Vendor Lock-In

Decouple collection from your SIEM and APM

Proprietary query languages, vendor agents, and platform-specific schemas tie your data to one tool. A vendor-neutral control layer separates collection from consumption so you can change platforms without rebuilding how you collect.

Why is switching observability platforms so expensive?

Switching is expensive because collection and consumption are welded together. SPL is specific to Splunk, KQL is specific to Microsoft Sentinel, and Datadog ships proprietary agents with custom metrics that commonly run 30-52% of the bill. Each vendor builds its own schema, so the integrations you build do not move with you.

The skills and queries your team writes stay locked to one platform. When renewal economics shift, often around a 9% annual uplift on a typical Splunk contract, you face a multi-month project to rebuild collection, parsing, and routing from the ground up.

For MSPs the cost compounds. Different clients run different platforms, so you carry parallel expertise across Splunk, Microsoft Sentinel, and Datadog at once, with no shared collection layer underneath any of them.

40-70%

Typical ingest reduction once a control layer normalizes and filters at the source

30-52%

Datadog custom metrics as a share of spend, an industry benchmark

2 weeks

Yale New Haven Health moved 30,000+ endpoints to Microsoft Sentinel (published)

How do you change SIEM or APM without redoing collection?

You put a vendor-neutral control layer in front of the stack so sources send once and the layer routes everywhere. Collection stays constant while the destination becomes a routing decision, which is what makes a future change a parallel run plus a routing change rather than a full rebuild.

Logmetry architects this layer to sit in front of your SIEMs and APMs as control, never in place of them. We work across Splunk, Microsoft Sentinel, Datadog, and Cribl, and one pipeline architecture serves every destination instead of duplicated expertise per platform.

  • Route to many destinations from one collection layer, including Splunk, Microsoft Sentinel, Datadog, object storage, and more
  • Normalize data to open schemas (ECS, ASIM, CIM) in the pipeline so it arrives ready for whichever platform you choose
  • Test a replacement SIEM or APM with copies of live data in parallel, without touching production
  • Migrate one source at a time with a full rollback path at every step, no big bang cutover
  • Consolidate endpoint collection on one agent layer that feeds any destination instead of stacking vendor forwarders

How Logmetry architects freedom of choice

Zbigniew Gajuk, Co-Founder and Chief Observability and Security Architect, has led this work at Fortune 500 scale across 26+ years. We review the environment first, recommend the right control layer for it with honest pros and cons, then implement at config-level depth so the result is durable freedom, not a new dependency.

1Architecture Review: map current platform dependencies, proprietary query libraries, custom integrations, and the agent footprint across your sources
2Design and Recommend: design a vendor-neutral pipeline that normalizes to open formats and routes to any destination, and present the tradeoffs so you choose the platform
3Implement and Validate: build the control layer in production, then run your existing platform alongside alternatives and compare coverage and cost on live data
4Migrate and Optimize: move source by source with rollback at each step, then govern the layer so the next migration becomes a routing change

Frequently asked questions

Does a control layer just create new vendor lock-in?

No. The control layer processes data in standard formats such as JSON, syslog, and raw, then outputs to open protocols and APIs. Your data is never held in a proprietary store. If you stopped using the layer, your sources would connect directly to destinations as they did before.

Can I test a new SIEM or APM without affecting production?

Yes. Multi-destination routing sends copies of live data to a candidate platform while your production tool keeps running unchanged. You compare detection coverage, parsing, and cost on real traffic, then commit only after the alternative proves itself in your environment.

How does this reduce the cost of a future migration?

Once collection is decoupled from consumption, a migration becomes a parallel run plus a routing change instead of an 18-month rebuild. Yale New Haven Health moved 30,000+ endpoints to Microsoft Sentinel in 2 weeks and cut SIEM spend 40% (published). Your timeline depends on your environment.

Is Logmetry a SIEM or a managed SIEM service?

Neither. We are a vendor-neutral team of observability and security architects. We design, implement, and run the control layer that sits in front of your SIEMs and APMs, and we stay expert across Splunk, Microsoft Sentinel, Datadog, and Cribl so the recommendation fits your environment.

Ready to explore this further?

Let's discuss how this applies to your environment.

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