Why Observability Tools Tend to Fail at Scale

Observability is no longer just about catching errors or checking if a server is up. In modern distributed systems, it’s about understanding behavior across dozens, if not thousands, of services, all running in different environments and generating massive amounts of data.

That level of complexity is exactly why choosing the right observability tool matters so much. The wrong decision doesn’t just slow you down. It can drain your budget, impact your performance at scale, and lock you into a system that no longer fits once your product takes off.

Any good architect will tell you that building great observability into a product requires ease-of-onboarding, high performance (even at scale), and a system that keeps it independent of the application itself. Switching observability tools later is painful and expensive. It’s best to avoid vendor lock-in from the beginning and choose something that can grow with you.

But that’s easier said than done. Most teams don’t think about long-term observability needs until it’s too late. Based on what we’ve heard from our customers here at Akamai, the real problem starts during the early stages of a company’s growth when teams pick tools that feel easy now, but become costly and rigid down the road.

This is where you’re focused on speed and low cost. You need to validate your idea and get something working. Open source tools like the ELK Stack shine here: flexible, cheap (at least up front), and great for hacking together an MVP.

Now that the product is growing, you need to keep your system up and stable. Observability becomes critical, and many teams default to easy-to-manage, blackbox tools like Snowflake that are fast and easy to use. Unfortunately, they’re also very expensive, especially as usage ramps up.

As traffic and data volumes grow, the tooling decisions made in Stage 2 start to backfire. Stage 3 is where observability bills from blackbox solutions become prohibitively expensive. Companies get stuck between two bad options. Continue paying exorbitant costs to stay with the convenient blackbox tool, or replace it with something cheaper, which takes time, introduces risk, and often delays core product work.

We think that this stage 3 problem actually originates in stage 2, when companies make the wrong decision to move to a blackbox solution. Instead, what if there was a solution that companies could transition to from open source that would last them the lifetime of their product?

So the real question here should be which solution is capable of serving a company best in the long run? Here at Akamai, we’ve heard from many customers who have experienced the stage 3 problem, the consequence of transitioning to a blackbox solution in stage 2. In response, we’ve been partnering with Hydrolix to come up with a solution that sits in the middle of these two options: TrafficPeak. TrafficPeak is a cloud-native solution with auto-scaling and integrated traffic observability. While remaining simple-to-use and giving users a significant degree of control, it is designed for high-volume environments like microservices, CDNs, or edge networks. TrafficPeak offers the control of open source with the simplicity of SaaS, but without the cost shocks of blackbox tools.

Let’s dive into how ELK stack (open source), Snowflake (blackbox), and TrafficPeak (scalable) hold up when it comes to setup and infrastructure complexity, performance at scale, cost management, customization, security, and maintenance. 

ELK Stack gives teams a high degree of control, but it comes with significant operational complexity. Building a complete ELK pipeline (Elasticsearch, Logstash, Beats or Agents, and Kibana) requires thoughtful configuration, dependency management, and deep familiarity with how each component fits together. Scaling during stage 3 introduces further challenges, such as managing sharding, indexing, and availability across nodes. For fast-moving organizations, these infrastructure requirements can become a bottleneck.

Snowflake, by contrast, is fully managed and cloud-native. It abstracts away infrastructure, letting teams focus on data rather than servers. However, observability use cases require building ingestion pipelines that feed logs and metrics into Snowflake, typically via Snowpipe, Kafka, or ETL frameworks. While initial setup may seem straightforward, the engineering effort to make observability data queryable and actionable within a data warehouse model introduces latency and complexity. It is powerful, but not built for real-time operations visibility.

TrafficPeak was built with deployment simplicity in mind. As a cloud-native solution, it integrates seamlessly into Kubernetes environments and can be deployed as a SaaS or containerized platform. There is no need for complex queueing systems or custom ingestion layers. Data collection, processing, and visualization are built into the same pipeline. It is designed to be up and running in hours, not weeks, making it accessible to teams without dedicated operations or data engineering resources.

In ELK, high-throughput ingestion at scale requires careful architecture. It is common to introduce Kafka or other queueing systems to handle bursts, and ingestion pipelines must be tuned to avoid dropped logs or failed index updates. Elasticsearch itself can become a bottleneck under heavy load if not sharded and sized correctly. These issues can be addressed, but doing so takes time, skill, and constant attention.

Snowflake excels at scale, which is one of its core strengths. It can ingest and process petabytes of data, and its separation of storage and compute allows flexible scaling. But ingestion is not instant. Observability pipelines often involve buffering, batch loading, or transformations before data is available for query. This makes Snowflake less suitable for real-time alerting or debugging, where sub-minute latency is critical.

TrafficPeak was designed for high-volume, real-time environments. It features autoscaling ingestion pipelines and built-in buffering and load-shedding mechanisms, which allow it to adapt dynamically to changes in traffic. Whether you are running a fleet of microservices, a global CDN, or streaming data from edge devices, TrafficPeak is designed to handle high-throughput workloads and surface insights quickly.

While ELK is cost-effective at first, especially for teams trying to avoid SaaS bills, the total cost of ownership can balloon quickly. Infrastructure costs rise as you scale horizontally, especially when logs, metrics, and traces are all centralized in Elasticsearch. Maintenance, tuning, and incident response can consume valuable engineering time. What starts as a free stack often becomes a hidden cost center.

Snowflake introduces a different kind of cost challenge. While its pay-per-use model allows precise control over compute and storage, observability data is notoriously high-volume and spiky. Query costs can rise quickly, especially when data is retained long-term or queried frequently. Without strict governance and optimization, costs can escalate unexpectedly, particularly when observability data is mixed with analytics workloads.

TrafficPeak was built from the ground up with cost efficiency in mind. Its pricing model is usage-aware and designed to avoid runaway costs. Features like data compression, tiered retention, and smart sampling help control volume and spend, while autoscaling ensures you only pay for the resources you actually use. TrafficPeak gives you visibility into both system health and system costs before either becomes a problem.

One of ELK’s greatest strengths is its flexibility. You can build custom pipelines, apply filters, define schemas, and create highly tailored dashboards for specific use cases. That makes it powerful, but also complex. Customization requires an understanding of Lucene queries, pipeline syntax, and index mapping. For teams that need fine-grained control, it is unmatched. For others, it can become a maintenance burden.

Snowflake is schema-first and built around SQL, which makes it highly extensible for data analysts and teams who want to join observability with business data. However, it is not built with native support for log parsing, trace stitching, or alerting. This limits its use in live observability workflows. You often need to layer additional tools on top to get dashboards or operational views.

TrafficPeak takes a “just enough” approach to customization. It comes with ready-to-use dashboards and workflows, but also provides APIs, labeling, and filtering tools for teams that want to tailor insights to their environment. It is designed to minimize time to value while still offering extensibility where it counts, such as log enrichment, tagging, and data correlation.

ELK Stack offers security, but not turnkey. Role-based access control (RBAC), TLS, and audit logging can be implemented via plugins or configuration, but they require ongoing maintenance. For regulated industries, achieving full compliance with an ELK deployment demands diligence and discipline.

Snowflake offers enterprise-grade security out of the box, including RBAC, row-level security, encryption at rest and in transit, and support for various compliance standards. It is well-suited to teams that must meet stringent requirements and want those features managed by a vendor.

TrafficPeak has security built in from the ground up. Features like RBAC, auditing, and data residency controls are native to the platform rather than add-ons. Whether you are in finance, healthcare, or government, TrafficPeak makes it easy to meet modern compliance requirements without cobbling together disparate tools.

ELK is fully self-managed unless you pay for Elastic Cloud or a third-party provider. That means your team is on the hook for scaling, patching, performance tuning, and troubleshooting. For many teams, this burden becomes unsustainable for teams without deep infrastructure expertise, especially as the environment grows.

Snowflake, being fully managed, removes the maintenance burden entirely. It handles upgrades, patching, and scaling behind the scenes. But because observability support is not its primary use case, support tickets can be routed through workflows that are not optimized for debugging live systems.

TrafficPeak offers vendor-managed observability with real-time support and optional SLAs. It is designed to minimize operational lift while providing access to engineers who understand observability-specific issues. The result is a platform that helps you ship and scale without constantly worrying about your telemetry stack.