by Coralogix · coralogix.com ↗
Log, metric, trace, and security observability platform with stream processing analytics.
Coralogix is a cross-stack observability platform built for teams that need to understand logs, metrics, traces, security signals, and AI workloads in one place. Its core promise is simple: help buyers get real-time visibility and long-term analytical value without the cost and operational overhead that often comes with heavy indexing, inflexible retention, or complex pricing. The product site positions Coralogix as AI-native and designed for modern teams that want to investigate faster, correlate across signals, and keep telemetry in the customer’s own cloud.
For buyers, the appeal is not just broader visibility, but a more controllable operating model. Coralogix emphasizes usage-based pricing, unlimited users and hosts, and the ability to query archived data directly from storage. That makes it especially relevant for organizations with growing telemetry volumes, strict compliance requirements, or a need to scale observability across large engineering and security teams. Across its own comparison pages, Coralogix repeatedly highlights cost predictability, support quality, and speed to insight as reasons teams switch from incumbent platforms.
Coralogix positions itself as a single data engine for teams that need to correlate logs, metrics, traces, security events, and AI-related workloads without switching tools. The platform emphasizes in-stream analysis, reusable datasets, and cross-signal correlation so users can move from raw telemetry to investigation and reporting in one workflow. This makes it appealing to operations, platform, and security teams that want broad visibility with fewer tradeoffs.
Coralogix says its Streama technology analyzes data as it arrives, which helps teams work with live telemetry rather than waiting for later processing. The platform also describes this approach as reducing reliance on heavy indexing and enabling long-term trend analysis.
The product website frames Coralogix as a platform that connects observability across application, security, and AI layers. Reusable datasets and unified querying are presented as ways to correlate events across the stack and answer questions faster.
Coralogix includes security capabilities alongside observability, and its docs also describe AI features billed separately through AI Units. The combination suggests buyers can centralize operational, security, and AI monitoring in one environment while tracking usage independently.
Coralogix consistently presents itself as a cost-optimized observability platform built around data volume and selective routing rather than per-seat or resource-heavy pricing. The company highlights no feature tiers, unlimited users and hosts, and the ability to query archived data without rehydration. For buyers trying to keep observability spending predictable as usage grows, these capabilities are a major part of the value proposition.
The pricing page says customers pay for data and nothing more, with logs, traces, metrics, and AI priced by consumption. Coralogix also states that units are its currency system, designed to keep pricing consistent as needs shift.
Coralogix says all data is written to the customer’s S3 bucket and can be queried directly from the UI without additional cost. That makes the platform attractive for teams that need long retention for compliance, analytics, or historical investigation.
The official pricing page states that every account includes unlimited sources, unlimited users, and unlimited hosts. This removes several common scaling constraints and makes the platform easier to extend across larger organizations.
Coralogix places notable emphasis on service and operational scale. Across official pages, the company says support is included with every account, response times are measured in seconds, and deployment can support very large event volumes and application counts. It also highlights enterprise features like RBAC, SSO, audit controls, and compliance support, which will matter most to regulated or distributed teams.
Coralogix states that every account includes 24/7 human support, and its pricing page says support from real software engineers is part of the package. This is useful for teams that want fast help without paying extra for premium tiers.
The pricing page lists RBAC, SSO with SAML, audit trail and IP access control, cross-team quota management, and full security and compliance controls. These features suggest the platform is designed for larger teams that need governance as well as visibility.
Coralogix says it can process more than 3M events per second across 500K+ applications worldwide, and its careers page adds claims about 50GB+ processed per second, 200K+ applications monitored, and 100PB+ data managed. Those scale markers indicate a platform aimed at demanding production environments.
Engineering leader responsible for reliability, budget predictability, and observability strategy.
Operator who needs fast incident response, broad telemetry correlation, and dependable support.
Buyer focused on auditability, access controls, and long-term retention of security-relevant data.
Coralogix presents itself as a cross-stack observability platform built for application, security, and AI layers. The company emphasizes AI-native investigation, real-time insight, stream analytics, and a cost-optimized architecture that stores telemetry in the customer’s cloud and supports long-term retention.
Trusted by over 4,000 teams worldwide.
The pricing page says Coralogix can process 3M+ events per second across 500K+ applications worldwide.
The careers page says Coralogix has 30K+ daily users and 100PB+ data managed.
Coralogix’s public pricing is deliberately simple: instead of a long list of named seat tiers, the company publishes usage-based rates for the main telemetry types it processes. The official pricing page says “Fair, fixed pricing for all” and explains that customers “pay for your data and nothing more.” For buyers, the main question is not which seat level to choose, but how much log, trace, metric, and AI usage they expect to ingest. Coralogix also emphasizes that all features and support are included, with unlimited users, unlimited hosts, unlimited sources, and enterprise features on every account. That makes the pricing story easier to evaluate for teams that want cost tied to usage rather than headcount. The public docs further show how the AI product is billed separately through AI Units, with free monthly allowances on free plans and token-based rates for paid use. If you are building an observability budget, the biggest cost drivers will be ingestion volume, AI token usage, and any separate cloud storage costs for the customer-owned S3 bucket.
In Coralogix’s own comparison content, the most common alternatives are Datadog, New Relic, Splunk, Grafana Labs, Dynatrace, Elastic Observability, and Honeycomb. The recurring competitive themes are predictable pricing, lower cost at scale, real-time analytics, support quality, and the ability to retain and query data without rehydration or heavy indexing.
These candidates come from measured co-mentions or source-backed alternatives. A full comparison is published only after both products have supporting evidence.
Coralogix presents itself as an AI-native observability platform with a pricing model that is unusually explicit about how customers pay. In the supplied documents, the company emphasizes usage-based billing, included support, and enterprise features rather than forcing buyers to piece together separate add-ons. That makes the product page especially relevant for teams evaluating observability tools on both capability and cost control.
From a review and marketplace perspective, the available sources are more informative about positioning than they are about detailed public ratings. The fetched Software Advice, Capterra, and G2 pages confirm that Coralogix is listed across major software marketplaces, but the text provided here does not expose robust review-count or star-rating data. Because of that, the strongest signals in this dataset come from the official pricing page and homepage, where Coralogix claims scale, retention, and support advantages that align well with enterprise buyers.
The clearest buyer-fit story is for teams that want to ingest logs, metrics, traces, and AI-related workloads under one platform, while keeping spend tied to data volume. The documents also suggest appeal for security and platform teams that value RBAC, SSO, audit trails, compliance controls, and unlimited users and sources. If you are looking for a simple, high-scale observability stack with a strong cost narrative, Coralogix’s supplied materials make that case directly.
Examples of how assistants answer real buyer questions in this category.
View all Observability prompts →I'll search for current information about observability platforms that offer unified metrics, logs, and traces capabilities.Let me search for more specific information about the major established platforms to provide a balanced recommendation.Based on my research, here are the observability platforms that support metrics, logs, and traces in one unified product: ## Enterprise SaaS Platforms Datadog is a top choice for cloud-native enterprises that want a unified SaaS platform combining APM, infrastructure monitoring, RUM, and security observability. It has expanded well beyond its original APM roots into a full observability stack covering infrastructure metrics, logs, traces, real user monitoring, synthetics, and security. New Relic offers a unified SaaS observability experience combining logs, metrics, traces, and synthetics under one pricing model. New Relic's NRDB (New Relic Database) stores all signal types in a unified telemetry database, and the Basic tier provides 100 GB of ingested data per month and one full-platform user, indefinitely. Dynatrace continues to serve large enterprises that prioritize automation and deep analytics, with its Davis AI engine correlating billions of metrics and events to surface root causes automatically. ## Open Source & Cost-Effective Options Grafana Cloud is powered by Grafana Labs' open source projects – Grafana for visualization, Loki for logs, Mimir for metrics, and Tempo for traces, providing a managed unified experience. SigNoz is a rapidly growing open-source alternative to platforms like Datadog and New Relic, built on OpenTelemetry and using ClickHouse for storage, offering a unified solution for metrics, traces, and logs in a single application. Elastic Observability integrates data from logs, metrics, traces, and uptime information, aggregating and visualizing operational data in real time by providing a unified platform to streamline incident response. Splunk Observability Cloud
Several observability platforms support metrics, logs, and traces within a single product, enabling unified visibility and faster root cause analysis by correlating these signals.[](https://coralogix.com/guides/observability/observability-platforms/) [ ](https://coralogix.com/guides/observability/observability-platforms/)[ ](https://www.linkedin.com/posts/bhavya-bojanapalli-1b29671a1_devops-elk-grafana-activity-7297849890909392898-CLig) Here are the top platforms that support this unified approach as of 2026: Leading Commercial Platforms - Datadog: A cloud-based platform providing a unified view of metrics, logs, and distributed tracing, often considered a leader for all-in-one monitoring. - [Splunk Observability Cloud](https://www.splunk.com/en_us/blog/learn/observability.html): Combines high-cardinality metrics, logs, and tracing (formerly SignalFx) to provide real-time visibility into cloud and hybrid environments. - Dynatrace: An enterprise-focused, AI-driven platform that automatically maps and monitors application performance, logs, and metrics, often using [Dynatrace OneAgent](https://www.dynatrace.com/news/blog/what-is-observability-2/). - New Relic: A full-stack observability platform that provides deep insights through a comprehensive, unified interface. - Elastic Observability: Built on the [Elastic Stack (ELK)](https://www.elastic.co/blog/3-pillars-of-observability) , it brings logs, metrics, and traces together with strong open-source roots. - Honeycomb: Specialized for high-cardinality, high-dimensionality event data (logs, traces, metrics) in complex distributed systems. - Coralogix: A platform that unifies logs, metrics, and traces, focusing on in-stream data analysis.[](https://www.velodb.io/blog/best-observability-tools) [ ](https://www.velodb.io/blog/best-observability-tools)[ ](https://www.cloudthat.com/resources/blog/top-10-observability-tools)[ ](https://www.cybernx.com/top-7-full-stack-observability-tools/)[
Vendors compare on OpenTelemetry (OTel) support `based on how natively they ingest OTLP (OpenTelemetry Protocol) data, the quality of their OTel Collector distributions, and how well their UI/backend preserves OTel semantic conventions` . Almost all major observability vendors now support OTel, but they differ in whether they are "OTel-native" (built around the standard) or "OTel-compatible" (translating OTel data into proprietary formats).[](https://openobserve.ai/blog/opentelemetry-backends-otlp-support/) [ ](https://openobserve.ai/blog/opentelemetry-backends-otlp-support/)[ ](https://www.datadoghq.com/knowledge-center/opentelemetry/) Key Comparison Areas for OpenTelemetry Support - Ingestion (Native vs. Translation): - Native Support: Vendors like Honeycomb, Lightstep, and newer tools like [SigNoz](https://signoz.io/) or [Dash0](https://www.dash0.com/) ingest OTLP directly, preserving data structure and attributes without translation. - Translation Support: Legacy SaaS vendors such as Datadog, New Relic, and Dynatrace typically use the OpenTelemetry Collector to translate OTel data into their own proprietary formats. This can sometimes lead to data fidelity loss or altered attributes.[](https://medium.com/dzerolabs/unpacking-observability-how-to-choose-an-observability-vendor-aa0e6d80b71d) [ ](https://medium.com/dzerolabs/unpacking-observability-how-to-choose-an-observability-vendor-aa0e6d80b71d)[ ](https://openobserve.ai/blog/opentelemetry-backends-otlp-support/) - Collector Distributions: Many vendors offer customized OTel Collector distributions (e.g., AWS ADOT, Grafana Alloy, Splunk Distribution, Datadog DDOT). These are often preferred for stability and tested compatibility, but they may limit flexibility by bundling specific components.[](https://oneuptime.com/blog/post/2026-02-06-choose-upstream-vs-vendor-opentelemetry-collector/view) [ ](https://oneuptime.com/blog/post/2026-02-06-choose-upstream-vs-vendor-opentelemetry-collector/v
I'll search for current information about ingestion pricing models for logs and traces in observability platforms.Based on current market information, here's how ingestion pricing models work for logs and traces in observability platforms: ## How Ingestion Pricing Works Observability vendors use several distinct pricing models for logs and traces: ### Per-GB Ingestion Models Platforms like Observe charge per GB of data ingested, with rates varying by signal type: $0.49/GB for logs and $0.59/GB for traces. Coralogix uses a usage-based, per-GB pricing model with no per-user or per-host fees, where pricing is based on the volume of data ingested. Google Cloud Observability prices trace spans by ingested volume, while New Relic charges per-GB of data ingested ($0.40/GB Original Data, $0.60/GB Data Plus) after a free 100 GB/month tier. ### Dual-Layer Pricing (Ingestion + Indexing) Datadog logs use two billing layers: you pay $0.10/GB for ingestion regardless of whether you ever query those logs, then you pay $1.70 per million events to index them, making them searchable. This means you're charged twice—once to receive the data and again to make it queryable. ### Hybrid Models (Host + Data Volume) SUSE Cloud Observability lists a base fee of $99/month for 10 hosts, $9.99/host/month for 10–100 hosts, and $8.99/host/month above 100 hosts, plus includes 5GB each of logs, metrics, and traces, then charges $0.15/GB for additional usage. ### Trace-Specific Pricing For APM, Datadog charges based on the volume of traces ingested, and bills separately for indexed spans, which are searchable for analytics, at $1.70 per million spans per month. ## Key Recommendations For high-volume, cost-conscious teams: Consider Observe or Coralogix. Both offer straightforward per-GB ingestion pricing without per-host or per-user fees, making costs predictable as you scale. For teams wanting generous free tiers: New Relic offers the first 100GB of data
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