSS, offers regional hosting, and can be self-hosted or licensed as Grafana Enterprise. The exception is federal work: Grafana Federal Cloud is FedRAMP High authorized and meets DoD Impact Level 5, a bar Coralogix does not list, though Grafana IRM sits outside that authorization boundary, so on-call may need a separate exception. If you are weighing Coralogix against a larger full-stack vendor, our [Datadog vs Coralogix comparison](https://betterstack.com/community/comparisons/datadog-vs-coralogix/) covers that matchup, and our [Sumo Logic vs Coralogix comparison](https://betterstack.com/community/comparisons/sumo-logic-vs-coralogix/) sets it against a security-focused platform.
| Compliance | Grafana Cloud | Coralogix |
|---|---|---|
| **SOC 2 Type II** | ✔ | ✔ |
| **ISO 27001** | Confirm with Grafana | ✔, plus 27701, 27017, 27018, 42001 |
| **PCI DSS** | ✔ | ✔ |
| **HIPAA** | Confirm with Grafana | ✔ |
| **FedRAMP** | ✔, High via Grafana Federal Cloud | ✘, not among listed certifications |
| **Customer-owned data storage** | Self-hosting or Enterprise BYOC | ✔, every plan |
## Leaving either platform
Since this comparison started with exits, it is worth walking through one in each direction.
Instrumentation moves easily either way. Both platforms accept OpenTelemetry, so you can point your collector at both destinations and run them side by side for as long as the migration takes.
Leaving Grafana Cloud for self-hosted Grafana is the smoothest path either vendor offers. Dashboards export as JSON, alert rules can be managed as code with Grafana's Terraform provider, and PromQL, LogQL, and TraceQL run unchanged on the open-source backends. Historical data is the part to plan, so keep both running in parallel for as long as your retention needs require rather than counting on one bulk export.
Leaving Coralogix inverts that. Your archive is already in your bucket as Parquet, readable by any engine that reads Parquet, so history is the easy part. The work is in everything built on top: DataPrime queries and Custom Dashboards have to be rebuilt, and alerts exported as Terraform target Coralogix's provider, so they are a specification to translate rather than a file to reuse. PromQL dashboards and queries are the exception and move with little change.
| What moves | Leaving Grafana Cloud | Leaving Coralogix |
|---|---|---|
| **Instrumentation** | ✔, OpenTelemetry and Alloy | ✔, OpenTelemetry |
| **Historical data** | Parallel run or export | Already in your bucket as Parquet |
| **Dashboards** | JSON, runs on any Grafana | Rebuilt, except PromQL panels |
| **Queries** | Run unchanged on the open-source stack | DataPrime rewritten, PromQL moves |
| **Alert rules** | Portable to self-hosted Grafana | Exportable as Terraform, then translated |
## Final thoughts
On list prices, Coralogix is the cheaper platform by a wide margin, and for a log-heavy team the margin is real. **The savings depend entirely on routing**, and routing is a set of bets about which logs you will never need to search. Grafana asks you to make bets too, about labels and scrape intervals, but a wrong guess there raises the bill, while a wrong route in Coralogix hides the data.
That is the decision underneath the feature tables. Grafana Cloud suits teams that want open query languages, the best dashboards, on-call in the same product, FedRAMP, and a self-hosting fallback, and who can live with series-based and per-seat billing. Coralogix suits teams whose log bill is the problem, who want years of retention in their own bucket and SIEM on the same data, and who are willing to learn DataPrime and buy a pager elsewhere.
Before you sign with Coralogix, write down every log source you plan to route out of Frequent Search and have your on-call lead initial the list.
[summary]
### Investigate and respond from the same AI conversation
Coralogix's MCP server can query telemetry and write alert Terraform, but there is no on-call data behind it to reach. Grafana's covers more ground, through a hosted server where every connected engineer beyond three is a billable AI user. Better Stack puts logs, metrics, traces, on-call schedules, and incidents behind one MCP server, so from Claude or Cursor you can run a SQL query against your logs, see who holds the pager, acknowledge the incident, and chart the fix in the same conversation.
**Your assistant should be able to act on what it finds, not just report it.** [Try Better Stack](https://betterstack.com).
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