# Dynatrace vs Coralogix: A Complete Comparison for 2026
In July 2026, Gartner named both Dynatrace and Coralogix Leaders in its Magic Quadrant for Observability Platforms. For Dynatrace it was the sixteenth consecutive time in the Leaders quadrant, a streak that reaches back to when the company was still called Compuware. For Coralogix it was the first, a jump straight from Visionary the year before. They now share the same corner of the same chart, which is exactly why the comparison is interesting: **two platforms arrived at the top of the observability market by believing almost opposite things about what an observability platform should be.**
Dynatrace built the most complete proprietary platform in the category. One agent that installs itself and injects instrumentation at runtime, one topology graph that maps every dependency automatically, one data lakehouse that stores everything and answers questions with a causal AI engine that has been running in production since 2017. The depth is real, and so is the price of that depth: your data lives in Dynatrace's cloud, your instrumentation is Dynatrace's format, and your bill meters consumption across memory-GiB-hours, host-hours, GiB-days, GiB-scanned, and per-session charges.
Coralogix built the opposite. **A streaming engine that analyzes telemetry before it is ever indexed, with your data landing in your own S3 or GCS bucket in open formats, priced purely by volume with no per-user and no per-host fees.** What you gain is cost control and data ownership. What you take on is a set of routing decisions about where each stream of data should live, and a proprietary query language that ties your dashboards and saved investigations to the platform even though your raw data does not.
Neither approach is wrong. They solve for different anxieties: Dynatrace for the enterprise that wants one system to see everything and explain it, Coralogix for the organization that wants to keep its data, control its spend, and still run a full observability and security stack. The rest of this comparison walks through where each one wins, using current 2026 pricing and feature status, so you can match the platform to the shape of your team, your infrastructure, and your budget.
## Quick comparison at a glance
| Feature | Dynatrace | Coralogix |
|---|---|---|
| **Primary purpose** | Enterprise observability + security platform | Streaming-first observability with customer-owned storage |
| **Gartner MQ 2026** | Leader (16th consecutive time) | Leader (first time, Visionary in 2025) |
| **Deployment model** | SaaS (Grail-hosted) + Managed | SaaS (data in your own S3/GCS/Azure bucket) |
| **Free tier** | 15-day trial only | 14-day trial only |
| **Pricing model** | Consumption rate card (DPS) | Units-based (data volume, no seats, no hosts) |
| **Cost anchored to** | Host memory, data volume, queries, capabilities | Data volume (unlimited users and hosts) |
| **Data ownership** | Dynatrace (Grail) | You (your cloud bucket, open formats) |
| **Query language** | DQL (unified via Grail) | DataPrime + PromQL + Lucene |
| **APM instrumentation** | OneAgent (auto-inject) or OTel | OTel SDKs + optional eBPF |
| **Code-level profiling** | Yes (PurePath, method-level) | Yes (continuous, included) |
| **Topology mapping** | Smartscape (automatic, real-time) | Service maps from telemetry |
| **Log management** | $0.20/GiB + retention + query fees or bundled | In-stream, infinite retention in your bucket |
| **Infrastructure monitoring** | Deep (incl. mainframe, SAP, VMware) | Fleet management, cross-account, no host fees |
| **Real user monitoring** | Yes (web + mobile + session replay) | Yes (browser + mobile + network monitoring) |
| **AI root cause** | Davis AI (deterministic, since 2017) | Olly (multi-agent, initiated then autonomous) |
| **MCP server** | Yes (GA, remote + local, query costs apply) | Yes (GA, all customers, generates IaC) |
| **AI agent / LLM monitoring** | AI Observability (Davis for AI) | AI Center (full suite with guardrails) |
| **Application security** | Yes (runtime vulnerability + protection + posture) | Yes (SIEM, 2,500+ rules, in-stream) |
| **MDR** | No | Yes (via Snowbit.io) |
| **On-call scheduling** | Via PagerDuty/ServiceNow/external | Not included (external tools) |
| **Status pages** | No | No |
| **SOC 2 Type II** | Yes | Yes |
| **HIPAA** | Yes | Yes |
| **PCI DSS** | No (not standard portfolio) | Yes |
| **ISO 27001** | Yes | Yes |
| **FedRAMP** | Yes (Moderate, expanding to High) | Yes (GovCloud region) |
| **24/7 human support** | Dynatrace ONE (tiered) | Yes (all tiers, 17s median response) |
| **OTel support** | Yes (counts toward consumption) | Yes (first-class, no surcharge) |
---
## Platform architecture and philosophy
Everything downstream in this comparison, the pricing, the lock-in, the investigation experience, traces back to one decision each company made about where your data should live and how it should be processed. They chose differently, and the difference is the whole story.
### Dynatrace: OneAgent, Grail, and Smartscape, one proprietary system that sees everything
Dynatrace rests on three pieces of engineering that are, on their own merits, excellent. OneAgent installs on each host, discovers every running process, and injects monitoring code at runtime, which gives you code-level visibility including CPU profiling, memory allocation tracking, and method-level hotspot detection through PurePath. Smartscape takes what OneAgent discovers and builds a live topology graph of every dependency across your environment, updating in real time as things change. Grail, the data lakehouse, stores logs, traces, metrics, and events together with schema-on-read querying through DQL, which removes the index management and hot/cold storage juggling that older architectures force on you.
The payoff is a platform where an alert, a topology map, a trace, and a root cause explanation all draw on the same underlying model. The cost of that payoff shows up in two places. The first is portability: your instrumentation, your topology, and your query language are all Dynatrace-specific, so the deeper you go, the harder it is to leave. The second is the bill. The Dynatrace Platform Subscription meters each capability separately, and forecasting next month's spend means predicting consumption across many independent dimensions at once. A 64 GiB database server costs roughly eight times what an 8 GiB web server costs for the same Full-Stack coverage, because Full-Stack is billed per memory-GiB-hour.
### Coralogix: Streama processing with your data in your own bucket
Coralogix analyzes telemetry with its Streama engine as it flows through the platform, before anything is indexed anywhere. Alerts and anomaly detection run on data that has not yet been written to storage, so there is no delay between an event happening and your alerting reacting to it. Once Streama has processed a stream, it routes to one of three destinations you configure in the TCO Optimizer: Frequent Search for hot, indexed, instantly queryable data, Monitoring for in-stream analysis only, and Archive, which is your own S3 or GCS bucket, queryable through DataPrime without a rehydration fee.
That customer-owned storage is the architectural decision that defines Coralogix. Your logs, traces, and metrics sit in your cloud account in open formats. Coralogix compresses logs roughly five times before writing them, so the effective S3 cost lands around $0.003 per GB, and retention is effectively infinite because it is your bucket and your retention policy. If you ever leave, the data is already yours.
The trade you make for that is a set of upfront decisions. Data routed only through the Monitoring pipeline cannot be queried after the fact, so if the wrong stream lands in the wrong tier, you discover it during the incident that needed it. And while your data is portable, your operational knowledge is less so: DataPrime is proprietary, and every dashboard, saved query, and alert condition your team writes in it is tied to Coralogix. Your raw data is open, but the tooling built on top of it is not. That is a milder form of lock-in than the format-and-topology lock-in Dynatrace creates, but it is worth naming clearly before you commit.
| Architectural factor | Dynatrace | Coralogix |
|---|---|---|
| Data storage | Grail (Dynatrace-hosted lakehouse) | Your S3/GCS/Azure bucket |
| Processing model | Ingest, then schema-on-read query | In-stream analysis, then tiered storage |
| Query language | DQL | DataPrime + PromQL + Lucene |
| Alert latency | Standard | Zero (in-stream, before indexing) |
| Data ownership | Dynatrace | You |
| Instrumentation | OneAgent (auto-inject) or OTel | OTel SDKs + optional eBPF |
| Topology mapping | Smartscape (automatic, real-time) | Service maps from telemetry |
| Cost pressure grows with | Host memory, queries, capabilities | Data volume |
| Retention model | Grail retention (usage-based) | Infinite (your bucket) |
[summary]
### Neither platform closes the loop on incidents
Both Dynatrace and Coralogix detect problems well and then hand the response off to another tool. Neither includes on-call scheduling with phone and SMS delivery or customer-facing status pages as part of the platform. Better Stack brings detection and response together with logs, metrics, and traces, so you move from alert to on-call to incident timeline to status page without leaving one interface.
**From heartbeat monitoring to incident timelines to status pages, one platform for the whole reliability lifecycle.** [Start free.](https://betterstack.com)
[/summary]
---
## APM and distributed tracing
This is the category Dynatrace built its name on, and it remains the clearest place to see the depth-versus-openness trade in action. Both platforms trace requests across services down to the code, but they get there by very different routes.
### Dynatrace: OneAgent and PurePath, the deepest auto-instrumentation in the category
Install OneAgent on a host and it discovers your processes, injects instrumentation, and starts capturing PurePath traces that follow a single transaction from the browser through every service call and database query to the exact method responsible for the latency. For performance engineering teams doing serious optimization work on JVM or .NET applications, the method-level flame graphs and thread contention analysis are the reason Dynatrace has kept its reputation for two decades. Smartscape enriches every trace with topology, so you are never looking at a span in isolation.
The catch is the same one that runs through the whole platform. OneAgent instrumentation is proprietary, and while Dynatrace supports OpenTelemetry ingestion, OTel data counts toward metered consumption rather than arriving surcharge-free. Trace queries on the pay-per-query model also incur GiB-scanned charges, which means a deep investigation into a tricky latency problem carries a metered cost that scales with how thoroughly you dig.
### Coralogix: OpenTelemetry-native APM with profiling and SLOs included
Coralogix APM is built entirely on OpenTelemetry, so there is no proprietary agent to adopt and no surcharge for using open instrumentation. The service catalog gives you a live view of every service with request volume, error rate, and P95 latency, filterable by environment or team. Continuous code-level profiling is included rather than sold as an add-on, showing which functions burn CPU and where memory is allocated in production. Serverless APM covers Lambda, SLO management with error budgets and burn-rate alerting is built in, and database monitoring links slow queries straight to the traces that triggered them.
One number to keep in view: the span metric cap of 300,000 unique combinations. For very high-cardinality APM workloads it needs monitoring, but it is a configuration constraint rather than a billing penalty, which is a different kind of ceiling from a metered query charge.
| APM / tracing | Dynatrace | Coralogix |
|---|---|---|
| Instrumentation | OneAgent (auto-inject) or OTel | OTel SDKs + optional eBPF |
| OTel treatment | Supported, counts toward consumption | First-class, no surcharge |
| Code-level profiling | Yes (PurePath, method-level, deeper) | Yes (continuous, included) |
| Serverless APM | Yes | Yes |
| SLO management | Yes | Yes (built-in with error budgets) |
| Database monitoring | Yes | Yes (built-in, trace-linked) |
| Trace query fees | $0.0035/GiB-scanned (pay-per-query) | None (volume-based) |
| APM pricing | $0.01/memory-GiB-hour (Full-Stack) | Included in units (no per-host charge) |
[summary]
### Tracing priced by volume, not by host memory or query depth
Dynatrace ties APM cost to host memory and meters your trace queries; Coralogix folds it into data volume. Better Stack takes a third path: full-fidelity distributed tracing priced purely by the data you send, with no per-host multipliers, no span indexing fees, and an AI SRE that starts investigating root cause automatically when an incident fires.
**Full-fidelity distributed tracing from every service, priced by volume with no surprises.** [Explore Better Stack tracing.](https://betterstack.com/tracing)
[/summary]
---
## Log management
Both platforms handle logs at enterprise scale, and both avoid the crude indexed-versus-archived split that makes some competitors painful. Where they part ways is on what a query costs and where the bytes ultimately live.
### Dynatrace: cheap ingest, then a choice between metered queries and expensive retention
Dynatrace ingests logs at $0.20/GiB, half of what many competitors charge, and Grail's schema-on-read model means you skip field indexing and parser configuration upfront. DQL parses on read, aggregates across signals, and enriches log data with Smartscape topology, which adds context that a raw log search cannot. The complexity arrives after ingest. On the pay-per-query plan, retention is cheap at $0.0007/GiB-day but every DQL query scans data at $0.0035/GiB, so the more thoroughly you investigate an incident, the more that investigation costs. The bundled-queries plan removes the per-scan charge but prices retention at $0.02/GiB-day, roughly 28 times higher, with querying included. You have to predict your team's query behavior before you sign to pick the right side of that trade.
### Coralogix: in-stream analytics with infinite retention in your own bucket
Coralogix processes logs in-stream, which is why alerts fire without indexing delay, then routes them to the tier you chose. Frequent Search runs $0.42/GB for fast, indexed queries, while data in your S3 bucket stays queryable through DataPrime with no rehydration fee. DataPrime handles deeply nested JSON, arrays, and mixed types without predefined schemas, which saves real setup time on messy or evolving log formats.
The economics of retention are where this design pays off. Because logs live in your bucket at an effective $0.003 per GB after compression, keeping years of searchable history costs a fraction of what hot vendor storage costs, and there is no rehydration tax every time you need to look back. For a compliance-driven team holding a terabyte a month for two years, that difference is the difference between a rounding error and a line item. The trade, again, is the routing discipline: anything sent only through Monitoring is not available for ad-hoc queries later, so the TCO Optimizer configuration is a decision you want to get right before you need the data.
| Log management | Dynatrace | Coralogix |
|---|---|---|
| Ingest rate | $0.20/GiB | $0.42/GB (Frequent Search), less for lower tiers |
| Query fees | $0.0035/GiB-scanned or bundled | None |
| Retention pricing | $0.0007 or $0.02/GiB-day (model-dependent) | Your S3 cost (~$0.003/GB effective) |
| Infinite retention | No (usage-based, up to 10 years) | Yes (your bucket, no rehydration fee) |
| Data ownership | Dynatrace | You |
| Schema handling | Schema-on-read (DQL) | Dynamic (no pre-definition needed) |
| Upfront decision | Pay-per-query vs bundled | Tier routing (TCO Optimizer) |
[summary]
### Log search with no query meter and no indexing tax
Dynatrace meters your queries or your retention, and Coralogix asks you to route data to the right tier ahead of time. Better Stack stores logs in a unified warehouse with SQL querying, no per-event charges, and no scan fees, so you can investigate as many times as an incident demands and pay only for what you sent.
**Unified log management with SQL search, live tail, and no indexing surprises.** [See how it works.](https://betterstack.com/logs)
[/summary]
---
## Infrastructure monitoring
Both platforms cover cloud infrastructure thoroughly. Dynatrace covers a great deal more than the cloud, and charges for hosts in a way Coralogix does not.
### Dynatrace: the broadest coverage in the category, priced by host memory
Dynatrace monitors the usual cloud estate, then keeps going into territory most cloud-native platforms ignore: mainframes, SAP systems, VMware Tanzu, and hybrid data centers. For a large enterprise carrying decades of infrastructure generations, Smartscape mapping all of it under one topology is a real and hard-to-replicate advantage. Tiered monitoring modes let you match depth to host criticality, from Foundation and Discovery at $7/month per host, to Infrastructure at $29/month, to Full-Stack at roughly $58/month per 8 GiB host.
The memory-based pricing is the thing to watch. Because Full-Stack is billed per memory-GiB-hour, large-memory hosts cost proportionally more: a 64 GiB database server runs around $467/month for Full-Stack coverage against $58 for an 8 GiB host. Kubernetes pods add $0.002 per pod-hour unless the host already runs Full-Stack, and cloud integrations pull in thousands of metrics by default, with custom metrics billed at $0.15 per 100,000 datapoints. Teams regularly discover cloud metric costs they never explicitly turned on.
### Coralogix: fleet management with cross-account visibility and no host fees
Coralogix's Infrastructure Explorer manages fleets across accounts and regions, with relationship mapping that connects pods to nodes and services to volumes, plus historical metadata snapshots that let you see how infrastructure changed over time. There are no per-host fees and no high-water-mark billing. Metrics are priced at $0.05/GB, where a gigabyte equals a thousand time series, and every engineer can view infrastructure data because there is no seat model gating access.
That last point is the structural contrast. Dynatrace's coverage is broader and its topology is deeper, but its cost is anchored to how many hosts you run and how much memory they carry. Coralogix's coverage is cloud-native rather than mainframe-deep, but its cost is anchored to data volume, so scaling your host count does not scale your bill.
| Infrastructure monitoring | Dynatrace | Coralogix |
|---|---|---|
| Pricing | $7 to $58+/month per host (mode + memory dependent) | Per GB/units (unlimited hosts) |
| Host-size pricing | Yes (memory-GiB-hours for Full-Stack) | No |
| Mainframe / SAP / VMware | Yes | No |
| Kubernetes | Yes ($0.002/pod-hour or included with Full-Stack) | Yes (relationship mapping, historical snapshots) |
| Cross-account fleet management | Yes (Smartscape) | Yes |
| Data in your account | No | Yes (your bucket) |
[summary]
### Infrastructure metrics wired into the whole reliability workflow
Dynatrace charges by host memory and Coralogix by data volume, but neither connects infrastructure metrics directly to on-call and incident response. Better Stack does, with no per-host fees and no cardinality penalties, and infra metrics that sit alongside uptime monitors, on-call schedules, and incident timelines.
**Infrastructure monitoring connected to alerting, on-call, and incident management, all in one place.** [Get started free.](https://betterstack.com)
[/summary]
---
## Digital experience monitoring
Both platforms carry complete real user monitoring suites, and both correlate frontend sessions to backend traces. The differences are in how each one prices sessions and what extra visibility it adds around them.
### Dynatrace: mature RUM with session replay, priced per session
Dynatrace covers web and mobile RUM with crash analysis, session replay, browser synthetics, and HTTP monitors. The correlation from a slow user session to the PurePath trace behind it is excellent, arriving with code-level context rather than a loose link. The pricing is per session and adds up at scale: RUM runs $2.25 per 1,000 sessions, session replay $4.50 per 1,000 sessions, browser synthetics $4.50 per 1,000 actions, and HTTP monitors $1 per 1,000 requests. At five million monthly sessions with replay, that is roughly $22,500/month at list rates, which is worth modeling carefully for high-traffic consumer apps.
### Coralogix: full-stack RUM with network monitoring and version comparison
Coralogix RUM captures browser and mobile sessions, Core Web Vitals, JavaScript errors, and session replay, and then adds two things Dynatrace does not match natively. Network monitoring shows every request from the user's device to the backend, so you can tell whether latency is client-side, network-related, or server-side before you open a single trace. Version tracking compares performance and error rates between deployment versions in real time, which surfaces a frontend regression in the data before the support tickets start. Digital Experience Analytics, combining behavioral data with RUM and APM, reached general availability in early 2026.
The investigation experience differs in shape. Dynatrace links a session to its backend trace with topology-aware, code-level context. Coralogix correlates across product screens and leans on its network and version data to localize a problem quickly. Both are strong; they optimize for slightly different first questions.
| Digital experience | Dynatrace | Coralogix |
|---|---|---|
| Browser RUM | Yes ($2.25/1,000 sessions) | Yes |
| Mobile RUM | Yes (with crash analysis) | Yes (iOS, Android) |
| Session replay | Yes ($4.50/1,000 sessions) | Yes |
| Synthetic monitoring | Yes (browser + HTTP, per-action) | Yes |
| Network monitoring | No | Yes |
| Version tracking | Basic | Yes (deployment-aware comparison) |
| Backend correlation | Topology-aware (PurePath) | Cross-screen correlation |
---
## AI capabilities
This is the section where the two platforms are most often described as representing different generations of AI thinking, and where, in 2026, those generations are visibly converging. Both now ship autonomous investigation and a generally available MCP server. The routes they took there could not be more different.
### Dynatrace: Davis, deterministic root cause since 2017, now fused with agentic AI
Davis AI predates the LLM era and works unlike most observability AI. It runs deterministic fault-tree analysis across the Smartscape topology graph, correlating events that share a root cause into a single problem instead of an alert storm. When a bad deployment cascades through six services, Davis traces the causal chain from the deployment event through the dependency graph to the user-facing symptom, with reproducible reasoning rather than probabilistic pattern-matching. Years in production make it the most battle-tested root cause engine in the category.
On top of that foundation, Dynatrace has layered agentic AI. Davis CoPilot (now folded into Dynatrace Assist) turns natural language into DQL and helps build dashboards, an SRE Agent investigates alerts autonomously, a Developer Agent proposes fixes linked to builds, and a Security Agent triages vulnerabilities, with AutomationEngine connecting detection to automated remediation. The Dynatrace MCP server is generally available in both remote and local versions, connecting Claude, Cursor, VS Code, GitHub Copilot, and Amazon Q to your environment. The one caveat that follows the rest of the platform: MCP queries run against Grail and incur GiB-scanned charges, so AI-driven investigation carries a metered cost.
### Coralogix: Olly, a multi-agent investigator with an AI Center for production LLMs
Olly is a multi-agent autonomous investigator that decides which data sources matter, executes queries across logs, metrics, and traces, and shows every step of its reasoning as it works. You start it in natural language and it runs autonomously from there, and connecting a GitHub repository gives it code context for its analysis. The Coralogix MCP server is generally available for all customers and does something Dynatrace's does not: it generates Terraform HCL and Kubernetes YAML from alert definitions, turning observability configuration into infrastructure as code, and a programmable agentic CLI supports fully headless workflows.
Where Coralogix has no direct Dynatrace counterpart is the AI Center, aimed squarely at teams running LLMs in production. AI Discovery finds every AI agent across your organization, the Evaluation Engine scores prompts and responses for hallucinations, PII leaks, relevance, and toxicity in real time, AI Guardrails intercept unsafe outputs before they reach users, and cost tracking monitors spend per message, session, and agent. As AI systems become the thing you are observing, not just the thing doing the observing, this is a category Coralogix has moved into early.
The practical difference at 3am: Dynatrace's Davis is deterministic and topology-aware, giving reproducible root cause on a graph model that Coralogix does not replicate. Coralogix's Olly is a transparent multi-agent reasoner that you initiate, paired with an AI observability suite that Dynatrace does not fully match. One is deeper on causal analysis of your infrastructure; the other is broader on governing the AI you run on top of it.
| AI capability | Dynatrace | Coralogix |
|---|---|---|
| Root cause engine | Davis AI (deterministic, topology-aware, since 2017) | Olly (multi-agent, shows reasoning chain) |
| Autonomous investigation | SRE Agent (agentic layer on Davis) | Olly (initiated, then autonomous) |
| Natural language querying | Davis CoPilot / Dynatrace Assist (GA) | Olly (GA) |
| MCP server | GA (remote + local) | GA (all customers) |
| IaC generation from alerts | No | Yes (Terraform + Kubernetes YAML) |
| Agentic CLI | No | Yes |
| MCP query costs | GiB-scanned charges apply | None (volume-based) |
| AI / LLM observability | AI Observability (Davis for AI) | AI Center (Discovery, guardrails, evaluation) |
| Automated remediation | AutomationEngine workflows | Via integrations |
[summary]
### AI investigation that also drives the response
Dynatrace and Coralogix both investigate autonomously and both ship an MCP server. What neither one closes is the path from a root cause hypothesis to an on-call page, an incident timeline, and a status page update. Better Stack's AI SRE connects to the full incident lifecycle, so the investigation and the response happen in the same place.
**Autonomous root cause investigation connected to on-call, incidents, and status pages.** [See the AI SRE.](https://betterstack.com)
[/summary]
---
## Security capabilities
For organizations where security sits inside the observability conversation, this section often decides the whole comparison, because the two platforms took security in different directions and both went deep.
Dynatrace built a runtime application security line that uses OneAgent's process-level visibility to do something signatures cannot: detect which vulnerable libraries are actually loaded in running production processes and whether the vulnerable code paths are reachable, then prioritize by real exposure rather than raw CVSS score. Runtime Vulnerability Analytics runs $13/month per 8 GiB host, Runtime Application Protection adds another $13/month per 8 GiB host and blocks exploitation attempts in real time, and Security Posture Management at $5/month per host checks compliance against CIS, NIST, DORA, and HIPAA. Threat Observability ties security signals to observability data for investigation. This is application security anchored in the running application.
Coralogix approaches security from the data side. It runs a full SIEM on the Streama engine with more than 2,500 out-of-the-box detection rules, 400-plus security integrations, and ML-based threat detection that fires before data reaches any storage tier. Managed Detection and Response is available through Snowbit.io for teams without a dedicated SOC. The compliance portfolio is broad, covering SOC 2, PCI DSS, ISO 27001, GDPR, HIPAA, and FCA, and the infinite retention in your own bucket is itself security-relevant, since frameworks that mandate multi-year log retention become far cheaper when the data lives in S3 rather than hot vendor storage.
The split is clean. Dynatrace protects the application at runtime and leads on posture management and defense-adjacent certifications like IRAP, TISAX, and FIPS 140-3. Coralogix runs threat detection and response across your telemetry and leads on data-centric compliance like PCI DSS and FCA. If your requirement is application-layer protection, Dynatrace has the product. If it is SIEM, MDR, and financial-services compliance, Coralogix has the product.
| Security | Dynatrace | Coralogix |
|---|---|---|
| Runtime vulnerability detection | Yes ($13/month per 8 GiB host) | Via SIEM correlation |
| Runtime application protection | Yes ($13/month per 8 GiB host) | No |
| Cloud SIEM | Via Threat Observability | Yes (2,500+ rules, in-stream) |
| MDR | No | Yes (via Snowbit.io) |
| Security posture management | Yes (CIS, NIST, DORA, HIPAA) | Yes (AI-SPM, compliance reporting) |
| SOC 2 Type II | Yes | Yes |
| PCI DSS | No | Yes |
| ISO 27001 | Yes | Yes |
| FedRAMP | Yes (Moderate, expanding to High) | Yes (GovCloud region) |
| IRAP / TISAX / FIPS 140-3 | Yes | Not standard |
---
## Incident management and alerting
Both platforms cover alerting well and both stop short of owning the incident response lifecycle, which is worth stating plainly because it shapes what else you will need to buy.
Dynatrace alerting is driven by Davis, which suppresses noise by collapsing correlated events into a single root-cause problem rather than a flood of individual alerts. That is a real strength during a cascading failure. AutomationEngine can trigger remediation workflows, and integrations route to ServiceNow, Slack, and PagerDuty. On-call scheduling and phone or SMS paging come from those external tools, not from Dynatrace.
Coralogix alerting fires from the Streama engine with zero indexing delay, and its ML-based anomaly detection adapts to normal behavior without manual threshold tuning. Flow alerts chain conditions across logs, metrics, traces, and security events in a visual builder, and Cases aggregate related alerts and route them to Slack, PagerDuty, ServiceNow, or webhooks, with a native bidirectional ServiceNow integration. As with Dynatrace, the paging layer itself lives in PagerDuty or OpsGenie.
Neither platform offers customer-facing status pages, and neither offers native on-call scheduling with phone and SMS delivery. If your reliability workflow needs those, both platforms assume you will bring a separate tool for them.
| Incident management | Dynatrace | Coralogix |
|---|---|---|
| Alert intelligence | Davis (correlated problems, GA) | ML-based (adaptive, zero-latency) |
| On-call scheduling | Via PagerDuty/ServiceNow/external | Not included (external tools) |
| Phone/SMS delivery | Via external tools | Via PagerDuty/OpsGenie |
| Flow / composite alerts | Yes (Davis correlation) | Yes (drag-and-drop, cross-signal) |
| ServiceNow integration | Yes | Yes (native, bidirectional) |
| Status pages | No | No |
---
## Pricing comparison
The structural difference matters more than any single rate: Dynatrace anchors cost to your infrastructure and your query behavior, while Coralogix anchors it to your data volume and hands you your own storage. Which one is cheaper depends almost entirely on whether you are host-heavy or data-heavy.
Consider a mid-sized cloud-native profile: 100 hosts averaging 8 GiB, 300 Kubernetes pods, 500 GB of logs a month, and 200 GB of traces a month.
| Cost component | Dynatrace (DPS, list rates) | Coralogix (list rates) |
|---|---|---|
| Host / infrastructure monitoring | ~$5,800/month (Full-Stack, 100 x 8 GiB) | $0 (no per-host fee) |
| Kubernetes (300 pods) | Included with Full-Stack | Included in volume |
| Log ingest (500 GB) | ~$100/month | ~$210/month (Frequent Search) |
| Log retention + queries | ~$300 to $400/month (model-dependent) | Your S3 cost (~a few dollars) |
| Traces (200 GB) | Allowance included, queries metered | ~$32/month |
| Metrics | Cloud metrics metered | ~$25/month |
| 24/7 support | Dynatrace ONE (tiered) | Included |
| On-call (5 responders, PagerDuty) | ~$245 to $415/month | ~$245 to $415/month |
| **Estimated monthly total** | **~$6,445 to $6,715/month** | **~$512 to $682/month + your S3** |
At this profile the gap is large, and it is almost entirely the host-based Full-Stack line. But flip the shape of the workload and the picture inverts. A data-heavy shop with only 10 hosts but 20 TB of logs a month sees Dynatrace's host cost stay small while Coralogix's volume-based bill climbs, and Coralogix's tiered routing and your-own-bucket retention become the levers that keep it in check. The honest summary is that host density favors Coralogix and raw data density narrows the gap, so you should run both calculations against your real telemetry before a single demo.
Two structural cautions round out the comparison. Dynatrace requires a minimum annual commitment, reported around $24,000/year, and its list rates are a starting point that enterprise discounts negotiate down from, so the sticker rarely equals the signed price. Coralogix has no seat or host minimums and includes 24/7 support at every tier with a median response time measured in seconds, though you carry your own S3 storage costs on top of the license. Neither offers a free tier; both offer a two-week trial.
| Pricing factor | Dynatrace | Coralogix |
|---|---|---|
| Free tier | 15-day trial | 14-day trial |
| Cost anchored to | Host memory + consumption | Data volume |
| Per-seat fee | None | None |
| Per-host fee | Yes (memory-GiB-hours) | No |
| Query fees | Logs/traces on pay-per-query | None |
| Minimum commitment | ~$24,000/year reported | None |
| Data storage cost | Included (Grail) | Your S3/GCS bucket |
| Bill predictability | Requires consumption forecasting | Volume-based, your storage separate |
[summary]
### Observability without the multi-vendor stack
Both Dynatrace and Coralogix need separate tools for on-call scheduling and status pages. Better Stack consolidates logs, metrics, traces, on-call scheduling, incident management, and status pages into one platform with one bill and usage-based pricing.
**Fewer vendors, fewer context switches, and a single place for the full reliability workflow.** [Talk to us.](https://betterstack.com)
[/summary]
---
## What each platform genuinely lacks
**Dynatrace gaps worth knowing:**
1. Consumption forecasting across many billing dimensions is hard, and unexpected costs from cloud metrics and query scanning are commonly reported.
2. Memory-based Full-Stack pricing penalizes large-memory hosts heavily.
3. Log and trace query fees on pay-per-query mean thorough incident investigation carries a metered cost.
4. Steep learning curve; reviewers consistently flag onboarding difficulty and information-dense dashboards.
5. Proprietary depth across OneAgent, PurePath, Smartscape, and DQL creates the strongest lock-in in the category, and your data lives in Grail rather than your own account.
6. Minimum annual commitment excludes smaller teams, and there is no free tier.
7. No customer-facing status pages and no native on-call with phone or SMS delivery.
8. OTel data counts toward metered consumption rather than being surcharge-free.
9. No PCI DSS in the standard portfolio.
**Coralogix gaps worth knowing:**
1. No on-call scheduling, escalation policies, or phone and SMS delivery.
2. No customer-facing status pages.
3. DataPrime is proprietary, so every query, dashboard, and saved investigation your team builds is tied to Coralogix even though your raw data is not.
4. TCO Optimizer tier routing requires upfront decisions that can leave you without queryable data at the wrong moment.
5. The 300,000 span metric cap requires monitoring for high-cardinality APM workloads.
6. No mainframe, SAP, or VMware Tanzu monitoring, so hybrid enterprises with legacy estates are less well served.
7. Application-layer runtime protection is not part of the product the way it is for Dynatrace.
8. You carry your own S3 storage costs alongside the license.
9. No free tier; evaluation is a 14-day trial.
---
## Final thoughts
Both of these platforms earned their place in the 2026 Gartner Leaders quadrant, and the choice between them is not about which is better in the abstract. It is about which set of trade-offs fits the organization you actually run.
Reach for **Dynatrace** when your environment is large, mixed, and mission-critical. If your estate spans **mainframes, SAP, VMware, and years of hybrid infrastructure** alongside cloud-native workloads, Dynatrace monitors all of it under one topology and Coralogix does not. If you need **deterministic, topology-aware root cause analysis** on complex dependency graphs, Davis remains the deepest engine in the category, and the runtime application security line gives you protection that lives inside the running process. The price of that reach is real: **a consumption model that demands active budget management, a steep learning curve, the strongest lock-in in the market, and your data living in Dynatrace's cloud rather than your own.**
Reach for **Coralogix** when cost control and data ownership are first-order concerns. **Because your telemetry lands in your own bucket in open formats, retention is effectively infinite at S3 prices and your data leaves with you if you go.** The absence of per-user and per-host fees means the platform scales with your data rather than your headcount or your server count, which changes the economics for large teams and large fleets. Add the **AI Center for governing production LLMs, a full in-stream SIEM, and PCI DSS and FCA compliance**, and Coralogix becomes the stronger fit for data-heavy, cost-sensitive, and regulated-by-data-residency organizations.
The fastest way to decide is to stop comparing feature lists and start measuring your own shape. Count your hosts and their memory, measure your monthly data volume, and be honest about whether your investigations are broad and frequent or narrow and rare. **Host-heavy and topology-complex points toward Dynatrace; data-heavy and ownership-conscious points toward Coralogix.** Those two numbers will tell you more than any demo, and they will tell you before you have signed anything.
[summary]
### One thing neither platform covers: the full reliability layer
Neither Dynatrace nor Coralogix includes uptime monitoring, on-call scheduling with phone and SMS, incident management, and customer-facing status pages as a unified product. Better Stack brings all of that together with logs, metrics, and traces, with usage-based pricing and no per-seat or per-host fees.
**The full reliability lifecycle in one place. Start free, no credit card required.** [Try Better Stack.](https://betterstack.com)
[/summary]