Better Stack vs Sumo Logic: A Complete Comparison for 2026

Security teams and DevOps teams often end up on Sumo Logic through the same path: they needed a log management platform, appreciated the SIEM angle, and either grew into the security features or chose it specifically for Cloud SIEM. It's a legitimate product with a 15-year track record in log analytics, an expanding AI platform in Dojo AI, and a compliance portfolio that covers FedRAMP, HIPAA, and PCI DSS.

But here's the tension at the heart of this comparison: Sumo Logic's pricing model has changed fundamentally over the past few years, from ingest-based to scan-based Flex Pricing, and the shift introduces costs that catch teams off guard. You no longer pay to put data in. You pay every time you query it. For teams that run frequent dashboards, monitors, and ad-hoc investigations, scan costs accumulate in ways that make budgeting genuinely difficult.

Better Stack approaches observability from a different axis entirely: unified logs, metrics, traces, error tracking, incident management, and status pages under one pricing model based on data volume, where 100% of ingested logs are immediately searchable with no scan fees, no cardinality penalties, and no indexing gates.

This comparison covers both platforms honestly. Sumo Logic has real strengths, especially in security operations, and acknowledging that makes the rest of the analysis worth reading.

Quick comparison at a glance

Platform architecture

Understanding what each platform is built to do clarifies nearly every other comparison point. Sumo Logic originated as a log management and SIEM platform. Its observability capabilities (APM, infrastructure monitoring, RUM) exist as additions on top of that foundation. Better Stack originated as an observability and incident management platform. Security is not currently part of the product.

Better Stack: unified observability

Better Stack collects logs, metrics, and traces through a single eBPF-based collector that operates at the kernel level. Once deployed, it auto-discovers services, instruments database calls, and begins capturing distributed traces without requiring any changes to application code. The same data warehouse stores all telemetry types, queryable through SQL or PromQL from a single interface.

What does that mean in practice? When an alert fires, the interface shows you service maps, related log lines, metric anomalies, and trace examples together. There's no switching between products to assemble the picture. Watch how Better Stack's collector gets up and running:

The eBPF collector runs as a DaemonSet on Kubernetes nodes and discovers services automatically. HTTP and gRPC traffic between services, database queries to PostgreSQL, MySQL, Redis, and MongoDB, and host-level metrics all start flowing within minutes of deployment.

OpenTelemetry-native, zero vendor lock-in. Better Stack treats OpenTelemetry as the primary collection format, not an alternative path. Your instrumentation stays in an open standard, which means changing your observability vendor is a configuration change, not a migration project.

Sumo Logic: logs-first with security at the core

Sumo Logic's architecture reflects its history. The platform's strongest layer is log analytics, built on a cloud-native architecture that has processed logs at scale since 2010. SIEM, anomaly detection, and behavioral analytics sit on top of this log foundation. Observability features (distributed tracing, infrastructure monitoring, RUM) were added over time and work well for many teams, but the depth of integration you get between SIEM and logs is not matched between APM and logs.

The platform has two distinct product lines marketed separately: Intelligent Security Operations (Cloud SIEM, Logs for Security, Cloud SOAR) and Intelligent Cloud Operations (monitoring, troubleshooting, APM). Teams that need both tend to buy the Enterprise Suite. Teams that only need observability often find they're paying for security capabilities they don't use.

Sumo Logic collects data via installed collectors (Installed Collectors for on-premises, Hosted Collectors for cloud services) and OpenTelemetry. The OpenTelemetry support is genuine and works well for teams already invested in OTel pipelines.

Pricing comparison

This is where the comparison gets complicated, and it's worth spending time here because Sumo Logic's pricing model has changed in ways that are not immediately obvious from the website.

Better Stack: predictable volume-based pricing

Better Stack charges based on actual data volume with no scan fees, no cardinality penalties, no indexing tiers, and no per-user charges. The formula is simple:

Pricing structure:

• Logs: $0.10/GB ingestion + $0.05/GB/month retention (100% searchable, no scan costs)
• Traces: $0.10/GB ingestion + $0.05/GB/month retention
• Metrics: $0.50/GB/month (no cardinality penalties)
• Error tracking: $0.000050 per exception
• Responders: $29/month (unlimited phone/SMS alerts)
• Monitors: $0.21/month each

100-host deployment example: $791/month

• Telemetry (2.5TB/month): $375
• 5 Responders: $145
• 100 Monitors: $21
• Error tracking (5M exceptions): $250

Query that data a thousand times or once: the price is the same.

Sumo Logic: scan-based Flex Pricing

Sumo Logic's current default model for new customers is Flex Pricing. The headline is compelling: $0 ingest, unlimited log ingestion. But the mechanics deserve careful attention.

Under Flex, you pay for data scanned, not data ingested or stored. Every dashboard load, every monitor evaluation, every ad-hoc log search consumes scan credits. The pricing page shows an estimated $3.14 per TB scanned for a mid-range analytics profile (750-1,500 scans per GB ingested). For a high-analytics profile (1,500-2,000 scans per GB), the rate increases.

What does that look like practically? If you ingest 2.5TB of logs per month and run typical dashboards and monitors, you might scan that data 10x over the course of the month, paying on 25TB of scans. The $0 ingest headline obscures this.

There is also a retention cost, though it's not prominently disclosed. Sumo Logic's credit structure includes storage fees. The headline "free ingest" is accurate for the ingestion step but does not mean the data is free to hold.

For tracing, Sumo Logic uses a credits model: 35 credits per GB per day. On the Enterprise Suite, one credit costs roughly $0.36 at MSRP list price. Ingesting 10GB/day of traces for a year costs approximately 127,750 credits, which works out to around $46,000/year at list price before negotiation.

Older tiered plans (Continuous, Frequent, Infrequent tiers) still exist for some customers on the Essentials package, where the pricing page shows approximately $3/GB for Continuous-tier log search.

The honest summary: Sumo Logic's Flex Pricing rewards teams that ingest lots of data but query it infrequently (compliance archiving, security audit trails, infrequent investigation). Teams that run frequent dashboards, many monitors, and regular investigations against large data volumes will see higher scan costs than a simple per-GB model would suggest. Budget predictability is harder because costs depend on query patterns, not just data volume.

Is your team running dashboards that refresh every few minutes? Every one of those refreshes scans your data and consumes credits.

Cost comparison: 3-year TCO

For a 100-host deployment over 3 years, comparing observability (excluding SIEM which Sumo Logic provides and Better Stack does not):

Note: Sumo Logic's Cloud SIEM, if needed, adds significant additional cost at Enterprise Suite pricing. Better Stack does not have a SIEM product. If SIEM is a core requirement, Sumo Logic offers something Better Stack does not.

Monitoring and troubleshooting

Sumo Logic's observability capability was historically secondary to its SIEM, but the platform has expanded meaningfully. Its APM, infrastructure monitoring, and Kubernetes observability are production-grade. The honest question is whether you want to pay for security capabilities you don't need in order to access the observability features.

Better Stack: zero-instrumentation observability

Better Stack's APM uses eBPF to capture distributed traces automatically, without SDK installation, code changes, or per-service configuration. Deploy the collector to Kubernetes via a single Helm chart and traces start flowing:

Frontend-to-backend correlation connects browser sessions to backend traces in a single view. When a page load is slow, the trace shows exactly which backend services and database calls contributed, without switching products or manually linking RUM and APM contexts.

OpenTelemetry-native, zero lock-in. All traces use the OTel format. If you ever want to route data elsewhere, you change a configuration line. No migration cost, no proprietary SDK dependency. What's the migration tax you're currently paying if you're locked into Sumo Logic's collection format?

Better Stack's APM covers polyglot environments naturally: Python, Go, Java, Ruby, Node.js, and others are instrumented via the same eBPF collector, with no per-language SDK to version-manage.

Sumo Logic: application observability with deep AWS integration

Sumo Logic's APM uses OpenTelemetry and its own collectors to capture distributed traces. Service maps give you topology views of your application, and the APM product links traces to log data reasonably well within the platform. The OTel support is genuine.

Where Sumo Logic's observability genuinely shines is AWS-native monitoring. The platform has deep integrations with AWS CloudTrail, CloudWatch, S3, GuardDuty, and dozens of other AWS services. If your infrastructure is heavily AWS-native and you want operational and security visibility together, Sumo Logic's ecosystem of AWS apps provides pre-built dashboards and detection rules that would take significant effort to replicate elsewhere.

Kubernetes monitoring is another area of legitimate strength. Sumo Logic's Kubernetes Observability feature provides pod-level monitoring, log correlation, and OTel instrumentation with pre-built dashboards covering cluster health, workload performance, and node resource usage.

The honest limitation: Sumo Logic's proprietary query language (SPL-like syntax) has a meaningful learning curve. G2 reviews and Gartner Peer Insights consistently cite query complexity as the most common friction point. Teams building complex aggregation queries or training new engineers face real overhead. Mobot (Dojo AI's conversational interface) helps, but it's SIEM-focused rather than general observability-focused.

Log management

Log management is the area where Sumo Logic has the longest track record and the clearest product investment. The platform was built on log analytics, and that heritage shows in features like LogReduce, LogCompare, and LogExplain, which are genuinely useful for making sense of large log volumes. The question is what you pay to access those logs during an investigation.

Better Stack: all logs, always searchable

Better Stack logs stores 100% of ingested logs as structured data in a single warehouse. There is no tiering, no indexing decision, and no scan cost. Every log line is immediately searchable from the moment it arrives, using familiar SQL syntax:

SELECT
  service_name,
  COUNT(*) as error_count,
  AVG(duration_ms) as avg_duration
FROM logs
WHERE level = 'error'
  AND timestamp > NOW() - INTERVAL '1 hour'
GROUP BY service_name
ORDER BY error_count DESC

Real-time streaming via Live Tail shows log events as they arrive, with filters that apply instantly. No rehydration, no waiting, no credits consumed for just watching what's happening in production right now.

You can also build charts and dashboards directly from log queries:

Pricing transparency: $0.10/GB ingestion + $0.05/GB/month retention. A service producing 100GB monthly costs $10 ingestion + $5 retention = $15 total. That's what you pay whether you run one query or ten thousand queries against those logs that month.

Sumo Logic: powerful log analytics with scan costs

Sumo Logic's log analytics capabilities are genuinely strong. LogReduce automatically clusters log lines into patterns, surfacing anomalies without requiring you to write queries for every possible error. LogCompare lets you diff log patterns across time windows, which is useful when something breaks after a deployment. These are real productivity features.

Live Tail streams logs in real time and is a good operational tool. The platform also offers rich pipeline capabilities: log parsing, field extraction, masking, and routing to different tiers based on content.

The scan cost reality: Under Flex Pricing, every log search, every dashboard refresh, and every monitor evaluation consumes scan credits. Sumo Logic provides tools to manage this (ingest budgets, partition-scoped queries, default scope optimization), but the cognitive overhead of optimizing queries for cost is friction that Better Stack's model eliminates. G2 reviewers consistently mention the need to understand data tiers and query patterns to control costs.

Log tiers on older plans (Continuous, Frequent, Infrequent) require upfront decisions about which logs deserve interactive search access and which go to cheaper, slower tiers. This is the same core tradeoff as Datadog's indexed/archived split. If you put logs in the wrong tier, you're waiting for rehydration during an incident. If you put everything in Continuous, you're paying a premium.

SIEM-connected log analytics is Sumo Logic's genuine differentiator: security-relevant logs feed directly into the Cloud SIEM detection engine, correlating with entity timelines, threat intelligence feeds, and MITRE ATT&CK rules. If your log management and security operations need to be one system, this integration is difficult to replicate elsewhere.

Infrastructure monitoring

Better Stack: no cardinality, no surprises

Better Stack metrics charges by storage volume, not by unique metric combinations. You can add any number of high-cardinality tags (customer ID, deployment version, feature flag) without triggering cost explosions. Full PromQL support means teams already running Prometheus pipelines don't need to learn new query syntax.

For teams that prefer drag-and-drop over writing queries:

Metrics, logs, and traces share the same storage and query interface. Correlating a CPU spike with a log error and a slow trace is a matter of changing the query type, not switching products.

Sumo Logic: metrics with Kubernetes depth

Sumo Logic metrics are measured in Data Points per Minute (DPM) and billed accordingly. The platform supports Prometheus-format metrics via OTel and has good visualization tools. Metrics can be correlated with logs in dashboards, which works reasonably well for operational use.

Where Sumo Logic's infrastructure monitoring earns its reputation is multi-cloud and Kubernetes observability. Pre-built apps for AWS, GCP, and Azure provide immediate visibility into cloud services with pre-configured dashboards, alerts, and anomaly detection. The Kubernetes monitoring feature correlates pod logs, metrics, and traces in a unified view that teams running complex Kubernetes environments genuinely value.

Metrics-based SLOs allow reliability tracking against business outcomes, with alerting when error budgets are at risk. This is a solid feature for engineering teams that have committed to reliability engineering practices.

Metrics capacity on Essentials is capped at 50,000 DPM per day. Teams exceeding that limit require the Enterprise Suite or negotiated overages.

Intelligent security operations and Cloud SIEM

This section is about an area where Sumo Logic has a clear, substantial advantage. If security operations are a primary driver for your platform choice, that matters.

Sumo Logic: Cloud SIEM and Cloud SOAR

Sumo Logic Cloud SIEM is a mature, production-grade security information and event management platform. It processes logs from across your infrastructure, applies detection rules aligned to the MITRE ATT&CK framework, and surfaces correlated Insights (Sumo Logic's term for grouped security events) that represent genuine threats rather than raw alerts.

900+ out-of-the-box detection rules cover network, identity, endpoint, and cloud threat patterns. The Insight Rules Engine reduces alert fatigue by grouping related signals and scoring them using entity-level risk. You're investigating correlated incidents, not reacting to individual log events.

Dojo AI's Summary Agent generates natural language summaries of each Insight, explaining what happened, what signals triggered the rule, and what the likely scope is. This reduces the time an analyst spends assembling context before starting an investigation.

UEBA (User and Entity Behavior Analytics) builds behavioral baselines for users and devices, flagging deviations that simple threshold rules miss. Insider threat detection, compromised credential use, and lateral movement patterns surface without requiring manual rule authoring.

Cloud SOAR provides playbook-based automation for incident response. Playbooks trigger on Insights, execute enrichment actions (threat intelligence lookups, user directory queries), and can take automated containment steps. Case Manager provides collaborative investigation workspaces with full audit trails.

Entity timeline and relationship graph let analysts pivot from a suspicious entity (a user, IP, or resource) into its full history and associated entities. This is meaningful for understanding blast radius before making containment decisions.

FedRAMP authorized and HIPAA compliant. For teams in government, healthcare, or financial services, this compliance coverage is not easily replicated.

Is your security team managing SIEM alerts manually today? That's the exact workflow Dojo AI's SOC Analyst Agent is built to partially automate.

Better Stack: observability-focused security posture

Better Stack is SOC 2 Type II compliant and GDPR compliant. Data is stored in DIN ISO/IEC 27001-certified data centers with AES-256 encryption at rest and TLS in transit. SSO via Okta, Azure AD, and Google is available, along with SCIM provisioning, RBAC, audit logs, and regular third-party penetration testing.

Better Stack does not have a SIEM, threat detection rules, UEBA, or SOAR automation. If your requirements include active threat detection, correlated security incidents, or automated response playbooks, Better Stack is not the platform for that today.

What Better Stack does provide is full observability of your infrastructure and applications, which gives security teams the log access and query capabilities they need for audit, compliance logging, and incident investigation. It's not a SIEM substitute, but it's a competent log source and observability layer.

Incident management

Better Stack: complete incident lifecycle

Better Stack incident management covers the full on-call workflow: alerting, escalation, on-call scheduling, Slack-native incident channels, post-mortems, and status pages, all at $29/month per responder with unlimited phone and SMS.

Incidents create dedicated Slack channels automatically, with investigation tools available directly inside Slack so engineers don't need to leave their collaboration environment:

On-call scheduling includes timezone-aware rotation management and automatic handoffs:

Post-mortems are generated automatically from incident timelines, giving your team a structured starting point without manual assembly work:

For enterprise teams with complex escalation logic, multi-tier policies with time-based rules and metadata filters handle even sophisticated on-call configurations:

Sumo Logic: monitoring without on-call management

Sumo Logic's alerting is strong: monitors fire on metrics, logs, traces, and security events, with AI-driven anomaly detection that surfaces issues without requiring manual threshold tuning. The platform integrates with PagerDuty, OpsGenie, Slack, and Microsoft Teams for notification delivery. For security alerts, the SOAR playbooks can automate response actions.

What Sumo Logic does not include is a native on-call scheduling and escalation system. Teams using Sumo Logic for operational monitoring need a separate tool (PagerDuty at $25-50/user/month, or OpsGenie) for on-call rotation management, phone/SMS delivery, and multi-tier escalation policies.

If your team is paying for both Sumo Logic and PagerDuty today, that's a combination Better Stack consolidates into a single bill.

Status pages and customer communication

Better Stack: built-in, synced status pages

Better Stack Status Pages sync automatically with incident management. When you declare an incident, the status page updates. Subscriber notifications go out via email, SMS, Slack, and webhooks without manual steps:

Custom domains, full branding control, private pages with SSO or password protection, and multi-language support are available. Scheduled maintenance windows publish automatically to subscribers.

Sumo Logic: no native status pages

Sumo Logic does not include a status page product. Teams that need to communicate publicly during incidents use separate tools (Statuspage.io, Atlassian Statuspage, or others) and manage the integration manually. This is a gap that Better Stack closes entirely.

Real user monitoring

Better Stack: unified RUM

Better Stack RUM captures Core Web Vitals, session replays, JavaScript errors, and website analytics. Because RUM data lives in the same warehouse as your backend traces and infrastructure metrics, frontend sessions correlate directly to backend service performance without any cross-product configuration.

Session replays run at 2x speed with automatic pause-skipping. Frustration signals (rage clicks, dead clicks) filter down to the sessions worth watching. PII is excluded at the SDK level. When a JavaScript error occurs during a session, the replay links directly to the stack trace and the distributed backend trace in one view.

Pricing: $0.00150/session replay, included in the same volume-based billing model as logs and metrics.

Sumo Logic: Real User Monitoring available

Sumo Logic does offer RUM (Real User Monitoring) as part of its observability suite, covering web applications with Core Web Vitals tracking, error monitoring, and browser performance metrics. The RUM data integrates with Sumo Logic's APM traces, so frontend slowness can be correlated to backend service behavior within the platform.

What Sumo Logic's RUM does not currently emphasize as strongly is session replay and product analytics. The focus is on performance metrics and error rates rather than user journey recordings. Teams that need deep session replay capabilities should evaluate whether Sumo Logic's RUM meets that requirement.

Dojo AI vs Better Stack AI

AI is a serious investment at both companies, but the focus differs significantly. Better Stack's AI is incident-oriented, activating during production issues to accelerate resolution. Sumo Logic's Dojo AI is security-oriented, focused on reducing SOC alert fatigue and accelerating threat investigations.

Better Stack: AI SRE and MCP server

AI SRE activates autonomously when an incident fires. It queries your service map, reviews recent deployments, analyzes log patterns, and surfaces a root cause hypothesis before you've even started your own investigation. At 3am, starting from a hypothesis rather than a blank screen is a meaningful difference.

Better Stack MCP server is generally available to all customers. Connect Claude, Cursor, or any MCP-compatible AI client directly to your observability stack:

{
  "mcpServers": {
    "betterstack": {
      "type": "http",
      "url": "https://mcp.betterstack.com"
    }
  }
}

From your AI client, you can ask "what monitors are currently firing?", "who's on-call right now?", "show me the top error sources in the last hour", or "build a dashboard for HTTP 5xx rates by service". The MCP server covers monitoring, incidents, logs, metrics, dashboards, error tracking, and on-call scheduling.

Sumo Logic: Dojo AI for security operations

Sumo Logic's Dojo AI platform is security-first. Its conversational interface, Mobot, lets analysts query Sumo Logic data in natural language. The Query Agent translates natural language into Sumo Logic Query Language automatically, reducing the friction of the platform's custom syntax.

Summary Agent is generally available and adds real value for SIEM users: when a Cloud SIEM Insight fires, the Summary Agent generates a natural language explanation of what happened and which signals triggered the rule, so analysts start with context instead of raw data.

SOC Analyst Agent (currently in beta) automates alert triage by evaluating severity, correlating related signals, and summarizing incident scope. This is aimed at reducing Tier-1 manual work in security operations.

Knowledge Agent answers how-to questions about Sumo Logic itself, drawing from documentation. It's genuinely useful for onboarding and reducing support ticket volume.

Sumo Logic MCP Server was announced in prototype form in December 2025, with general availability targeted for 2026. It is currently in limited beta with select customers. If you're evaluating MCP as a production capability today, Better Stack is the only platform where it's generally available and not gated by an allowlist.

What's the practical implication? If your AI workflow involves asking Claude or Cursor to investigate production incidents, Better Stack's MCP server is production-ready today. If your workflow involves AI-assisted security triage inside a SIEM, Dojo AI is the more mature offering, even if the MCP server itself is still limited beta.

Deployment and integrations

Better Stack

Deploy the eBPF collector via a single Helm chart. Automatic service discovery starts flowing traces, metrics, and logs within minutes. No per-service instrumentation, no SDK management, no per-language library versions to track.

For teams already using OpenTelemetry:

For Vector-based log pipelines:

Better Stack connects natively to 100+ covering all major stacks: MCP, OpenTelemetry, Vector, Prometheus, Kubernetes, Docker, PostgreSQL, MySQL, Redis, MongoDB, Nginx, and more.

Sumo Logic

Sumo Logic's integration catalog genuinely spans 2,000+ applications: cloud providers (AWS, GCP, Azure, multi-cloud), identity providers (Okta, Azure AD), endpoint detection, network security, SaaS applications, and databases. The breadth is a real strength for security teams that need to normalize logs from dozens of disparate sources into a single detection platform.

Deployment uses Installed Collectors (for on-premises log sources), Hosted Collectors (for cloud sources), and OpenTelemetry-based collection. The variety of collection methods means most enterprise environments can get data flowing without significant customization.

The integration depth for AWS specifically is exceptional: CloudTrail, GuardDuty, Security Hub, VPC Flow Logs, S3 access logs, CloudWatch, and dozens of other AWS services have pre-built apps with detection rules, dashboards, and recommended alert configurations.

How many distinct log sources does your organization manage today? If the answer is "dozens, across different security and infrastructure tools," Sumo Logic's integration catalog may cover more of them with less custom work than Better Stack's 100+ focused integrations.

Error tracking

Better Stack

Better Stack Error Tracking accepts Sentry SDK payloads, so teams already using Sentry's SDKs can point them at Better Stack without rewriting instrumentation.

AI-native debugging integrates with Claude Code and Cursor through pre-made prompts that summarize error context. The workflow: copy the prompt, paste into your AI coding assistant, get a root cause hypothesis without manually reading stack traces.

Full trace context surfaces the distributed trace that preceded each exception, automatically linked without configuration.

Sumo Logic

Sumo Logic does not have a dedicated error tracking product. Application errors appear in log analytics and can be monitored via log-based monitors and LogReduce pattern analysis. Teams that need structured error tracking (grouping by fingerprint, trend analysis, deployment correlation, session replay linkage) typically use Sentry or a separate error tracking tool alongside Sumo Logic.

Enterprise readiness

Both platforms cover standard enterprise security and access control requirements. The differences are meaningful in regulated industries.

Better Stack's enterprise tier includes SSO via Okta, Azure AD, and Google; SCIM provisioning; RBAC; audit logs; data residency in EU and US with an optional self-hosted S3 bucket option; enterprise SLAs; a dedicated Slack support channel; and a named account manager.

Sumo Logic's enterprise compliance is broader: SOC 2 Type II, GDPR, HIPAA, FedRAMP, PCI DSS, and ISO certifications. The platform also offers multi-org support, which matters for MSSPs managing multiple customer environments. Unlimited users is a headline feature (no per-seat charges for platform access) that genuinely removes a procurement friction point for large organizations.

For most enterprise teams outside heavily regulated industries, Better Stack's compliance and access control portfolio covers procurement requirements at a fraction of Sumo Logic's Enterprise Suite cost. If your compliance team is checking boxes for FedRAMP or HIPAA, Sumo Logic has the certifications and Better Stack does not.

Final thoughts

These two platforms are genuinely suited for different buyers, and the decision is not particularly close once you understand what each one is built to do.

Choose Better Stack if your primary needs are observability (logs, metrics, traces), incident management, error tracking, status pages, and cost predictability. You get full-stack visibility without per-seat charges, scan fees, indexing decisions, or cardinality anxiety. The eBPF collector removes instrumentation overhead. The MCP server connects your AI tooling to production data today, not sometime in 2026. At a 100-host scale, the TCO difference versus Sumo Logic's Enterprise Suite is substantial.

The honest gap to acknowledge: Sumo Logic's Flex Pricing rewards specific usage patterns but introduces query-cost unpredictability that teams doing frequent dashboards and investigations find frustrating. The G2 and Gartner Peer Insights reviews are consistent on this. If you're currently optimizing Sumo Logic queries or data tiers to control costs rather than to get better answers, that's engineering time you could spend elsewhere.

Ready to evaluate? Start your free trial or explore the Better Stack pricing model to see what a predictable observability bill actually looks like.

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