Top 10 SolarWinds Alternatives in 2026

SolarWinds covers infrastructure and network monitoring well, and many teams still run it for that. What moves them to evaluate alternatives is usually a newer set of needs: cloud-native telemetry, OpenTelemetry (OTel) instrumentation, and faster incident investigation.

Those needs put two things in tension: how fast a team can investigate versus how much telemetry it can afford to keep. Resolving that without a painful migration is what separates a real replacement from a lateral move.

This guide covers how to evaluate SolarWinds alternatives across cost control, SaaS deployment, OTel support, data ownership, and AI-native incident investigation, plus the mechanics of moving off a SolarWinds agent onto an OTel-native pipeline.

Why Teams Are Moving Off SolarWinds in 2026

The pressure to switch comes from several sources. It builds across cost, security posture, cloud-native coverage, and the day-to-day friction of investigating incidents on a slow user interface (UI). Each of these maps to a capability you should test in any replacement.

• Cost and module sprawl: From unpredictable module-based licensing and high recurring costs to transparent, predictable, and scalable pricing models.
• Security and sovereignty: From supply-chain vulnerabilities and data sovereignty risks to robust security postures that prioritize platform integrity and data ownership.
• Cloud-native coverage: From fragmented, log-centric tools that silo telemetry to unified, cloud-native platforms that correlate logs, metrics, and traces for end-to-end visibility.
• Investigation speed: From slow, UI-heavy workflows that drag during critical incidents to responsive, high-performance interfaces that keep investigation flows moving at the speed of the incident.

These drivers turn every pain point into a benchmark, allowing you to filter out platforms that add complexity rather than resolving it.

What to Look for in a SolarWinds Alternative

When evaluating potential replacements, prioritize features that solve your current operational pain points while setting up a scalable architecture for the future:

• Match the SolarWinds product you are replacing: Choose a platform that directly replaces your current observability platform, log-centric tool, APM, or real user monitoring solution.
• Unified observability: Select a tool that correlates logs, metrics, and traces automatically within a single investigation surface.
• AI-native incident investigation: Prioritize platforms with AI agents capable of reading telemetry, building hypotheses, and pinpointing root causes independently.
• Pricing model and data ownership: Avoid per-host or per-user pricing, opting instead for models that keep telemetry data accessible and independent of proprietary storage.
• Migration effort and OpenTelemetry support: Minimize switching costs by choosing platforms with native OpenTelemetry support to avoid rebuilding proprietary agent footprints.

Evaluating vendors against these specific criteria ensures your new solution actively resolves current operational friction rather than simply adding new layers of complexity.

The 10 Best SolarWinds Alternatives for Observability in 2026

Each alternative category below differs on pricing model, deployment, OpenTelemetry stance, and best-fit use case. Your procurement review should still run its own telemetry and workflows against that backdrop and validate current rates before comparing vendors.

Alternative Type	Vendors	Deployment	OTel Support	Pricing Model	Best For
Full-stack observability + SIEM	Coralogix	SaaS + customer-owned cloud storage	OTel-native, no proprietary agents	Per-gigabyte (GB) ingested ($0.42 logs, $0.16 traces, $0.05 metrics); no per-host/user fees	Cross-stack plus Security Information and Event Management (SIEM) with data ownership
Broad SaaS observability suite	Datadog	SaaS only	OTel accepted, agent often used for deeper features	Per-product billing	Large enterprises with broad integration needs
APM-led usage platform	New Relic	SaaS only	OTel first-class	Per-GB ingest plus per-user tiers	APM-led teams, usage-based billing
Managed dashboard stack	Grafana Cloud	SaaS or self-hosted	OTel-native collector path	Free tier plus usage-based	Teams standardized on open dashboards
Enterprise APM suite	Dynatrace	SaaS, managed	Native OTel plus proprietary agent option	Consumption, no per-user	Enterprise APM with causal AI
Enterprise log analytics suite	Splunk	SaaS, cloud, on-prem, hybrid	OTel Collector default	Host or usage-based	Existing enterprise log estates
Search-first observability stack	Elastic	SaaS, serverless, self-managed	OTel-native distribution	Per-GB or resource-based	Search-heavy log workloads
Credit-based log analytics suite	Sumo Logic	SaaS only	OTel Collector distribution	Credit-based, unlimited ingest	Mid-market logs plus SIEM
High-cardinality tracing platform	Honeycomb	SaaS, private cloud on Enterprise	OTel-native all tiers	Event-volume, unlimited seats	High-cardinality tracing
Managed search and metrics service	groundcover, Last9	SaaS only	OTel Collector plus extended Berkeley Packet Filter (eBPF)	Consumption-based	Migrations from self-managed search and metrics stacks

1. Coralogix: Full-Stack Observability and SIEM

Coralogix is a full-stack observability platform that brings telemetry, SIEM, and customer-owned data together, which suits teams worn down by per-seat pricing. It answers all four SolarWinds concerns with ingestion-based pricing, customer-owned storage, OTel-native ingestion, and an autonomous investigation agent.

The architecture runs on Streama, an in-stream engine that analyzes and alerts on telemetry before it reaches storage, then writes it to your own Amazon Simple Storage Service (S3) or US-region Google Cloud Storage (GCS) bucket in open Parquet format, so the vendor never holds it. Remote, index-free querying runs against that archive with no rehydration step or separate bill, and ingestion is OTel-native with no proprietary agents.

Key features:

• Streama in-stream processing that alerts before data lands in storage.
• Customer-owned S3 or US-region GCS storage in open Parquet format.
• Olly, the autonomous observability agent, which separates causality by testing its own hypotheses.
• OTel-native ingestion with no proprietary agent, plus a built-in SIEM.

Pros:

• Data ownership by design, since telemetry stays in your bucket.
• Ingestion-based pricing with no per-host or per-user fees.
• Investigation that traces multiple impact sites to a single root cause.

Cons:

• Anything routed to the Blocked pipeline sits outside Olly’s view.
• The breadth of capability brings a learning curve, with some users citing a lot of options to absorb early on.

Best for: Teams that want cross-stack observability and SIEM in one place while keeping ownership of their telemetry.

Pricing: Per-GB ingested, at $0.42 for logs, $0.16 for traces, and $0.05 for metrics, with no per-host or per-user charges.

2. Datadog: Broad SaaS Observability Suite 

Datadog covers telemetry, Real User Monitoring (RUM), and security across large enterprise estates, with a broad integration catalog and AI investigation tooling that ties signal correlation into prebuilt dashboards. The depth question is whether the platform moves from an alert into multi-step root cause analysis, pulls in code and ownership context, and keeps remediation actions inside the guardrails your team requires.

Breadth is what draws teams here, and pricing complexity is what slows them down. The deepest feature access usually still runs through the Datadog Agent, so anyone standing on OTel should test feature parity before assuming the data alone unlocks everything.

Key features:

• Broad integration catalog spanning infrastructure, APM, logs, RUM, and security.
• AI investigation tooling layered onto prebuilt dashboards.
• OTel ingestion supported, with the Datadog Agent as the path to fullest features.

Pros:

• Single vendor across a wide range of telemetry and security products.
• Polished interface and large integration ecosystem.

Cons:

• Costs stack across independently metered products.
• Telemetry is held in Datadog’s store rather than your own.

Best for: Large enterprises that want broad coverage in one SaaS suite and can actively manage spend.

Pricing: Per-product billing, with hosts billed per host and logs billed on ingest plus indexing, so procurement has to model the full bill rather than the headline rate.

3.New Relic: APM-Led Usage Platform

New Relic centers on application performance, which makes it a fit for teams whose investigations start with a slow endpoint. Small groups often live inside the free tier, while larger ones have to model how data ingest and per-user pricing interact as they scale. OTel belongs in the workflow test here, and any AI story is worth measuring against fully autonomous agent narratives instead of taking the label at face value.

Key features:

• APM-led workflows with distributed tracing across 50-plus capabilities.
• First-class OTel ingestion alongside its own agents.
• A perpetual free tier with 100 GB of monthly ingest.

Pros:

• Cost clarity for smaller teams on the free and entry tiers.
• Usage-based ingest rather than per-host billing.

Cons:

• Per-user charges turn material as access spreads across engineering.
• Telemetry is vendor-stored.

Best for: APM-led teams that start small and want usage-based ingest, with seat costs modeled before rollout.

Pricing: Per-GB ingest above a free 100 GB monthly tier, plus per-user pricing across Basic, Core, and Full Platform user types.

4. Grafana Cloud: Managed Dashboard Stack

Grafana Cloud wraps open-source logging, metrics, tracing, and profiling components in a managed offering, which suits SRE teams already living in open dashboards who want portability without running the stack themselves. If your team already builds in that open-source dashboard layer, the continuity is the main draw.

OTel ingestion usually runs through a collector-style path, and those open-source roots are the main reason teams reach for this category when portability drives the decision. Self-hosted deployments hand you more control over storage and operations, though that control arrives with the operational ownership behind it. A live incident is where the trade-off shows: when the signals require different query languages, the correlation cost climbs at the worst possible moment.

Key features:

• Managed Loki, Mimir, Tempo, and Pyroscope under one platform.
• OTel-native collector ingestion path.
• Self-hosted option for teams that want to own storage and operations.

Pros:

• Open-source portability that reduces lock-in.
• A genuinely usable free entry tier.

Cons:

• Cardinality-driven costs can spike during incidents.
• Correlation across signals can require different query languages.

Best for: Teams standardized on the open-source dashboard layer that want a managed backend without running it themselves.

Pricing: Free tier plus usage-based charges for metrics series, log and trace volume, and active users, so model both telemetry volume and user patterns before assuming the free tier scales with you.

5. Dynatrace: Enterprise APM Suite

Dynatrace builds its root cause analysis around causal AI, with topology-aware auto-discovery mapping dependencies before an incident forces the question. The pitch is depth without per-user pricing, paid for through consumption-based billing.

If your migration goal is to cut proprietary-agent dependency, the test is how much topology, code-level context, and dependency mapping native OpenTelemetry delivers versus what still needs the vendor’s own agent. That agent question is the catch: APM depth justifies the enterprise price only if it does not quietly depend on the OneAgent in your own environment.

Key features:

• Causal-AI root cause analysis with topology-aware auto-discovery.
• Native OTel ingestion alongside the OneAgent option.
• Unlimited users with no per-seat charge.

Pros:

• Deep enterprise APM without per-user fees.
• Dependency mapping in place before incidents force the question.

Cons:

• Consumption rates need validation against memory, host, and data volume.
• Fullest depth can still lean on the OneAgent.

Best for: Enterprises that want causal-AI APM depth and unlimited user access under consumption billing.

Pricing: Consumption-based pricing under the Dynatrace Platform Subscription, billed per host-hour and per gigabyte rather than per user, so validate the rate card against your own volume before the contract number means anything.

6. Splunk: Enterprise Log Analytics Suite

Splunk fits organizations sitting on large historical log estates, established search workflows, and governance requirements that span cloud, on-prem, and hybrid environments. Ingestion runs through an OTel Collector distribution in agent or gateway mode. For teams already running a major log program, continuity is the main reason to stay, since OTel-native ingestion on enterprise-grade infrastructure keeps existing workflows intact.

Key features:

• Deep log search and analytics across cloud, on-prem, and hybrid.
• OTel Collector ingestion in agent or gateway mode.
• Mature SIEM and governance tooling for large estates.

Pros:

• Strong fit for existing enterprise log and search programs.
• Flexible deployment across hosting models.

Cons:

• Per-unit costs move through sales conversations rather than a public rate card.
• Pulling older data mid-investigation carries an operational and cost hit.

Best for: Organizations with large existing log estates and governance needs across hosting models.

Pricing: Host or usage-based across workload, ingest, and entity models, with most tiers quoted through sales, so pin down which model fits before opacity becomes a budgeting problem.

7. Elastic: Search-First Observability Stack

Elastic brings search-engine roots to observability, a fit for teams whose debugging hinges on querying large log volumes. An open standards architecture reduces tooling overlap by natively ingesting OpenTelemetry data, correlating across signals, and exposing pipeline and retention controls.

That OTel direction makes the category a natural home for teams that already think in search, indexing, and flexible deployment. Deployment spans SaaS, serverless, and self-managed patterns, so the fit is best for teams with existing search skills.

Key features:

• Search-first querying over large log volumes.
• OTel-native distribution with cross-signal correlation.
• SaaS, serverless, and self-managed deployment options.

Pros:

• Strong log search depth for search-oriented teams.
• A self-managed path for teams that want to own their data.

Cons:

• Self-managed deployments carry real operational overhead.
• Billing tracks uncompressed volume, so model the full architecture.

Best for: Search-heavy log workloads run by teams with existing Elastic or search skills.

Pricing: Per-GB or resource-based depending on whether you run Serverless (usage-based) or Cloud Hosted (resource-based), so model the deployment before assuming the search engine you know stays cheap at volume.

8. Sumo Logic: Credit-Based Log Analytics Suite

Sumo Logic pairs log analytics with SIEM, unlimited ingest, and compliance certifications, aimed at mid-market teams that want security workflows without operating their own stack. The category earns its place when procurement cares about bundled compliance posture and predictable usage planning.

Key features:

• Log analytics paired with a bundled SIEM.
• Free, unlimited log ingest under the Flex model.
• Compliance certifications and broad user access.

Pros:

• Unlimited ingest removes pressure to drop log sources.
• Analysts and engineers share one tool.

Cons:

• Storage, scan, and retention consume credits, so usage still needs modeling.
• Telemetry is vendor-stored.

Best for: Mid-market teams that want log analytics plus SIEM without running their own backend.

Pricing: Credit-based pricing where ingest is free under Flex while storage, scan, and retention draw credits, so model query and storage behavior before a large migration commits you to the rate.

9. Honeycomb: High-Cardinality Tracing Platform 

Honeycomb earns evaluation when debugging depends on request-level context, the ability to slice telemetry by arbitrary dimensions like user ID or request path without precomputing them. For teams whose hardest incidents live between aggregate metrics and individual requests, that slicing is the reason to look.

OTel sits at the center of the workflow, and unlimited query seats keep the tool open to the whole engineering team without per-seat friction.

Key features:

• High-cardinality querying across arbitrary dimensions.
• BubbleUp anomaly surfacing during incidents.
• OTel-native ingestion across all tiers.

Pros:

• Request-level debugging that aggregate dashboards miss.
• Unlimited seats keep the whole team in the tool.

Cons:

• Coverage outside tracing is narrower than cross-stack platforms.
• Event-based math climbs as volume grows.

Best for: Teams whose hardest incidents live between aggregate metrics and individual requests.

Pricing: Event-volume pricing with unlimited seats, so model event volume, retention, and pipeline needs early rather than assuming the per-event math stays cheap.

10. groundcover, Last9:Managed Search and Metrics Services

Managed search and metrics services like groundcover and Last9 package open-source-style observability patterns into a managed offering, a natural landing spot for teams tired of operating their own logging or metrics infrastructure. Coverage is cross-stack, with Kubernetes and OpenTelemetry-oriented ingestion patterns.

The draw is a smoother migration off self-hosted infrastructure, with the managed layer absorbing the operational work the team used to carry, often inside your own cloud through a BYOC deployment.

Key features:

• Cross-stack coverage with Kubernetes and OTel-oriented ingestion.
• eBPF-based instrumentation with zero code changes (groundcover).
• High-cardinality telemetry handling and ingestion controls (Last9).

Pros:

• Smoother migration off self-hosted logging or metrics stacks.
• BYOC keeps telemetry inside your own cloud.

Cons:

• Groundcover centers on Kubernetes, so non-cloud-native fit is narrower.
• Each tool’s pricing unit differs, so comparisons need care.

Best for: Teams migrating off self-managed search and metrics infrastructure that want a managed layer without giving up data residency.

Pricing: Per-node pricing for groundcover and per-event ingested for Last9, with no per-user fees, so model each tool’s unit against your own footprint.

Migrating from SolarWinds

The mechanics of leaving SolarWinds determine whether a switch takes weeks or quarters, and most of the perceived difficulty comes down to rebuilding a proprietary agent. A target platform that accepts standard OTel instrumentation without its own agent removes that cost. A few things to keep in mind:

• Plan for a clean break on history: Most migrations let new data flow to the new platform while old SolarWinds data ages out in place, rather than exporting it.
• The OTel lift is mostly the agent: Install the OTel software development kit (SDK) without changing instrumentation first, confirm nothing breaks, then replace libraries one at a time.
• Dual-emit during cutover: If your platform ingests OpenTelemetry Protocol (OTLP) directly, run a dual-emit phase so existing dashboards keep populating while you validate the new pipeline.
• Watch Collector state: The OTel Collector holds queued data in memory by default and loses it on restart, so your cutover timing has to account for that.

Where Coralogix helps is the agent question: OTel-native ingestion with no proprietary agent removes the largest single migration cost, since there is no vendor agent footprint to rebuild.

How Coralogix Replaces SolarWinds for AI-Native Observability

Coralogix leads the list because its answer to each SolarWinds blocker is a specific, testable capability instead of a longer feature sheet. The renewal-cost, sovereignty, cloud-native, and slow-investigation problems each have a concrete response, detailed in its entry above, and the only question left is whether those responses hold on your own data.

If the renewal math no longer works or investigations drag for hours, start a free 14-day trial and run your own production telemetry through Coralogix to compare cost, migration effort, and AI-native investigation depth before committing to another SolarWinds renewal.

Frequently Asked Questions About SolarWinds Alternatives

What is the difference between SolarWinds Observability SaaS and Self-Hosted?

SolarWinds Observability SaaS is cloud-hosted, while Self-Hosted runs on the SolarWinds Platform on customer-managed infrastructure. In practice, the difference affects hosting model, data location, upgrade path, and how much operational responsibility stays with your team. For a deeper comparison of architectures, see the Coralogix guide to observability platforms.

Does SolarWinds support OpenTelemetry?

OpenTelemetry support is best read as a workflow test. The question is whether OTel data creates the entities, topology, dependency views, dashboards, and alerts your incident workflow depends on. For how OTel-native ingestion compares to Prometheus, see the OTel Prometheus guide.

What is the cheapest SolarWinds alternative?

Several categories offer free or low-cost entry paths, including managed dashboard stacks, high-cardinality tracing platforms, APM-led usage platforms, and search-first observability stacks. For paid workloads, the cheapest option depends on your data volume, retention needs, query patterns, user count, and whether costs depend on hosts, seats, indexed events, or credits. For unlimited users and hosts with no per-seat fees, ingestion-based and credit-based models avoid per-user charges entirely.

Is SolarWinds still affected by the SUNBURST breach?

SUNBURST is a historical supply-chain breach, but it remains relevant to procurement risk assessment for regulated or government-adjacent teams. Agencies remediating compromised on-premises environments can follow federal eviction guidance, and the breach continues to shape how teams evaluate software supply-chain risk, data sovereignty, and customer-owned storage. Data sovereignty and customer-owned storage remain relevant evaluation criteria as a result, covered in the European Union Data Act guide.