The real cost of migrating observability tools [Q&A]

Enterprises that have relied on the same observability platform for years face a harsh reality when it’s time to move on: migration is harder than it should be. That’s because it’s not just about shifting data, it’s about reconstructing your monitors, dashboards, alerts, and workflows across sprawling, interconnected environments.

We spoke to Shahar Azulay, CEO and co-founder of groundcover to discuss his observations on the real cost of migrating observability tools.

BN: What are the hidden costs or risks that enterprises often overlook when planning a migration from a legacy SaaS observability provider?

SA: ​​When enterprises plan a migration from a legacy SaaS observability provider, they often underestimate the complexity beyond just moving data. Reconstructing monitors, dashboards, alerts, and workflows across sprawling, interconnected environments introduces hidden labor and risk. Projects regularly stretch from months to over a year, and because the processes are brittle and manual, organizations often end up paying for expensive professional services to manage them.

Another hidden risk comes from fragmentation: legacy SaaS models frequently split telemetry into separate products, each with its own storage, licensing, and billing. That fragmentation makes reconciliation during migration more error-prone and financially risky. Add to that unpredictable pricing structures and usage-based ‘gotchas’ (e.g. surprise fees tied to ingestion, retention, or export), and the cost forecast can inflate dramatically.

There are also technical and compatibility risks. Differences in how data is collected or represented may prevent a one-to-one translation; dashboards may break (especially given subtle feature discrepancies); schema and legacy quirks can lead to data loss; and custom resources (annotations, bespoke configurations) cannot simply be dropped — they must be remapped and validated. Often, organizations also discover at migration time that many dashboards or workflows are already stale or unused, forcing them to make decisions on what to retire or rebuild.

All of these ‘hidden’ costs — in time, effort, expertise, risk, and surprise financial exposure — tend to be underappreciated until it’s too late.

BN: How can organizations begin to quantify the technical debt associated with their current, long-standing observability platform?

SA: The first step is visibility. Most organizations don’t even realize how much of their observability stack is stale or redundant until they try to migrate. That’s why one of the most valuable early outputs of our migration tool is an inventory and assessment report. It automatically discovers every dashboard, monitor, and workflow while highlighting which ones are active, stale, or broken.
From there, teams can quantify technical debt in concrete ways:

• Redundancy: How many monitors or dashboards are duplicated or overlapping.
• Obsolescence: How many haven’t fired or been viewed in months.
• Complexity: How many dependencies or integrations exist per monitor or workflow.

When those findings are expressed as ratios, for example, 40 percent of dashboards unused or 30 percent of alerts stale, leadership gains a measurable view of operational drag. That’s your technical debt in the observability layer. It’s often the best business case for modernization: migrating isn’t just about moving forward, it’s about cleaning house.

BN: If adopting an SaaS platform, which signs of future vendor lock-in should a procurement team watch out for?

SA: When your organization is evaluating a SaaS observability platform, some of the biggest lock-in risks are already baked into what you’d think of as ‘normal’ provider behavior in legacy observability systems. For example, unpredictable pricing and usage-based ‘gotchas’ are especially dangerous. If you get hooked on a vendor whose costs balloon once you’re deeply invested — through metric ingestion, long-term retention, dashboards, agents, or alerts that rely on their proprietary logic — moving away becomes expensive and painful.

Another red flag is fragmentation: when telemetry (logs, metrics, traces) is split across multiple products or modules, each with its own storage, licensing, and billing. That kind of setup introduces friction: you’re not dealing with just one system, but many interdependent ones, which increases complexity every time you consider migration.

Also, watch for subtle incompatibilities — differences in how data is collected, how dashboards or alerts are implemented — that mean you can’t simply ‘lift and shift.’ If monitors or dashboards built in the old system rely heavily on vendor-specific features that aren’t available elsewhere, you will pay in time and rework if you switch.

Finally, reliance on professional services and brittle, manual processes is itself a sign of lock-in. If your migration or your operational workflows cannot be managed without experts on the vendor’s stack, switching becomes not just a technical task but a logistics and staffing headache.

BN: What features should enterprises be looking for to ensure a future-proof solution?

SA: To build an observability platform that truly stands the test of time — one that’s resilient to change and makes migrations manageable rather than monstrous — there are certain features you’ll want to demand from the start.

One of the big ones is support for aligned data sources — meaning the new system should be able to accept the same kinds of telemetry (metrics, logs, events) with matching configurations (names, types) so that data carries over cleanly. If you can’t carry over your existing data sources, you’ll lose context or have to rebuild lots of it.

Visual compatibility matters too. Dashboards and panels tend to hide subtle differences: things like how charts render, thresholds work, and what widgets are available. A future-proof platform is one where dashboards built in your legacy system don’t break due to feature mismatches, or where mismatches are obvious and manageable.

Similarly, parity in collection methods is critical. If data is collected or represented differently in the target system (differences in granularity, units, or semantics), translating monitors/alerts can become a full-time job. So look for platforms that match or closely mimic your existing collection style.

Also, don’t underestimate support for custom resources — annotations, custom configuration, metadata. If your new platform can map and validate those, you avoid having to throw away context or start from scratch.

Automated or AI-assisted migration capabilities are also essential. When legacy processes are brittle, manual, and handled by armies of experts, that’s a recipe for cost overruns and delays. A system that can standardize incompatibilities, automate much of the translation of dashboards, monitors, and alerts, or suggest equivalent features when perfect matches don’t exist will save huge effort.

Finally, the ability to detect incompatibilities ahead of time is a big plus. If during the evaluation you can inventory version and product differences, see where translation or mapping will be hard, and get a sense of where you’ll need work — that helps you plan. And when perfect feature-by-feature mapping isn’t possible, it helps a lot if the system can intelligently propose fallback or alternative solutions, instead of just failing outright.

BN: How can automation be used to reconstruct complex monitors, dashboards, and alerts, and what kind of tools or frameworks are needed?

SA: Automation becomes the cornerstone of a scalable, reliable migration approach. First, you can automatically inventory version and product differences. That enables the system to pinpoint incompatibilities without requiring every assumption to be manual. Once those differences are understood, you can apply a mapping layer to standardize and automate the translation of dashboards, monitors, and alerting logic. For example, query languages in the legacy platform can be translated automatically, and when a perfect match does not exist, AI can propose the closest efficient alternative.

Under the hood, the tools or frameworks needed include:

• A discovery engine that parses existing dashboards, monitors, alerts, pipelines, and metadata to build a representation of the legacy system’s configuration.
• A mapping / translation framework that knows how to map legacy queries, metrics, and alert logic into the target system’s constructs.
• An AI-assisted translator to handle edge cases, vendor-specific features, or nontrivial conversions.
• A validation / reconciliation layer to check whether reconstructed dashboards or alerts yield expected results, and detect broken or missing elements.
• An orchestration engine that manages phased cutover, rollback, staging vs production migration, and incremental deployment of migrated resources.

In short: you move from brittle manual copy/paste to a programmatic, AI-assisted migration pipeline, where incompatibilities are surfaced, mappings are automated, and fallback suggestions are intelligently chosen.

Ultimately, automation turns migration from a one-time pain into a repeatable capability. It’s how observability should evolve — faster, cleaner, and built for change.

Image credit: alphaspirit/depositphotos.com