12 Best Dynatrace Alternatives for Observability in 2026

Observability used to mean one category. It now means at least three, and they no longer replace each other.

Full-stack platforms watch hosts and services, cost-control layers exist for teams whose real problem is ingest volume, and a newer group is built around the model call, where the unit of investigation is an agent run rather than a request. Dynatrace sits in the first group. Before you compare prices, work out which group you actually need.

These 12 Dynatrace alternatives are compared on what they can see, what they bill for, and where they run.

What Is Dynatrace?

Dynatrace is a full-stack observability platform for enterprise environments. It combines application performance monitoring, infrastructure monitoring, log analytics, digital experience monitoring, and application security in one product, with an AI engine layered across all of it.

Three pieces define how it works: 1. OneAgent installs once per host and auto-instruments the processes it finds. 2. Smartscape builds a live topology map of services, processes, hosts, and their dependencies. 3. Grail stores logs, metrics, traces, and events in a single lakehouse queried through DQL, the Dynatrace Query Language.

What Does Dynatrace Do?

Dynatrace covers the signals a large operations team needs across hybrid and cloud-native estates. Here is what the platform handles:

• Application performance monitoring with code-level profiling and end-to-end transaction tracing
• Infrastructure monitoring across hosts, containers, Kubernetes, and cloud services
• Log analytics with on-read parsing and DQL-powered querying
• Real user monitoring and session replay across web and mobile
• Synthetic browser and HTTP monitors from public and private locations
• Runtime vulnerability analytics and application protection
• Automated root cause analysis through the Davis AI engine

The automation is the draw, and it puts Dynatrace among the more capable AIOps tools for large estates. Deploy OneAgent, and discovery, dependency mapping, and baselining happen without manual configuration.

Dynatrace Pricing

Dynatrace sells through the Dynatrace Platform Subscription, a consumption model with a minimum annual commitment that is not published. Capabilities draw down against that commitment at published rate-card prices, billed in 15-minute increments.

Here's a quick breakdown of Dynatrace's pricing based on each use case:

• Foundation & Discovery - $7 per host per month, billed at $0.01 per host-hour
• Infrastructure Monitoring - $29 per host per month, billed at $0.04 per host-hour
• Full-Stack Monitoring - $58 per month for an 8 GiB host, billed at $0.01 per memory-GiB-hour
• Kubernetes Platform Monitoring - $1.40 per pod per month, included on Full-Stack hosts
• Log Analytics - $0.20 per GiB ingested, then either $0.02 per GiB-day with queries included, or $0.0007 per GiB-day plus $0.0035 per GiB scanned
• Traces - $0.20 per GiB ingested, with 10 days of retention included
• Real User Monitoring - $2.25 per 1,000 sessions, $4.50 with session replay
• Synthetic Monitoring - $4.50 per 1,000 browser actions, $1.00 per 1,000 HTTP requests
• Application Security - $13 per month per 8 GiB host for runtime vulnerability analytics

Dynatrace does not offer a free tier, only a trial and a public Playground.

How to Choose a Dynatrace Alternative

Teams leave Dynatrace for three reasons that show up again and again: bills that cannot be forecast a quarter out, instrumentation locked to a proprietary agent, and a platform built for operations managers rather than the engineer holding the pager at 2am. A replacement that fixes one and breaks the others is not a replacement.

Use these to narrow the field:

• Billing unit - Per host punishes autoscaling and ephemeral workloads. Per GB punishes verbose logging. Per event punishes deep traces. Pick the one that matches how your system actually generates data.
• Signal coverage - Metrics, logs, and traces in one store with real correlation, or separate products with separate query languages stitched together at the dashboard layer.
• Instrumentation portability - Native OTLP ingest means pointing an existing OpenTelemetry Collector at a new backend instead of rewriting application code.
• Deployment model - SaaS, self-hosted, or running inside your own cloud account. Data residency and compliance requirements usually settle this before anything else.
• Cost control before storage - Whether the platform lets you shape, sample, or route telemetry by value before it bills, or only after.
• AI and agent visibility - Whether a trace continues past the model call into the prompt, the token count, the tool invocation, and the retry.
• Query language - DQL, NRQL, SPL, and PromQL are not portable. SQL and OTel-native query builders are closer to skills your team already has.

The last line is more important than the others, as most platforms below now ingest a model call, but ingesting it and organizing around it are different things. The question is whether the prompt version, the tool result, the retry, and the eval score arrive as one object or as four you have to correlate yourself.

12 Best Dynatrace Alternatives for Observability

1. Respan

Dynatrace tells you the model call took four seconds. Respan tells you which model, which prompt version, how many tokens it burned, what the tool returned, and whether the fallback fired. Every LLM call, tool run, retrieval, and agent turn becomes a span in one trace with its input, output, latency, and cost attached, so the metric on the dashboard and the run that produced it are one click apart. A gateway, observability, evaluations, and prompt optimization sit on one platform rather than four.

Pros:

• One endpoint reaches 1,000+ models with automatic fallbacks, load balancing, retries, and per-key spend limits, so a provider outage or a rate limit does not take production down
• Full trace tree of every prompt, tool call, retrieval, and response, with latency and cost on each span, so debugging starts at the run instead of a dashboard
• Evaluators combining LLM judges, deterministic code checks, and human review score live traffic and test sets, turning quality into a number you can regress against
• Behaviors classify semantic patterns across interactions, surfacing user frustration, jailbreak attempts, escalations, and wins, plus custom patterns like churn risk trained on your own labeled examples
• Error tracking and monitors alert on cost, latency, or failure thresholds into Slack, email, or a webhook
• Red teaming runs campaigns against deployed agents to show what gets blocked and what gets through
• Cost and token usage break down by model, key, and end user, and budgets block requests before spend runs away
• Prompt versioning, one-click deployment, and side-by-side experiments ship the version that measurably wins
• SOC 2, HIPAA, GDPR, and ISO 27001, with a BAA available for healthcare teams

Cons:

• Self-hosted deployment starts at Enterprise
• The Free tier doesn't offer additional logs

Pricing: Free covers 100k logs, 1k scores, and 7-day retention with unlimited seats. Team is $199 per month billed yearly for unlimited datasets, evaluators, and prompts, 10k scores, 30-day retention, and 5 members at $15 each thereafter. Enterprise adds self-hosting, SAML, a HIPAA BAA, and custom throughput.

2. Datadog

Datadog covers more ground than anything else here: infrastructure, APM, logs, RUM, synthetics, security, CI visibility, and Agent Observability for LLM workloads. Each is a separate meter. A team running infrastructure and APM on the same hosts is paying two per-host charges before logs enter the picture, and every unique tag combination creates a billable custom metric.

Pros:

• Free tier covers 5 hosts, and APM can now be bought standalone without an Infrastructure subscription
• Watchdog surfaces anomalies without threshold configuration

Cons:

• Per-host billing counts ephemeral Kubernetes nodes at their high-water mark
• Custom metric overages are the most common source of unexpected invoices
• AI Credits, Agent Observability, and security each add meters on top of the host rate

Pricing: Infrastructure Pro is $15 per host per month annually, $18 on demand; Enterprise is $23 and $27. APM is $31 per host with Infrastructure attached, or $36 standalone, rising to $40 for APM Enterprise. Logs are $0.10 per GB ingested plus $1.70 per million events at 15-day indexing. AI Credits start at $500 per 500 credits monthly.

3. New Relic

Two meters run the New Relic bill: gigabytes ingested and user seats. Hosts, containers, and agents are unlimited at no cost, which makes it structurally cheaper than Dynatrace for autoscaling fleets and structurally expensive for large teams who all need full access. The free tier offers 100 GB a month with one full platform user.

Pros:

• Unlimited hosts and containers, so scaling pods does not move the bill
• 100 GB monthly ingest free on every edition, permanently
• NRQL queries across every signal from one interface, plus a dedicated AI observability module

Cons:

• Full platform seats reach $349 per user per month on Pro, or $418.80 pay-as-you-go
• Free-tier ingest hard-stops at 100 GB and platform access is suspended until upgrade or month reset
• Standard caps at five full platform users, forcing a jump to Pro pricing

Pricing: Free includes 100 GB and one full user. Beyond that, Original Data is $0.40 per GB and Data Plus $0.60 per GB, with EU residency adding $0.05. Core users are $49. Full platform users are $10 for the first and $99 each up to five on Standard, then $349 on Pro annually. Enterprise is custom and adds FedRAMP and HIPAA eligibility.

4. Honeycomb

Where Dynatrace automates the answer, Honeycomb hands you the raw event and makes querying it fast. It stores wide, high-cardinality structured events and derives traces and metrics from them, so filtering by user ID, build SHA, or feature flag carries no cardinality penalty. BubbleUp surfaces which field correlates with an anomaly, and Agent Timeline renders LLM calls, tool invocations, and handoffs in one conversation view.

Pros:

• Arbitrary high-cardinality queries without pre-aggregation or index planning
• Unlimited seats and unlimited querying on every tier, including Free
• OpenTelemetry-native with no proprietary agent

Cons:

• Traditional infrastructure metrics coverage is thinner than full-stack platforms
• Event-based billing scales with trace depth, and a 150-span trace counts as 150 events
• SLOs cap at 2 on Pro, and Service Map is Enterprise-only

Pricing: Free covers 20M events and 100M metrics data points monthly at 14-day retention. Pro starts at $150 per month for 50M events, scaling to 750M events and 3.75B data points, with SSO and 100 triggers. Enterprise is custom and starts from a base allowance of 10 billion events per year, adding Service Map, 100 SLOs, and Private Cloud. Telemetry Pipeline starts at $0.10 per GB.

5. Grafana

The Grafana stack is Mimir for metrics, Loki for logs, Tempo for traces, and Pyroscope for profiles, available self-hosted under open source or managed as Grafana Cloud. Nothing else here gives away as much: the free tier includes 10k active series, 50 GB each of logs, traces, and profiles, three users, and every Cloud service at limited usage. The trade is operational, since the components are separate systems with separate query languages.

Pros:

• Self-hosted, Cloud, Federal Cloud, and Bring Your Own Cloud all supported
• Agent Observability traces LLM generations and runs evals and guards, with metering deferred until October 1, 2026

Cons:

• Correlating across Loki, Mimir, and Tempo means PromQL, LogQL, and TraceQL
• Logs bill in four parts, process, write, retain, and query, with traces and profiles on the first three, which complicates forecasting
• Enterprise features start at a $25,000 annual spend commit

Pricing: Grafana offers a free tier. Pro is a $19 monthly platform fee plus usage: metrics from $6.50 per 1k series, logs and traces at $0.05 per GB process, $0.40 write, and $0.10 retain, visualization at $8 per active user, and IRM at $20 per active user. Enterprise starts at $25,000 per year with metrics falling as low as $3 per 1k series. Self-hosted Grafana OSS is free.

6. Chronosphere

Chronosphere built its product around the observation that most telemetry is never queried. Its Control Plane aggregates, drops, and samples data before storage, and billing follows the useful data retained rather than raw ingest, hosts, or VMs. For teams whose Dynatrace bill is driven by volume rather than capability, that inverts the equation. Palo Alto Networks completed its acquisition in January 2026 and is integrating the platform alongside Cortex AgentiX, though the Telemetry Pipeline remains available standalone.

Pros:

• Cost controls apply before storage rather than after the invoice
• 100% PromQL compatible, so existing Prometheus rules and dashboards port over
• Telemetry Pipeline ships 70+ integrations and can feed other platforms, not only Chronosphere

Cons:

• No published rates and no self-serve free tier; every engagement starts with sales
• Evaluation runs as a pilot rather than a signup, typically two to three weeks
• Post-acquisition packaging is still settling, so standalone terms should be confirmed at quote

Pricing: Custom quote only. The Observability Platform bills on useful telemetry retained after Control Plane shaping, using credits as a currency spanning metrics, logs, and traces. Telemetry Pipeline is priced separately on raw data throughput. Pilots are typically free unless large-scale or long-term.

7. Splunk Observability Cloud

Splunk Observability Cloud is the SignalFx lineage rebuilt into three per-host bundles under Cisco ownership. Its streaming architecture alerts in seconds rather than on batch cycles, and NoSample tail-based tracing captures full-fidelity traces before filtering. Buyers should know that Cisco also sells AppDynamics from the same catalog at $6 to $50 per vCPU, and the two products overlap heavily.

Pros:

• NoSample full-fidelity trace ingest instead of head-based sampling
• Real-time streaming analytics with sub-second alerting on high-cardinality metrics
• Agent Observability covers agents, models, and AI costs

Cons:

• Host-based pricing starts at a 15-host minimum
• Database Monitoring and Secure Application bill on top of any bundle
• Two overlapping APM products in one catalog complicates the buying decision

Pricing: Infrastructure is $15 per host per month annually, App & Infra $60, End-to-End $75. Standalone, APM is $55 per host, RUM $14 per 10,000 sessions, Database Monitoring $75 per instance, and Secure Application $22 per host. Synthetics run $1 per 10,000 uptime requests, which prices well against standalone synthetic monitoring tools if you are already paying for hosts. Free trial available, no free tier.

8. Elastic Observability

Elastic Observability runs on Elasticsearch, so full-text search across logs is its strongest surface. Serverless bills purely on gigabytes ingested and retained across two tiers, with volume-tiered rates that step down as data grows. Teams already operating ELK get the shortest migration path on this list, since the query patterns and tooling carry over.

Pros:

• Serverless, Elastic Cloud Hosted, and self-managed deployment, with self-managed free at the license level
• Metrics in time series mode price at 25% of the standard rate for both ingest and retention
• OpenTelemetry-first ingest with 350+ integrations and a dedicated LLM observability product

Cons:

• Billing meters uncompressed, normalized, enriched volume at the end of the ingest pipeline, so billed gigabytes exceed raw source size
• Support beyond Standard costs 5% to 15% of total consumption
• Synthetic monitoring, Workflows, and Agent Builder are Complete-tier add-ons

Pricing: Logs Essentials starts at $0.07 per GB ingested and $0.017 per GB retained monthly. Complete starts at $0.09 per GB for logs and traces and $0.023 for metrics, retaining at $0.019 and $0.005. Egress is 50 GB free then $0.05 per GB. Add-ons include browser tests at $0.0123 per run and the Elastic Managed LLM at $4.50 per million input tokens.

9. Braintrust

Braintrust sits at the evaluation end of the spectrum. Datasets, experiments, traces, online evals, and prompt management run in one product, and its GitHub Actions integration fails builds on quality regressions with confidence intervals attached. It bills on processed data and scores rather than spans, which means verbose agents and large RAG contexts consume quota faster than request count suggests.

Pros:

• Eval-driven release gates in CI rather than post-deployment dashboards
• Unlimited users, projects, datasets, playgrounds, and experiments on every tier including Free
• Qualifying early-stage startups get 6 to 12 months of Pro at no cost

Cons:

• Not a replacement for infrastructure or application observability
• SAML SSO, BAA, uptime SLA, and S3 export are Enterprise-only, so compliance drives the tier decision more than volume
• Processed data counts every byte of inputs, outputs, prompts, metadata, and attachments

Pricing: Starter is free with $10 model credits, 1 GB processed data then $4 per GB, 10k scores then $2.50 per 1,000, and 14-day retention. Pro is $249 per month with $249 credits, 5 GB then $3 per GB, 50k scores then $1.50 per 1,000, and 30-day retention extendable to 180 days at $0.50 per GB monthly. Enterprise is custom.

10. Fiddler AI

Fiddler AI came from classical ML monitoring, applying Shapley values and attribution methods to explain model outputs, and now leads with an AI Control Plane for agents. Its Centor evaluation models run guardrail and scoring checks in your environment rather than calling an external LLM, which removes the per-check API cost most eval platforms pass through. The free tier for Fiddler AI, as well as for similar tools, includes guardrails only, with observability starting at per-trace billing.

Pros:

• Guardrails covering hallucination, toxicity, PII and PHI exposure, prompt injection, and jailbreaks at under 80ms added latency
• Evaluation models run in-environment, so scoring does not incur external LLM charges
• Governance evidence and audit records built for regulated review cycles

Cons:

• No LLM gateway, so failover, caching, and spend limits stay with whatever routes the request
• The Developer plan runs on Fiddler's cloud only; VPC and on-premise start at Enterprise
• Free tier includes no observability at all

Pricing: Free covers real-time guardrails only. Developer is $0.002 per trace, adding unified observability across agentic and predictive systems, tests and experiments, custom evaluators, RBAC, and SSO on SaaS. Enterprise is custom, adding enterprise-grade guardrails, VPC and on-premise deployment, and a named customer success manager. Annual commitment required.

11. Coralogix

Coralogix processes telemetry in flight through its Streama engine and writes everything to your own S3 bucket in open Parquet, which makes retention effectively infinite and archive queries free of additional quota. The TCO Optimizer routes each log source to Frequent Search, Monitoring, or Compliance, so a CDN access log and a payment error do not cost the same. There are no tiers: RBAC, SAML SSO, audit trail, and 24/7 support are included on every account.

Pros:

• Data lands in your own bucket, so retention is unlimited and there is no rehydration charge
• No per-host, per-user, per-query, or per-dashboard fees, with unlimited users and hosts
• AI Center adds evaluators, guardrails, and token cost tracking on the same platform

Cons:

• Quota is enforced daily, and exceeding it without Pay-As-You-Go blocks ingestion until 00:00 UTC
• Unused units and tokens expire at term end with no carryover, refund, or credit
• Two overlapping pricing presentations, headline per-GB rates and the unit system, take some reading to reconcile

Pricing: Headline rates are $0.42 per GB for logs, $0.16 for traces, $0.05 for metrics where 1 GB equals 1,000 time series, and $1.50 per million tokens for AI evaluation. Billing runs on units at $1.50 each, where one unit buys 1.3 GB of Frequent Search logs at $1.15 per GB or 3 GB of Monitoring logs at $0.50 per GB. A 14-day trial includes 8 units with no credit card.

12. OpenObserve

OpenObserve unifies logs, metrics, traces, RUM, session replay, and error tracking in one Rust-built platform with SQL querying instead of a proprietary language. The self-hosted Enterprise edition is free up to 50 GB a day and includes SSO, RBAC, federated search, and audit trail, which are paid add-ons or absent elsewhere. Cloud dropped its free tier in June 2025 in favor of fully usage-based pricing.

Pros:

• Self-hosted Enterprise free to 50 GB/day with SSO, RBAC, cipher keys for HIPAA and PCI, and audit trail included
• SQL querying and native OTLP ingest, so no new query language and no agent replacement
• Bring Your Own Bucket connects your own S3 or Azure Blob storage

Cons:

• Smaller integration ecosystem and community than established platforms
• Self-hosting at scale requires infrastructure expertise your team has to supply
• No permanent Cloud free tier, only a 14-day trial

Pricing: Open source is free forever, self-hosted with no usage limits. Self-Hosted Enterprise is free to 50 GB per day, then contact sales. Cloud Pay As You Go is $0.50 per GB ingested and $0.01 per GB queried, reflecting a 30% annual-commitment discount, with 15-month metrics retention and 30 days for logs, traces, and RUM, extendable at $0.02 per GB per 30 days. SSO is included.

Frequently Asked Questions

What is the best Dynatrace alternative?

It depends on which Dynatrace problem you are solving, and for a growing number of teams the answer is not another host monitor. If your production traffic runs through models, Respan is the alternative that continues the trace past the API call into the prompt version, the tool result, and the retry, with cost and eval scores attached to the same span. Teams whose problem is purely ingest volume look at Chronosphere and Coralogix for data shaping, and teams punished by per-host billing on container-dense estates look at New Relic and Grafana. Most stacks running LLM features need the AI layer covered either way.

Datadog vs Dynatrace: which should you choose?

Dynatrace automates more and Datadog covers more. Dynatrace deploys one agent and produces topology, baselines, and root cause suggestions with less configuration, which suits large hybrid estates with a dedicated observability team. Datadog offers a wider product surface and an easier incremental start, but each product is a separate meter, so a stack of Infrastructure, APM, logs, and RUM accumulates four charges per host. On cost, Dynatrace bundles APM, infrastructure, Kubernetes, and OpenTelemetry into Full-Stack at $0.01 per memory-GiB-hour, while a comparable Datadog setup runs $46 to $58 per host before logs. Datadog is usually the easier adoption; Dynatrace is usually the deeper automation.

How much does Dynatrace cost?

Dynatrace runs on a consumption model with an unpublished minimum annual commitment. Rate-card entries include Full-Stack Monitoring at $58 per month for an 8 GiB host, Infrastructure Monitoring at $29 per host, log ingest at $0.20 per GiB, RUM at $2.25 per 1,000 sessions, and browser synthetics at $4.50 per 1,000 actions. Unlimited users are included. Real cost depends on how many capabilities you enable, since each draws against the same commitment separately.

Can you migrate off Dynatrace without re-instrumenting?

Partly. If your services already emit OpenTelemetry, most platforms here accept OTLP directly, so you point the Collector at a new backend without touching application code. Anything relying on OneAgent auto-instrumentation has to be re-instrumented. The larger risk is not the agent but the integrations nobody documented: CI/CD quality gates that block deploys on Dynatrace scores, autoscaling scripts triggered by Dynatrace metrics, and alert routing rules. Map every system consuming Dynatrace telemetry through its API before cutting over, run both platforms in parallel for a full business cycle so the new baselines learn your monthly peaks, and validate that your top SLIs reproduce before turning the old one off.

Is there a free Dynatrace alternative?

Respan's free tier covers 100k logs, 1k evaluation scores, and unlimited seats with no credit card, which is enough to instrument a production agent and start scoring its output. Among the infrastructure platforms, Grafana Cloud's free tier includes 10k active series and 50 GB each of logs and traces, New Relic includes 100 GB monthly ingest, Honeycomb covers 20M events, and OpenObserve is free to self-host. Dynatrace itself offers only a trial and a public sandbox.

Does Dynatrace cover LLM and AI agent observability?

Dynatrace ingests OpenTelemetry GenAI data and reports token usage and model latency alongside infrastructure signals. What it is not built around is the agent run as the primary unit: the prompt version that produced an output, the tool call that returned the wrong shape, the retry chain, and the evaluation score attached to a specific response. Platforms built around the model call treat those as one object rather than three systems to correlate. Respan does this while also covering the gateway, so routing, tracing, and evals share the same record of what happened.