When small teams of under 10 developers are choosing an APM tool, TraceKit and Datadog are two popular options. Here’s the bottom line: TraceKit is ideal for small teams focused on cost and simplicity, while Datadog suits larger organizations with complex infrastructure needs.
Key Takeaways:
- TraceKit starts at $29/month with request-based pricing, unlimited team members, and a setup time of under 5 minutes.
- Datadog starts at $31/host/month for APM, with additional costs for logs, metrics, and other features. Small teams often spend $500+ monthly.
- A 5-person startup saved $46,200 annually by switching from Datadog ($4,800/month) to TraceKit ($950/month).
- TraceKit offers unique tools like live production breakpoints and AI-driven debugging.
- Datadog excels in enterprise-level monitoring but has a higher cost and steeper learning curve.
Quick Comparison:
| Feature | TraceKit (Starter) | Datadog (Entry) |
|---|---|---|
| Pricing | $29/month | $31/host/month |
| Setup Time | Under 5 minutes | 2–4 weeks |
| Team Members | Unlimited | Unlimited |
| Retention | 30 days | 7–15 days (standard) |
| Key Features | Live Breakpoints, AI Debugging | Distributed Tracing, Service Maps |
| Cost for Small Teams | Predictable | Escalates with scaling |
For small teams, TraceKit offers a simpler, more affordable solution without sacrificing key debugging features.
TraceKit vs Datadog APM Comparison for Small Teams
Application Performance Monitoring (APM) Tools: Visibility, Performance & Reliability | Uplatz
Pricing Comparison
For smaller teams, pricing differences between TraceKit and Datadog can feel like the difference between hiring another developer or not. TraceKit uses a request-based pricing model, charging based on the number of traces logged each month. In contrast, Datadog follows a host-based pricing structure, charging $31 per host per month for APM alone. Additional costs apply for log management, custom metrics, and other features. Here’s a quick side-by-side comparison:
| Feature | TraceKit Starter ($29/mo) | Datadog APM (Entry) |
|---|---|---|
| Pricing Basis | Request-based (1M traces/mo) | Host-based ($31/host/mo) |
| Data Retention | 30 days | 7–15 days (standard) |
| Team Members | Unlimited | Unlimited |
| Custom Metrics | Included (Unlimited) | $5 per 100 metrics |
| Setup | Under 4 minutes (one command) | Agent-based approach |
| Key Features | Live breakpoints, anomaly detection, webhooks | Distributed tracing, service maps, Watchdog AI |
TraceKit’s pricing remains predictable as traffic grows, scaling with actual usage rather than infrastructure. For teams operating microservices across multiple containers, this can be a huge advantage. Datadog’s host-based model, on the other hand, can lead to rapidly increasing costs as containerized environments expand.
TraceKit Pricing
TraceKit offers affordable plans designed to meet the needs of bootstrapped teams and growing organizations. The Starter plan costs $29 per month and includes 1 million traces with 30-day retention – perfect for teams processing about 100,000 requests daily. The Growth plan, at $99 per month, offers 10 million traces and 45-day retention, ideal for teams handling close to 1 million requests per day. For high-traffic applications, the Pro plan provides 50 million traces and 180-day retention for $299 per month.
To give you an idea of scalability, a 5-person startup on the Growth plan reported spending $950 per month after scaling their trace volume. Even so, they saved significantly compared to their previous Datadog costs.
All TraceKit plans come packed with features like live breakpoints, distributed tracing, AI-powered anomaly detection, flame graphs, and customizable dashboards. Plus, there’s a free tier for students and indie developers with zero revenue. This tier includes 200,000 traces per month with 7-day retention, making it an excellent option for those just starting out. And with no hidden fees or surprise overages, TraceKit keeps things simple.
Datadog Pricing
Datadog’s pricing structure can be a challenge for smaller teams, especially as usage scales. The base APM cost is $31 per host per month, but that’s just the beginning. Log management costs $0.10 per GB ingested, plus indexing fees of $1.06 per million events for 3-day retention. Custom metrics add another $5 per 100 metrics per month. For example, managing 100 custom metrics could cost around $3,600 annually.
A 2025 case study highlighted a 5-person startup’s monthly expenses with Datadog: $2,400 for 15 APM hosts, $1,200 for 200 GB of logs, and $750 for 500 custom metrics. Their total: roughly $4,800 per month for just six services. After switching to TraceKit’s Growth plan, their effective monthly cost dropped to $950, saving them approximately $46,200 annually.
Datadog’s additional feature fees and potential overage charges make budgeting tricky for small teams. For startups and organizations keeping a close eye on expenses, these extra costs can quickly add up, especially during periods of traffic spikes.
Setup and Ease of Use
Small teams often need tools that can be implemented quickly and efficiently. When it comes to onboarding, TraceKit and Datadog take vastly different paths – one focuses on speed and simplicity, while the other requires more extensive planning and setup.
TraceKit Setup
TraceKit is built for developers who need to start debugging without delay. From signing up to viewing live traces in the dashboard, the entire process takes less than 4 minutes. Its CLI installation streamlines the process by automatically detecting your framework – whether you’re using Express, Django, Laravel, or Flask – and initializing everything with a single command.
Developers frequently report that they can complete the setup in under 15 minutes with minimal effort, which makes it a go-to choice for teams looking for a quick start[3].
One feature that stands out is TraceKit’s Live Production Breakpoints. This allows developers to set capture points directly from the dashboard to inspect variable states in live code. No need to add log statements or redeploy code – saving time and simplifying debugging workflows[3].
On the other hand, Datadog requires a more involved setup process.
Datadog Setup
Datadog’s configuration involves multiple steps and a more complex infrastructure setup. To get started, you need to:
- Install the Datadog Agent on each host.
- Configure the
datadog.yamlfile with API keys and site-specific settings. - Set up sidecar containers if you’re using Docker or Kubernetes.
- Enable integrations for services like AWS or Postgres.
- Instrument your application code with language-specific libraries.
For context, a 5-person startup made the switch from Datadog to TraceKit in early 2026, largely to cut down on costs – Datadog was costing them $4,800 per month. The migration itself was straightforward, taking just 3 hours in total: 30 minutes to replace Datadog’s agents with TraceKit tools, 15 minutes to update Docker Compose by removing sidecar containers, and 2 hours to verify traces in staging[1]. This is a stark contrast to the 2–4 weeks typically required for a full Datadog enterprise implementation[2].
For teams working with microservices, Datadog’s host-based setup can be a challenge. Every new container or service requires configuring a new agent or sidecar, which can quickly increase operational complexity and overhead.
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Features for Small Teams
For small teams, tools that are easy to use and deliver results quickly are essential. Both TraceKit and Datadog provide application performance monitoring, but their features cater to different needs and priorities.
TraceKit Features
TraceKit is designed to help teams debug production issues quickly and efficiently. One of its standout features is Live Production Breakpoints, also known as "Capture Points." These allow developers to inspect live variable states and execution paths in running code – without the need for redeployment[3].
The platform also includes AI-driven trace analysis, which automatically flags issues like N+1 database queries, slow API calls, and recurring error patterns[2]. Developers can visualize request flows using sequence diagrams or flame graphs while recording distributed traces for deeper insights[3]. Additionally, service dependency maps provide a real-time view of microservice interactions, and smart alerts notify teams of health check failures directly via Slack or Telegram[3].
In January 2026, a fullstack developer successfully implemented TraceKit into a Flask app in under 15 minutes, gaining access to real-time traces for just $29/month[3]. The platform’s performance impact is minimal, with an overhead of less than 5% in production[3]. This makes it ideal for small teams that need effective debugging tools without the burden of high costs or complex setups.
"TraceKit helped me find performance issues before I released the new version of my framework. I was able to fine-tune everything and fix problems before they hit users." – Ali Khorsandfard, Creator of Gemvc PHP Framework[3]
On the other hand, Datadog takes a broader approach, focusing on enterprise-level monitoring and observability.
Datadog Features
Datadog is built for large-scale monitoring and offers over 500 technology integrations[5]. Its Davis AI engine stands out for its ability to correlate telemetry data – logs, metrics, and traces – to automatically identify root causes of issues. Datadog excels in deep infrastructure monitoring, providing detailed metrics for CPU, memory, and disk usage on a per-host basis. This makes it particularly useful for complex, multi-service environments.
The platform also features highly customizable dashboards, which can even generate Terraform code for replication across teams[4]. However, its "single pane of glass" design, while comprehensive, can feel overwhelming for smaller teams. As a Gartner reviewer noted in September 2025:
"For engineers they find there are too many features, so they dont know where to start to find the data they need."[4]
Datadog’s performance overhead can range from 5–10% if not optimized[5]. Additionally, its modular pricing – where features like APM, logs, and custom metrics are billed separately – can result in unexpected costs, making it less predictable for smaller teams.
| Feature | TraceKit | Datadog |
|---|---|---|
| Primary Debugging Tool | Live Breakpoints (No redeploy) | Davis AI & PurePath Tracing |
| Instrumentation | Automatic via SDK | Often manual or complex agent config |
| Infrastructure Depth | Basic health checks | Deep per-host metrics (CPU/RAM/Disk) |
| Integrations | Major frameworks & languages | 500+ technology integrations |
| Performance Overhead | < 5% | 5–10% (if not optimized) |
| Learning Curve | Low; focused on debugging | High; complex interface |
Scalability and Support
Scalability for Small Teams
TraceKit’s pricing model is built with flexibility in mind, making it a great fit for small teams. Instead of charging per host, TraceKit uses a request-based pricing structure, billing based on the number of traces per month. This approach is particularly useful for teams running multiple containers, as it avoids the need to upgrade to larger servers, keeping costs predictable.
All paid plans include unlimited team members and services, so you won’t face extra charges as your team or application grows. For example, in November 2025, a 5-person startup switched from Datadog to TraceKit. This move slashed their monthly observability expenses from $4,800 to just $950, saving them $46,200 annually – all while maintaining visibility across six services[1].
Support also scales with your chosen plan. The Starter and Growth tiers provide priority support, while the Pro plan offers dedicated support with a service-level agreement (SLA). TraceKit takes pride in its developer-focused approach, ensuring you’ll get responses from engineers who understand the challenges of debugging in production[3]. This makes TraceKit a strong ally for small, agile teams, especially bootstrapped startups looking for cost-effective solutions.
While TraceKit works seamlessly for smaller teams, scaling for larger enterprises presents a different set of challenges.
Enterprise-Level Scalability
For large-scale deployments, Datadog’s host-based pricing model is more aligned with enterprise needs. The platform is designed for organizations managing thousands of services across complex infrastructures. Datadog also offers over 500 technology integrations and includes the Davis AI engine, which automatically correlates telemetry data to pinpoint root causes in massive distributed systems[5].
However, this host-based pricing can quickly become a financial burden for smaller teams. Datadog charges approximately $31 per host per month for APM, and this structure can lead to escalating costs for teams utilizing dynamic container scaling – even if their overall request volume remains constant.
"Datadog is notoriously expensive, with a complex and opaque pricing model… making cost forecasting nearly impossible and often leads to vendor lock-in."
- Aditya Somani, Engineering Lead[6]
Datadog’s support model also reflects its enterprise focus. The fastest response times are available through its Premier support plan, which costs 8% of your monthly spend, with a $2,000 minimum monthly charge and a one-year commitment. For a small team spending $500 per month, this would mean an additional $2,000 per month for support. While Premier support guarantees responses to critical issues within 30 minutes, the standard plan offers a slower 2-hour response time, which may not suit teams with urgent needs.
Which APM is Best for Small Teams?
When it comes to application performance monitoring (APM) tools for small teams, TraceKit stands out as the top choice for teams of fewer than 10 developers. Here’s why:
Cost-effectiveness is a major factor. TraceKit’s request-based pricing model prevents the unpredictable bills that can arise with Datadog’s more complex structure. Plus, TraceKit offers unlimited team members and services, ensuring costs remain stable as your team grows.
Ease of setup is another area where TraceKit shines. It can be up and running in under 5 minutes thanks to its automatic instrumentation. In contrast, Datadog typically requires 2–4 weeks of configuration work to get fully operational [2]. As Waseem Senjer, a startup founder, shared:
"TraceKit was very easy to set up and started showing useful data almost immediately. I like that it stays simple, the interface is clear, and the traces are easy to understand." [3]
This simplicity doesn’t come at the expense of functionality. Features like live production breakpoints and AI-driven debugging allow teams to identify and fix issues without needing to redeploy. For small teams, these tools can make a big difference in saving time and reducing complexity.
FAQs
What makes TraceKit a better choice than Datadog for small teams?
When it comes to debugging and performance monitoring tools, TraceKit and Datadog cater to different needs, especially in terms of cost, ease of setup, and target audience.
For smaller teams, TraceKit is an appealing choice, starting at just $29/month. Its straightforward setup process takes only a few minutes, making it an accessible option for teams that need to get up and running quickly. Despite its simplicity, TraceKit delivers powerful features like live breakpoints, AI-powered observability, and performance analysis across a range of programming languages, including PHP, Python, and Node.js.
In contrast, Datadog is built with large organizations in mind. It’s a comprehensive, enterprise-level platform offering advanced capabilities like infrastructure monitoring and synthetic testing. However, this level of functionality comes at a price – starting at $500+ per month – and with a level of complexity that may be unnecessary for smaller, budget-conscious teams.
For startups or teams working with limited resources, TraceKit offers an affordable and efficient way to access essential debugging and performance monitoring tools without the added cost or complexity of a larger platform like Datadog.
Why is TraceKit’s pricing a better fit for small teams compared to Datadog?
TraceKit offers pricing that fits snugly into the budgets of small teams, especially those operating with tight resources. With plans starting at just $49 per month, it’s a wallet-friendly alternative to Datadog, whose costs can soar to $500 or more per month for comparable use cases. This makes TraceKit a smart option for startups or teams with fewer than 10 developers.
What’s more, TraceKit is incredibly easy to set up. You can have it running in just minutes, unlike Datadog, which can take days to configure properly. This mix of affordability and simplicity means small teams can access powerful observability tools without breaking the bank or dealing with the hassle of complex setups.
Why is TraceKit faster and easier to set up than Datadog for small teams?
TraceKit is all about keeping things simple. With just a single command, you can have it installed and running in under four minutes. There’s no need for complicated setup or lengthy configurations, which makes it perfect for smaller teams looking to hit the ground running.
On the other hand, setting up Datadog can be a more involved process. It often takes days to integrate all the necessary components across distributed systems. TraceKit also comes with zero-configuration instrumentation for widely used programming languages like PHP, Node.js, Go, Python, Java, and Ruby. This means developers can start tracing and debugging almost immediately, saving a ton of time and effort. For teams operating on tight budgets and schedules, this kind of efficiency can make all the difference.
