Comparison

Tayga is a focused tool: it turns traces and logs into explanations of failing and slow requests. The products below are broader, and for many teams one of them is the better choice. This page says where Tayga fits and where it does not.

How this comparison was made

Every competitor fact comes from a primary source: the vendor’s docs, pricing page or blog, or the repository’s license read through the GitHub API. All sources were accessed on 2026-10-07; products and prices change, so check the linked source before you rely on a cell. Where a source does not state something, the cell says “not stated”. Nothing is taken from third-party reviews. Tayga’s own column is sourced to its repository.

Summary

• No other product in this set documents per-request error stories built from traces as they stream in, with a deterministic root cause. The closest are Coroot, whose root-cause analysis runs a non-LLM step and then has an LLM summarise it (Enterprise edition), and Dynatrace, which describes its root-cause analysis as “causal AI” over its topology model. Both are much broader products than Tayga.
• Log template mining is common, and so is alerting on it. Grafana Loki, ClickStack, Datadog, Dynatrace, New Relic, Coroot and OpenObserve (Enterprise) all group logs into patterns. ClickStack, Datadog’s Log Patterns view and Dynatrace mine at query time; Loki’s optional pattern ingester and Coroot’s node agent mine as logs arrive. Datadog’s Watchdog Log Anomaly Detection also analyses logs at intake, finds new warning and error patterns and increases in existing ones, and can alert through a Watchdog logs monitor. New Relic supports NRQL and anomaly alert conditions on its log patterns. Tayga mines every service’s logs at ingest, keeps the templates, and alerts on new, spiking and (opt-in) silent ones, in its open-source core.
• The commercial and newer open-source products are moving to LLM agents for investigation. Tayga uses no LLM: its answers are rules over span data, so the same input gives the same output.
• Every other product is broader than Tayga. All of them except Jaeger cover metrics and dashboards at least, and several add eBPF instrumentation, profiling, RUM or session replay, and years of production use. Tayga needs OpenTelemetry traces and logs, has no metrics store, and has been tested against the OpenTelemetry demo.

Matrix

“S” = analysis while data streams in (at ingest). “Q” = analysis at query time.

Product	Analysis	Automatic RCA / error explanation	LLM in RCA?	Log template mining	Self-host	License	Pricing model	Instrumentation
Tayga	S 1	Yes, per request: error and slow stories 2	No, deterministic rules 3	Yes: Drain at ingest, with new/spike/silence alerts 4	Yes: Docker Compose and Helm (see Install) 5	AGPL-3.0-only 6	Free core; enterprise on request 7	OTel (OTLP) 8
Jaeger	Q 9	Not stated (critical-path view only) 10	n/a 11	No (traces only) 12	Yes 13	Apache-2.0 14	Free open source 15	OTel, Jaeger, Zipkin 16
Grafana LGTM	Q, plus S for Tempo’s metrics-generator and Loki patterns 17	Self-hosted: not stated. Cloud: Sift and Assistant 18	Assistant is an LLM; Sift’s method is not stated 19	Yes: Loki pattern ingester (Drain, off by default) 20	Yes; Sift and Assistant need Grafana Cloud 21	AGPL-3.0 22	Cloud: per GB, per series, per user 23	OTel, Jaeger, Zipkin 24
SigNoz	Q 25	Noz (AI teammate) 26	Yes 27	Not in current docs (“soon” in a blog) 28	Yes (Community) 29	MIT, plus an ee/ license 30	Per GB / per sample; no per seat 31	OTel 32
Coroot	Logs S on the node; RCA per investigation 33	Yes: AI-powered RCA 34	Hybrid: non-LLM analysis, then an LLM summary 35	Yes, on the node agent 36	Yes 37	Apache-2.0 38	Per CPU core (Enterprise) 39	eBPF plus OTel 40
OpenObserve	Q 41	SRE Agent (Enterprise) 42	Yes (LLM providers) 43	Yes, Log Patterns (Enterprise) 44	Yes 45	AGPL-3.0 46	Per GB ingested and queried 47	OTel 48
ClickStack / HyperDX	Q 49	Not stated (AI Notebooks beta, MCP server) 50	AI Notebooks (prompts) 51	Yes: Drain3, query time, sampled 52	Yes 53	MIT (HyperDX), Apache-2.0 (ClickHouse) 54	Managed: compute plus storage 55	OTel 56
Datadog	Logs: Watchdog at intake; otherwise not stated 57, 58	Watchdog RCA (needs APM); Bits AI 59	Bits AI is an AI agent; Watchdog’s method is not stated 60	Yes: Patterns, Q, 10,000 samples 61; Watchdog Log Anomaly Detection at intake, with new-pattern and spike alerts (Watchdog logs monitor) 58	Not stated 62	Proprietary 63	Per host, per GB, per indexed event 64	Agent or OTel 65
Dynatrace	Not stated 66	Causal AI RCA 67	RCA page: no LLM mentioned; a separate GenAI assistant 68	Yes, Q (Preview) 69	Yes (Managed) 70	Proprietary 71	Platform subscription; per host-hour, per GiB 72	OneAgent or OTel 73
New Relic	Not stated 74	Autopilot 75	Yes (GenAI terms) 76	Yes: ML model per account; NRQL and anomaly alerts on patterns 77	Not stated 78	Proprietary 79	Per GB plus per user, or compute 80	APM agents or OTel 81

Where Tayga is the better fit

Only claims backed by the matrix:

• Per-request error and slow stories, built while traces stream in. Each story has a root-cause span, a critical path and a comparison with the endpoint’s baseline ¹². None of the other nine documents per-request stories built at ingest; their RCA runs per investigation or alert (Coroot, OpenObserve, Datadog, New Relic), or the docs do not say when it runs ^33425975.
• Deterministic, no LLM. The root cause comes from fixed rules over the span tree ³. Coroot’s RCA ends in an LLM summary ³⁵; Grafana Assistant, SigNoz Noz, OpenObserve’s SRE Agent, Datadog’s Bits AI and New Relic Autopilot are AI or LLM agents ^1927436076.
• Log templates and log alerts in the open-source core. Tayga mines every service’s logs at ingest and alerts on new, spiking and opt-in silent templates, delivered to your own webhooks and Slack ⁴⁶. Datadog’s Watchdog Log Anomaly Detection also works at intake and alerts on new and increasing warning or error patterns ⁵⁸, and New Relic alerts on log patterns through NRQL and anomaly conditions ⁷⁷; both are proprietary platforms ⁶³⁷⁹. Among the open-source products, Loki’s pattern ingester is off by default ²⁰, ClickStack mines patterns at query time over a sample ⁵², and OpenObserve’s Log Patterns are Enterprise ⁴⁴.
• Stories and log alerting are in the self-hosted AGPL core ⁴⁶. OpenObserve’s patterns and RCA agent are Enterprise ⁴²⁴⁴; Coroot’s AI RCA is Enterprise, or the Community Edition through Coroot Cloud ³⁴; Grafana’s Sift and Assistant need Grafana Cloud ²¹.

Where another product is the better choice

• You need metrics and dashboards. Every product here except Jaeger covers general metrics and dashboards; Tayga has neither (Grafana and Prometheus are optional extras next to it) ⁸². SigNoz, OpenObserve and ClickStack each cover traces, metrics and logs in one product ²⁵⁴¹⁵³.
• You cannot add OpenTelemetry instrumentation. Coroot’s eBPF agent traces services without SDKs ⁴⁰, and Dynatrace has OneAgent ⁷³. Tayga needs OTel traces and logs ⁸.
• Your root causes are in the infrastructure. Coroot walks the dependency graph across infrastructure signals ³⁵, Dynatrace evaluates its whole causal topology ⁶⁷, and Datadog Watchdog RCA works across APM and infrastructure ⁵⁹. Tayga’s root cause is always a span.
• You want a hosted service. Grafana Cloud, SigNoz Cloud, OpenObserve Cloud, Managed ClickStack, Datadog, Dynatrace and New Relic all offer one ^{23314755647280}. Tayga has none.
• You need proven scale. Jaeger is a CNCF graduated project ¹⁵, and the others support Kubernetes and Helm or managed scale ¹³²⁹³⁷. Tayga is young and has been tested against the OpenTelemetry demo ⁸².

Tayga can also sit next to one of them: it takes OTLP from the same Collector, and its trace view links out to Jaeger.

What could not be verified

These were left out of the matrix or marked “not stated”:

• Datadog, Dynatrace and New Relic: whether analysis runs at ingest or at query time, outside log patterns and Datadog’s Watchdog Log Anomaly Detection.
• New Relic: when log patterns are assigned (the docs say a pattern is “enriched onto the existing log message” as newrelic.logPattern, but not when), and whether any alert fires on a pattern’s first appearance (the docs point to a “Logs with no pattern” tab for logs “appearing for the first time”).
• Dynatrace: its log alerts use event matchers, log metrics or DQL queries that you define ⁸³; the sources fetched do not describe an alert on Patterns (Preview) results.
• Datadog: whether Watchdog Log Anomaly Detection covers logs other than warning and error status, or alerts when a pattern goes silent.
• Datadog Watchdog RCA and Grafana Sift: whether they use an LLM.
• Datadog: the price of Bits AI.
• Datadog and New Relic: an explicit statement that no self-hosted edition exists; only its absence from the pricing pages was checked.
• SigNoz: whether Noz runs on self-hosted installs (the docs disagree).
• Coroot: whether the Community Edition gets AI RCA through Coroot Cloud (the docs and the editions page disagree).
• OpenObserve Log Patterns: ingest time or query time.
• ClickStack: alerting on new event patterns.
• Jaeger: trace comparison (not found in the current docs).

Sources

All web sources were accessed on 2026-10-07. Repository sources are files of the Tayga repository.

Footnotes

1. Tayga streaming analysis: the assembler closes per-trace session windows from a Redpanda topic and writes stories; the logminer runs detection every 60 s. See Architecture. ↩ ↩²

2. Tayga stories: Error stories. ↩ ↩²

3. Tayga deterministic: pure functions in tayga-analysis (its crate doc: “no I/O, no clocks”; the root cause in rootcause.rs). See Root cause and critical path. ↩ ↩²

4. Tayga logs: Log templates, Log alerts, The notifier. ↩ ↩² ↩³

5. Tayga self-hosting: deploy/standalone/compose.yaml, make up, and the Helm chart in deploy/helm/tayga. ↩

6. Tayga license: LICENSE (AGPL-3.0), LICENSING.md. See License. ↩ ↩² ↩³

7. Tayga pricing: LICENSING.md (commercial license on request); no published prices. See Enterprise. ↩

8. Tayga instrumentation: OTLP gRPC and OTLP/HTTP in tayga-ingest. ↩ ↩²

9. Jaeger trace search and UI: https://www.jaegertracing.io/docs/latest/ ↩

10. Jaeger critical path: https://www.jaegertracing.io/docs/2.dev/deployment/frontend-ui/ ↩

11. Jaeger lists no RCA feature: https://www.jaegertracing.io/docs/1.76/features/ ↩

12. Jaeger is a tracing platform: https://www.jaegertracing.io/docs/latest/ ↩

13. Jaeger Docker images, operator and Helm: https://www.jaegertracing.io/docs/1.76/features/ ↩ ↩²

14. Jaeger license: https://github.com/jaegertracing/jaeger (GitHub API) ↩

15. Jaeger, a CNCF graduated project: https://www.jaegertracing.io/docs/latest/ ↩ ↩²

16. Jaeger receivers: https://www.jaegertracing.io/docs/latest/architecture/ ↩

17. TraceQL, LogQL, Tempo metrics-generator, Loki pattern ingester: https://grafana.com/docs/tempo/latest/, https://grafana.com/docs/loki/latest/, https://grafana.com/docs/loki/latest/get-started/components/ ↩

18. Sift and Assistant: https://grafana.com/docs/grafana-cloud/machine-learning/sift/ and https://grafana.com/docs/grafana-cloud/machine-learning/assistant/ ↩

19. Assistant is “a purpose-built LLM”: https://grafana.com/docs/grafana-cloud/machine-learning/assistant/; the Sift docs state no method: https://grafana.com/docs/grafana-cloud/machine-learning/sift/analyses/ ↩ ↩²

20. Loki pattern ingester, “disabled by default”: https://grafana.com/docs/loki/latest/get-started/components/ ↩ ↩²

21. Sift is “included in Grafana Cloud”; Assistant needs a Cloud backend: https://grafana.com/docs/grafana-cloud/machine-learning/sift/, https://grafana.com/docs/grafana-cloud/machine-learning/assistant/ ↩ ↩²

22. Grafana, Loki and Tempo licenses: https://github.com/grafana/grafana, https://github.com/grafana/loki, https://github.com/grafana/tempo (GitHub API) ↩

23. Grafana Cloud pricing: https://grafana.com/pricing/ ↩ ↩²

24. Tempo protocols: https://grafana.com/docs/tempo/latest/ ↩

25. SigNoz query and storage: https://signoz.io/docs/architecture/ ↩ ↩²

26. Noz: https://signoz.io/docs/ai/overview/ ↩

27. Noz, “SigNoz’s AI teammate”: https://signoz.io/docs/ai/overview/ ↩ ↩²

28. SigNoz logs docs and blog: https://signoz.io/docs/logs-management/overview/, https://signoz.io/blog/improvements-to-logs-search-and-filter/ (the post shows no date) ↩

29. SigNoz self-hosting options: https://signoz.io/docs/introduction/ ↩ ↩²

30. SigNoz LICENSE: https://github.com/SigNoz/signoz ↩

31. SigNoz pricing: https://signoz.io/pricing/ ↩ ↩²

32. SigNoz OTel Collector ingest: https://signoz.io/docs/architecture/ ↩

33. Coroot on-node log analysis and the RCA flow: https://docs.coroot.com/logs/overview/, https://coroot.com/blog/we-built-ai-powered-root-cause-analysis-that-actually-works/ ↩ ↩²

34. Coroot AI RCA: https://docs.coroot.com/ai/ and https://coroot.com/editions ↩ ↩²

35. Coroot RCA steps: https://coroot.com/blog/we-built-ai-powered-root-cause-analysis-that-actually-works/ ↩ ↩² ↩³

36. Coroot log patterns: https://docs.coroot.com/logs/overview/ ↩

37. Coroot installation options: https://docs.coroot.com/installation/architecture/ ↩ ↩²

38. Coroot license: https://github.com/coroot/coroot, https://github.com/coroot/coroot-node-agent (GitHub API and LICENSE) ↩

39. Coroot pricing: https://coroot.com/pricing/, https://coroot.com/editions ↩

40. Coroot eBPF and OTLP: https://docs.coroot.com/tracing/ebpf-based-tracing/, https://docs.coroot.com/configuration/coroot-node-agent/, https://docs.coroot.com/tracing/opentelemetry-go/ ↩ ↩²

41. OpenObserve SQL and PromQL: https://openobserve.ai/docs/ ↩ ↩²

42. OpenObserve SRE Agent (Enterprise): https://openobserve.ai/docs/enterprise-setup/enterprise-features/ ↩ ↩² ↩³

43. OpenObserve LLM providers: https://openobserve.ai/ai-sre/ ↩ ↩²

44. OpenObserve Log Patterns (Enterprise): https://openobserve.ai/docs/enterprise-setup/enterprise-features/ ↩ ↩² ↩³

45. OpenObserve self-hosted plans: https://openobserve.ai/pricing/ ↩

46. OpenObserve license: https://github.com/openobserve/openobserve (GitHub API and LICENSE) ↩

47. OpenObserve pricing: https://openobserve.ai/pricing/ ↩ ↩²

48. OpenObserve OTel: https://openobserve.ai/docs/ ↩

49. ClickStack query-time patterns: https://clickhouse.com/blog/event-patterns-clickstack ↩

50. ClickStack AI Notebooks and MCP server: https://clickhouse.com/blog/whats-new-in-clickstack-may-2026 ↩

51. AI Notebooks “combine prompts, queries …”: https://clickhouse.com/blog/whats-new-in-clickstack-may-2026 ↩

52. Drain3, query time, 10,000 events: https://clickhouse.com/blog/event-patterns-clickstack ↩ ↩²

53. Open Source ClickStack, self-managed: https://clickhouse.com/docs/use-cases/observability/clickstack/overview ↩ ↩²

54. HyperDX, ClickHouse and ClickStack licenses: https://github.com/hyperdxio/hyperdx, https://github.com/ClickHouse/ClickHouse, https://github.com/ClickHouse/ClickStack (GitHub API) ↩

55. ClickStack managed pricing: https://clickhouse.com/pricing ↩ ↩²

56. ClickStack OTel Collector: https://clickhouse.com/docs/use-cases/observability/clickstack/overview ↩

57. Datadog analysis timing: Watchdog Log Anomaly Detection analyses logs “at the intake level” ⁵⁸; for everything else, not stated in the Datadog sources below. ↩

58. Watchdog Log Anomaly Detection, “analyzed at the intake level”, “an emergence of logs with a warning or error status”, “a sudden increase”, alerts through “a Watchdog logs monitor”: https://docs.datadoghq.com/logs/explorer/watchdog_insights/; anomaly types “new log patterns and increases in existing log patterns”: https://docs.datadoghq.com/watchdog/alerts/ ↩ ↩² ↩³ ↩⁴

59. Watchdog RCA and Bits: https://docs.datadoghq.com/watchdog/rca/, https://docs.datadoghq.com/bits_ai/bits_ai_sre/ ↩ ↩² ↩³

60. Bits is “an autonomous AI agent”: https://docs.datadoghq.com/bits_ai/bits_ai_sre/; Watchdog’s method is not stated: https://docs.datadoghq.com/watchdog/rca/ ↩ ↩²

61. Datadog Log Patterns: https://docs.datadoghq.com/logs/explorer/analytics/patterns/ ↩

62. Datadog self-hosting: no such option on https://www.datadoghq.com/pricing/list/ ↩

63. Datadog license: no open-source license stated on https://www.datadoghq.com/pricing/list/ ↩ ↩²

64. Datadog list prices: https://www.datadoghq.com/pricing/list/ ↩ ↩²

65. Datadog OTel options: https://docs.datadoghq.com/opentelemetry/ ↩

66. Dynatrace analysis timing: not stated in the Dynatrace sources below. ↩

67. Dynatrace RCA: https://docs.dynatrace.com/docs/dynatrace-intelligence/root-cause-analysis ↩ ↩²

68. Dynatrace RCA page and the GenAI page: https://docs.dynatrace.com/docs/dynatrace-intelligence/root-cause-analysis, https://docs.dynatrace.com/docs/dynatrace-intelligence/agentic-and-generative-ai/agentic-and-generative-ai-getting-started ↩

69. Dynatrace Patterns (Preview), “a read-time view”: https://docs.dynatrace.com/docs/analyze-explore-automate/logs/lma-logs-app/patterns ↩

70. Dynatrace Managed, on-premise: https://www.dynatrace.com/company/trust-center/sla/managed/ ↩

71. Dynatrace license: no open-source license stated on https://www.dynatrace.com/pricing/ ↩

72. Dynatrace pricing: https://www.dynatrace.com/pricing/ ↩ ↩²

73. Dynatrace OTLP and OneAgent: https://docs.dynatrace.com/docs/ingest-from/opentelemetry ↩ ↩²

74. New Relic analysis timing: not stated in the New Relic sources below. ↩

75. New Relic Autopilot: https://docs.newrelic.com/docs/agentic-ai/sre-agent/overview/ ↩ ↩²

76. “Subject to the Generative AI Service Specific Terms”: https://docs.newrelic.com/docs/agentic-ai/sre-agent/overview/ ↩ ↩²

77. New Relic log patterns, “Create alerts for patterns by adding NRQL alerts” and “Use anomaly alert conditions to detect anomalies in known log patterns”: https://docs.newrelic.com/docs/logs/ui-data/find-unusual-logs-log-patterns/ ↩ ↩²

78. New Relic self-hosting: no such option on https://newrelic.com/pricing ↩

79. New Relic license: no open-source license stated on https://newrelic.com/pricing ↩ ↩²

80. New Relic pricing: https://newrelic.com/pricing ↩ ↩²

81. New Relic OTLP endpoint: https://docs.newrelic.com/docs/opentelemetry/best-practices/opentelemetry-otlp/ ↩

82. Tayga limits: Grafana and Prometheus only with make up-extras; tested against the OpenTelemetry demo 3.1.0. ↩ ↩²

83. Dynatrace log alerts (events extracted from logs, metrics based on logs, DQL in custom alerts): https://docs.dynatrace.com/docs/analyze-explore-automate/logs/alerting-on-logs ↩