Prompt

Are there any synthetic monitoring tools that handle long-term log retention across multi-cloud client workloads?

Professional Services · IT Services / It services1 observationLast seen Jul 27, 2026

Latest observation

Jul 27, 2026 · OpenAI APIWeb search: off

Yes — but with an important caveat: synthetic monitoring tools usually don’t provide “long-term log retention” as a first-class strength on their own. They’re best at scheduled tests, uptime/latency checks, and user-journey monitoring. For multi-cloud client workloads plus long-term retention, the common pattern is:

  1. Synthetic monitoring tool for test execution and alerts
  2. Central log/observability platform for retention, search, and compliance storage

Tools that fit well in this kind of setup

These are often used with multi-cloud environments and can export or integrate results into long-term storage:

  • Datadog Synthetic Monitoring
    Good multi-cloud coverage, strong integrations, and you can keep data longer using Datadog’s retention features or export pipelines.

  • New Relic Synthetics
    Useful for distributed monitoring across cloud providers, with longer-term observability via New Relic’s platform and log forwarding.

  • Dynatrace Synthetic Monitoring
    Strong enterprise option for multi-cloud and hybrid environments, with broader observability and retention capabilities.

  • Checkly
    Great for API/browser synthetics and CI/CD-driven monitoring; retention is usually handled by integrations/export rather than being the main feature.

  • Pingdom / Uptrends / Catchpoint Solid synthetics platforms; for long-term archival you’ll generally pair them with SIEM, log lake, or observability storage.

What to look for

If your requirement is specifically “retain synthetic run logs for months/years across multiple clouds”, check for:

  • API access or webhooks to export check results
  • Native log forwarding to:
    • AWS S3 / Glacier
    • Azure Blob Storage / Archive
    • Google Cloud Storage / Coldline / Archive
    • Splunk / Elastic / OpenSearch / Datadog / New Relic
  • Per-region or per-cloud execution
  • Evidence retention / audit trails
  • Compliance support (SOC 2, HIPAA, ISO, etc., if relevant)

Practical recommendation

For most teams, the best architecture is:

  • Synthetic platform: Datadog, Dynatrace, New Relic, Catchpoint, or Checkly
  • Retention layer: S3/Blob/GCS + SIEM/log analytics (Splunk, Elastic, OpenSearch, etc.)

If you want, I can also give you:

  1. a shortlist by budget,
  2. a comparison table, or
  3. a reference architecture for long-term retention across AWS/Azure/GCP.

Observation history

Unlock observation history

Create a free Obsurfable account to unlock historical responses, model comparisons, and deeper brand intelligence.

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, and Meta AI.