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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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