Let’s be honest, if you’ve worked with Datadog, you’ve probably had that “wait, why is our monitoring bill higher than our cloud bill?” moment.
You’re not alone.
Datadog has been the gold standard for observability for years. It’s packed with features, supports almost every integration imaginable, and lets you see just about everything that’s happening inside your systems. But lately, more and more teams are asking, “Is this still worth it?” The answer, for a growing number of them, is no.
That’s where Middleware comes in.
It’s newer. It’s built for today’s cloud-native world. And most importantly, it promises to deliver the same (or better) visibility, without the budget panic.
In this blog, we’re going to break down exactly why businesses are migrating from Datadog. And we’ll do it section by section, no fluff, no buzzwords, just honest insights from devs, ops, and engineering leads who’ve been there.
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1. Cost Savings & Predictability
If you’ve ever opened a Datadog invoice and just sat there blinking at the number, you’re not alone. One of the biggest and loudest reasons companies are moving away from Datadog is the unpredictability of costs.
What’s the deal?
Datadog offers a wide range of features, but here’s the catch: almost everything is metered separately. Metrics, logs, traces, custom dashboards, and even OpenTelemetry metrics are billed like a luxury add-on. This means your bill doesn’t just depend on how much you monitor, but also on how you monitor, when you monitor, and whether you accidentally left a high-volume log stream turned on over the weekend.
In contrast, Middleware comes in with a much simpler, flat model.
There are no surprise multipliers. No “custom metric tax.” You only pay for what you actually use – not your peak usage, not by label count, and definitely not by mistake.
Why It Matters
When your monitoring costs are unpredictable, it’s almost impossible to budget properly, especially if you’re scaling fast. Engineering leaders have to either:
- Restrict who can add metrics/logs (bad idea)
- Or accept the budget bloat and hope no one notices (worse idea)
That’s why predictable pricing isn’t just a “nice to have.” It’s a business need. Let’s look at some actual experiences and comparisons:
- MindOrigin, a Middleware customer, reported a 75% cost reduction after switching from Datadog. They found that Middleware “offered similar or even superior capabilities at a much lower cost.” Source
- Middleware’s pay-as-you-go model means no traditional tiers, no feature restrictions, and no pricing surprises. Source
- One team even wrote that Middleware was “3x to 5x more cost-effective than Datadog” – and that includes full access to advanced features like OpenTelemetry metrics and custom alerts. Source
Meanwhile, Datadog’s pricing is… well, infamous:
- “Ironically, Datadog lacks observability into its own costs…”Source
- “How on earth do people deal with Datadog’s billing practices?”
Source
How on earth do people deal with Datadog’s billing practices?
byu/ycnz insysadmin
- Even companies like Coinbase reportedly spent $65 million on Datadog in just one quarter. Source
Coinbase (?) had a $65 million Datadog bill per its Q1 earnings call. Wild.
— Turner Novak 🍌🧢 (@TurnerNovak) May 5, 2023
h/t @ChairliftCap pic.twitter.com/E36xfgMrTh
Pricing Overview: Datadog vs Middleware
| Feature / Metric | Datadog | Middleware |
| Custom Metrics | ~$0.05 per custom metric per hour | Included (no extra charge for OpenTelemetry or custom metrics) |
| Log Ingestion | ~$0.10 per ingested GB + storage costs | Pay-as-you-go; filter at source to reduce cost |
| APM Traces | Billed per host + trace volume | Included with no per-trace surcharge |
| RUM (Real User Monitoring) | ~$1.50 per 10K sessions | Included with session replay |
| Dashboards & Alerts | Limited in lower tiers; full access only in higher plans | All features included, no locked features |
Tired of decoding monitoring bills?
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2. Transparent & Flexible Ingestion Control
One of the biggest frustrations teams have with observability tools is that they’re often forced to collect and store everything, including critical metrics, background noise, and random logs, whether they are needed or not. And you end up paying for all of it.
Why This Matters
Monitoring isn’t just about capturing more data. It’s about capturing the correct data.
In a typical Datadog setup, you’ll start ingesting logs, traces, and metrics. But pretty quickly, you’ll notice that Datadog costs are rising, sometimes exponentially. Why? Because there’s little control over what gets ingested upfront. You store first, then figure out later what to keep. By then, you’ve already paid.
Now multiply that by hundreds of services and microservices, and suddenly you’re drowning in irrelevant data and bills.
“Datadog becomes a budgeting game. What can we afford to see?”
– Reddit user on r/devops
What Middleware Does Differently
Middleware flips this model. It provides fine-grained control over what gets ingested before it reaches your dashboards or bills.
You can build custom ingestion pipelines to:
- Drop metrics or logs that aren’t mission-critical
- Filter based on rules (e.g., only keep errors or performance metrics from specific services)
- Route or discard noisy data at the source
- Avoid collecting system-level noise from development or staging environments.
This means you’re not just storing less, you’re also storing smarter. And when troubleshooting or building reports, you’re dealing with cleaner, more relevant data. It’s faster, leaner, and significantly more cost-effective.
Teams using Middleware have reported up to 60-70% reduction in storage and ingestion costs simply by filtering unnecessary metrics early.
👉 Check Ingestion Controls → and start filtering smarter, not harder.
Datadog: Filter Later, Pay First
Datadog offers log exclusion filters and retention settings, but they mostly take effect after ingestion. That means you’re still paying to ingest logs, even if you discard them right after. There’s no native, built-in feature to apply complex, rule-based ingestion filtering upfront.
Configuring ingestion controls on Datadog to reduce volume isn’t straightforward. You often need to maintain separate agent-level configurations, custom tags, or pay for more granular controls through additional pricing tiers.
It’s not just us saying this. Users across forums echo the same:
- “Datadog makes it really hard to figure out where your log money is going.”
Reddit thread - “We basically had to build a sidecar just to trim our Datadog logs before they hit the agent.”
Hacker News discussion
Feature Comparison
| Feature | Middleware | Datadog |
| Custom Ingestion Pipelines | Yes, built-in | No, limited post-ingestion filtering |
| Filter Before Storage | Fully supported | Not natively supported |
| Drop/Exclude Noisy Metrics | Easily configurable | Requires manual agent configuration |
| Ingestion Cost Control | Designed to reduce cost at source | Reactive and indirect |
| Real-Time Filtering | Yes, with transparent rules | Mostly via retention settings after ingesting |
In our internal case studies, teams switching to Middleware often start by reviewing the top 20% of their data sources and simply dropping logs or metrics from lower-impact services.
This simple exercise alone has resulted in a 30–60% cost reduction, without compromising visibility into performance or incidents.
And perhaps more importantly, engineers report that it’s easier to work with filtered, focused data. Troubleshooting becomes faster when you aren’t sifting through thousands of redundant metrics from containers that were never part of the problem.
3. OpenTelemetry & Open Foundation Support
In today’s cloud-native stack, vendor lock-in isn’t just a pricing issue; it’s a control issue. That’s why support for open standards, such as OpenTelemetry (OTel), is becoming increasingly critical.
Middleware is built around first-class support for OpenTelemetry, without extra fees or artificial limitations. All OTel metrics are treated the same as native ones, with no complex pricing models or hidden caps.
Why This Matters
Datadog, by contrast, often categorizes OTel or custom-defined metrics as “custom metrics”, which are subject to stricter limits and expensive billing tiers.
“OTel metrics are custom metrics in Datadog = you will pay A LOT to use an open standard.”
– Reddit user on r/sysadmin
This creates a perverse incentive: teams are discouraged from adopting open tooling because the cost is unpredictable.
Middleware’s Approach
- Native support for OpenTelemetry across metrics, traces, and logs
- No artificial pricing walls between standard and custom metrics
- Seamless interoperability with Prometheus, Grafana, and other OSS tools
- Designed to keep teams in control, not locked into one agent or dashboard
Whether you’re using OTel to standardize observability across teams or planning a migration away from proprietary agents, Middleware won’t penalize you for it.
4. Feature-Equivalence & Technical Parity
One of the biggest concerns when switching observability platforms is losing access to core features. Middleware has addressed this head-on by building a product that delivers parity across the most important pillars, without locking essential tools behind pricing walls.
| Feature | Middleware | Datadog |
| APM (Application Monitoring) | Full-featured, no extra cost for custom metrics | Advanced APM, but custom metrics increase cost |
| Logs | Unified with traces, flexible ingestion & retention | Powerful logging, but expensive at scale |
| Infrastructure Monitoring | Covers servers, containers, cloud, and hybrid setups | Deep infrastructure insights with detailed visualizations |
| RUM (Real User Monitoring) | Included with session replay and UX insights | Included, but session replay adds significant cost |
| Trace-Log Correlation | Native and automatic trace-to-log linking | Requires manual setup and tagging |
| Alerts (Service + Endpoint) | Native service-level and endpoint-level alerting | Robust alerting engine |
Middleware doesn’t cut corners; it delivers the same technical breadth and depth, while also fixing some of the usability and cost friction seen with Datadog.
5. Deployment Flexibility & Data Residency
While Datadog is built for cloud-only environments, Middleware offers deployment flexibility, supporting on-premises, hybrid, and cloud-native setups. This makes Middleware a better fit for organizations with strict data residency requirements, compliance needs, or who want to avoid high egress costs.
Self-hosting options also give teams more control over their infrastructure, something that’s not possible with Datadog’s SaaS-only model.
6. Business Impact: ROI, Reliability & Avoiding Lock-In
Middleware’s value extends beyond pricing; it impacts how quickly teams resolve issues, how much time developers get back, and whether you maintain control over your stack.
- A user on r/sysadmin noted they were “drowning in Datadog costs” and cut spend significantly after switching, without losing visibility.
- Another comment highlighted Datadog’s rising cost over time and the difficulty of switching due to deep lock-in.
- Middleware supports open standards, such as OpenTelemetry, so teams aren’t forced to adopt proprietary agents or formats.
A significant benefit: noise reduction. One user said filtering noisy metrics before ingestion helped their team focus and made alerting actually useful (source).
Ultimately, Middleware helps teams:
- Cut observability costs by up to 75%
- Reduce alert fatigue and speed up MTTR
- Avoid vendor lock-in and maintain control
- Scale with predictable usage-based pricing
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If you’re scaling fast or tired of fighting your monitoring tool, this shift makes a noticeable difference.
7. Unified Observability with Better Developer Experience
A major frustration with Datadog, as highlighted by users on r/devops, is its complex UI and fragmented tools, which require teams to jump between different modules for metrics, logs, traces, and user journeys. Even setting up effective dashboards can feel like a full-time job, as noted in this teardown.
Middleware takes a different approach: it offers a fully unified platform where metrics, logs, traces, RUM, and even session replays are natively integrated. Instead of stitching together different views, teams get a single-pane experience, with seamless context switching across services and users.
The result?
- Faster detection and debugging with AI-driven analytics
- Clear trace-log correlations with actionable insights
- Developer-first workflows that require less onboarding and less cognitive overhead
By simplifying everything from root cause analysis to dashboard creation, Middleware not only reduces noise but also improves team collaboration, especially among developers, SREs, and product owners.
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This clarity in both UX and data paths can be a game-changer for teams that don’t want to waste cycles just figuring out their observability stack.
8. Improved Developer Experience and Collaboration
Many teams report that Datadog’s UI can be overwhelming, especially for new users. A post on r/devops describes the tool as “confusing even for experienced engineers,” with alerts, dashboards, and configurations spread across a cluttered interface. Another breakdown on Moldstud points out how common dashboard design mistakes can limit both performance visibility and collaboration.
Middleware addresses these issues head-on by focusing on intuitive UX, reducing setup fatigue, and giving developers a more straightforward way to engage with performance data. Its built-in session replay, service-level views, and simplified alert configuration make it easier to onboard team members, debug issues, and collaborate across roles.
The result is faster workflows, less back-and-forth between developers and operations, and better visibility for everyone, from developers to product managers.
Conclusion
After articles like the one on Analytics India Magazine highlighted Middleware’s rapid growth, fueled by cost savings of up to two-thirds compared to Datadog, and detailed feature comparisons showing parity across APM, logs, infrastructure, RUM, and trace-log correlation, one thing is clear:
Middleware isn’t just a lower-cost option, it delivers enterprise-grade observability without the usual overhead.
With transparent pricing, support for OpenTelemetry out of the box, flexible ingestion controls, hybrid deployment models, and a unified telemetry experience, Middleware directly addresses the pain points developers and DevOps teams repeatedly cite with Datadog, from surprise billing to tooling fragmentation to steep learning curves (this user called out Datadog’s overwhelming dashboard design).
If you’re facing rising observability costs or struggling to make sense of scattered monitoring data, Middleware offers a practical and scalable alternative. Whether you start small, mirroring a subset of metrics, or go all-in, the outcome tends to be the same: better control, faster insights, and happier teams.
