Best Application Performance Monitoring: What AI Recommends

For a HIPAA + ISO 27001 environment where no patient-adjacent telemetry can leave your boundary, the key architectural decision is usually less about “which APM UI is nicest” and more about how much of the observability stack you are willing to operate yourself.

Most compliance-heavy teams end up choosing between:

• A unified OpenTelemetry-native platform (simpler operator experience)

• A composable CNCF-style stack (more control, more operational burden)

• A commercial self-managed product (enterprise features, licensing cost)

Below is a comparison focused on private cloud/on-prem deployment complexity.

PlatformLogsMetricsTraces/APMDeployment complexityBest fitSigNoz✓✓✓MediumTeams wanting a Datadog-like experience without SaaS telemetryGrafana LGTM Stack✓✓✓HighPlatform teams comfortable operating Kubernetes-scale infrastructureElastic Observability✓✓✓Medium–HighOrganizations needing mature search, SIEM adjacency, enterprise controlsOpenSearch + telemetry components✓PartialPartialMedium–HighTeams already standardized on OpenSearchJaeger + Prometheus + logging stackDepends✓✓HighMaximum customization

1. SigNoz — lowest operational friction for a unified self-hosted APM

SigNoz is built around OpenTelemetry and provides correlated logs, metrics, traces, dashboards, alerts, and APM-style views in one product. Its self-hosted deployment keeps ingestion endpoints and telemetry under your control. SigNoz (+1 more citation)

Typical architecture:

Applications | OpenTelemetry SDKs | OpenTelemetry Collector | SigNoz | ClickHouse + storage

Operational profile

• Kubernetes deployment: moderate complexity

• VM/bare metal: easier than a full observability suite

• Upgrades: fewer moving parts than assembling separate products

• Developer adoption: generally easier because engineers get a single UI

Compliance advantages

• No telemetry leaves your network

• OpenTelemetry avoids vendor-specific agents

• Easier to document data flow for audits

Tradeoffs

• Smaller ecosystem than Elastic/Grafana

• You still own storage sizing, retention, backups, HA design

For an 80-engineer SaaS organization, this is often the “platform team owns the platform, developers consume it” model.

2. Grafana stack — maximum flexibility, maximum platform ownership

The Grafana ecosystem is the most common “build your own observability platform” approach:

• Grafana Loki → logs

• Prometheus or Grafana Mimir → metrics

• Grafana Tempo → traces

Grafana Tempo supports OpenTelemetry and integrates trace data with Grafana dashboards, metrics, and logs. Grafana Labs

Operational profile

Complexity: high

You operate:

• ingestion pipelines

• multiple storage engines

• retention policies

• scaling rules

• correlation between signals

A mature Kubernetes platform team can run this very well, but it becomes a product internally.

Strengths

• Very large ecosystem

• Excellent Kubernetes support

• Strong customization

• Avoids dependence on one vendor

Weaknesses

• More components to patch and secure

• More RBAC/auth integration work

• More tuning for production scale

This is usually best when your platform engineering team already runs Kubernetes infrastructure at scale.

3. Elastic Observability — enterprise search and security alignment

Elastic provides self-managed observability through the Elastic Stack. Its APM capabilities correlate application performance data with logs, metrics, service maps, and traces. YouTube

Operational profile

Complexity: medium–high

Typical components:

Elastic Agents / OpenTelemetry | Elasticsearch | Kibana

Strengths

• Excellent log search

• Mature access controls

• Strong audit/security ecosystem

• Many enterprises already operate it

Weaknesses

• Resource intensive

• Elasticsearch operations require expertise

• Licensing considerations matter for some enterprise features

For healthcare SaaS, Elastic is attractive when observability overlaps heavily with security monitoring and audit workflows.

4. Build-your-own OpenTelemetry stack

A common regulated-enterprise architecture is:

OpenTelemetry Collector | +--> Prometheus-compatible metrics backend | +--> Loki/OpenSearch logs | +--> Jaeger/Tempo traces

This gives maximum control.

Operational profile

Complexity: highest

You become responsible for:

• schema consistency

• cross-linking logs ↔ traces ↔ metrics

• dashboard standards

• developer onboarding

This approach works best if your platform team wants observability itself to be an internal platform product.

Deployment complexity ranking

From easiest to hardest:

• SigNoz

Single integrated experience

• Fewer decisions for application teams

• Elastic Observability

Mature but heavier operational footprint

• Grafana LGTM

Powerful, but you operate many moving parts

• DIY OpenTelemetry backend stack

Maximum control, maximum ownership

Questions I would use to narrow this for your environment

For an 80-engineer healthcare SaaS company, the deciding factors are likely:

QuestionIf yes, lean towardDo you already run Kubernetes heavily?Grafana stackDo security teams want observability + SIEM workflows together?ElasticDo developers need a Datadog-like APM experience quickly?SigNozDo you have dedicated SRE/platform staff to operate observability as a product?Grafana or DIYIs minimizing compliance audit scope a priority?Fewer components → SigNoz/Elastic

A common regulated architecture is also OpenTelemetry everywhere + self-hosted backend, because it lets you change backends later without rewriting application instrumentation. SigNoz (+1 more citation)

For your stated constraints (HIPAA boundary, ~80 engineers, private deployment), the shortlist I would evaluate in a proof-of-concept would usually be:

• SigNoz for operational simplicity

• Grafana LGTM for maximum platform control

• Elastic Observability if security/search requirements dominate

The POC should test: telemetry scrubbing/redaction, RBAC/SSO integration, audit logging, HA upgrades, retention costs, and whether developers can debug a production incident without platform engineers acting as translators.

For a mid-size B2B SaaS team already standardized on OpenTelemetry, the good news is you are looking at the right layer: you can replace many commercial APM vendors without changing instrumentation. The harder part is not dashboards or ingestion — it is retention economics. Keeping 18 months of traces is usually the cost driver, not the APM UI.

The short list I would evaluate:

PlatformBest fit18-month retention storyMain storage concernGrafana Tempo + Grafana Mimir + Grafana LokiTeams willing to assemble a stackExcellent for long retention because traces can live in object storageYou must operate multiple componentsSigNozTeams wanting a more integrated APM replacementGood if you tune ClickHouse retention and storage tiersClickHouse disk growth can surprise youElastic ObservabilityTeams already comfortable with ElasticsearchMature lifecycle controlsElasticsearch storage is expensive at long retentionUptraceSmaller teams wanting OTEL-native APMPossible with ClickHouse tuningSmaller ecosystem

Grafana Tempo is probably the strongest architectural match for your specific requirement because it was designed around storing traces cheaply. Tempo uses object storage for long-term trace data and supports Azure Blob Storage as a backend. Grafana Labs The pattern is typically: keep recent traces on fast storage, then age them into cheaper blob tiers.

SigNoz is attractive if you want a more “Datadog/New Relic-like” experience without assembling Grafana components. It is OpenTelemetry-native and uses ClickHouse as its telemetry store. SigNoz The catch is that ClickHouse is still a database you operate: replication, disks, merges, backups, and retention policies become your responsibility. SigNoz

Elastic Observability has very mature lifecycle tooling. Its index lifecycle management lets you automate rollover and deletion policies for APM data streams. elastic.co However, keeping high-cardinality traces for 18 months in Elasticsearch can become expensive because you are paying for indexed storage, not just compressed blobs. elastic.co

The retention trap: traces are not metrics

A common mistake is budgeting “18 months of telemetry” as one number.

A rough model:

Monthly storage = traces ingested/day + metrics samples/day + indexes/metadata overhead + replication factor + backups

In most SaaS environments:

• Metrics compress extremely well and are relatively cheap.

• Traces explode because every request creates spans.

• Logs often dominate everything if retained at full fidelity.

For example, if you ingest:

• 500 GB/day raw traces

• 30-day hot retention

• 18-month archive retention

You are not looking at a 500 GB problem. You are looking at:

• Hot tier: ~15 TB

• Archive tier: ~270 TB before compression/compaction

The architecture matters more than the software choice.

Azure cost model to think through

For Azure, I would separate storage into tiers:

1. Hot query tier (days/weeks)

Used for active debugging.

Examples:

• Managed disks for ClickHouse

• VM SSDs

• Elasticsearch data nodes

Keep this short.

Typical policy:

• 7–30 days traces

• 30–90 days metrics

2. Cheap retention tier (months/years)

Used for retrospectives.

Examples:

• Azure Blob Storage Cool/Archive tiers with Tempo

• Exported ClickHouse parts

• Parquet files

This is where 18-month retention becomes financially viable.

3. Sampling and aggregation

Before storage, reduce volume:

• Keep 100% of errors

• Keep 100% of slow traces

• Keep representative samples of healthy requests

• Preserve span attributes needed for retrospectives

A 10x reduction in trace volume is often realistic.

My evaluation order for your situation

Given:

• B2B SaaS

• Azure

• OpenTelemetry already deployed

• 18-month incident retrospective requirement

• 40% cost reduction target

I would run a proof of concept in this order:

Option 1 — Grafana stack (most likely winner)

Architecture:

For a 400-engineer enterprise with GDPR/SOC 2 constraints, I would narrow the field considerably. The requirement is not just “self-hosted dashboards”; it is a supported, horizontally scalable observability platform with unified telemetry, RBAC, retention controls, upgrades, integrations, and vendor accountability.

The strongest enterprise-grade self-managed options I would evaluate are:

Elastic, Grafana Labs, Dynatrace, and Splunk.

Shortlist

PlatformDeployment modelLogsMetricsTraces/APMEnterprise maturityFit for your caseElastic Observability (self-managed)Your VPC / Kubernetes / VMsExcellentGoodGoodVery mature⭐ Strong if logs are centralGrafana Enterprise StackYour infrastructureGood (Loki)Excellent (Prometheus ecosystem)Good (Tempo)Mature with commercial support⭐ Strong cloud-native choiceDynatrace ManagedCustomer-controlled environmentExcellentExcellentExcellentVery high⭐ Best “buy vs build” optionSplunk Enterprise + Observability componentsSelf-managed / private environmentsExcellentExcellentExcellentVery highStrong for regulated enterprisesOpenSearch + OpenTelemetry stackSelf-managedGoodModerateModerateLower operational maturityUsually still a DIY platform

1. Dynatrace Managed — closest to a “replace your homegrown platform” answer

Dynatrace Managed is probably the closest match if your goal is reducing operational burden. It supports deployment in customer-controlled environments and provides application monitoring, infrastructure monitoring, logs, distributed traces, topology mapping, and enterprise operations tooling. docs.dynatrace.com (+1 more citation)

Why it fits a 400-engineer organization:

• Mature APM agents and automatic instrumentation

• Strong dependency/service mapping

• Enterprise RBAC and governance

• Less internal platform ownership than assembling OSS components

• Better suited to a centralized SRE/platform team supporting hundreds of engineers

Tradeoffs:

• Premium licensing

• Less “open stack” flexibility

• You need to validate exact deployment architecture with your compliance team (network isolation, patching responsibility, data residency)

Best fit: You want observability to become a managed internal capability, not another platform your engineers maintain.

2. Grafana Enterprise Stack — best cloud-native/self-hosted architecture

Grafana Enterprise Stack is probably the strongest option if your engineering culture already values Kubernetes, OpenTelemetry, and open standards.

The stack combines:

• Grafana Enterprise for visualization/governance

• Prometheus-compatible metrics storage

• Loki-based logs

• Tempo-based traces

Grafana describes the Enterprise Stack as a self-managed observability platform with enterprise support, scalability, security controls, and correlation across telemetry types. Gartner (+1 more citation)

Strengths:

• Excellent Kubernetes fit

• Strong OpenTelemetry alignment

• Avoids cloud data egress

• Engineers often already know Grafana dashboards

• Can run entirely inside your VPC

Challenges at your scale:

• You still own architecture decisions:

retention

• cardinality management

• storage sizing

• upgrades

• multi-cluster aggregation

• “Enterprise supported” does not mean “zero operations”

Best fit: You have a strong platform/SRE organization and want maximum control.

3. Elastic Observability — strongest if logs are your hardest problem

Elastic Observability remains one of the most mature self-managed observability platforms, especially for organizations where centralized logging, search, and incident investigation dominate.

Strengths:

• Very strong log analytics/search

• Mature security and access controls

• Good APM and distributed tracing support

• Large ecosystem

Challenges:

• Elasticsearch operations at enterprise scale are non-trivial

• Storage costs can become significant

• Metrics experience is generally less natural than Prometheus/Grafana for cloud-native environments

Best fit: Your incident response workflow starts with “find the right logs quickly.”

4. Splunk — regulated-enterprise heavyweight

Splunk Enterprise is common in heavily regulated industries because of its governance, audit, and security ecosystem.

Strengths:

• Very mature operational model

• Strong compliance story

• Excellent search