Alright, data lovers! 🕵️♀️ I just finished a deep dive into our own internal data from the last 18 months, where we rotated through three different ad ops models: our scrappy in-house team, a specialized freelancer, and a full-service agency. This wasn't planned as an experiment, but the shifts happened naturally, and I've been geeking out on the performance logs and cost breakdowns.
I tracked everything—not just ROAS, but also setup time for new campaigns, creative iteration speed, issue resolution time (like when a deep link broke on a specific Android build 😅), and reporting transparency. The trade-offs were clearer than I expected.
Here's what our numbers showed:
**In-house Team (2 people)**
* **Strengths:** Unbeatable speed for urgent, brand-aligned creative tests. They lived in our crash and UX monitoring tools, so they'd proactively pause ads causing high uninstall rates on certain OS versions.
* **Cost:** Predictable salary + tools, but high overhead for keeping skills sharp on all platforms.
* **Data Point:** Creative iteration loop was 3x faster than the agency. However, scaling during peak seasons led to missed optimizations and a 15% higher CPA on search campaigns.
**Freelancer (Specialized in Push & Video)**
* **Strengths:** Deep platform expertise brought immediate efficiency gains in a specific channel. Our push campaign CVR improved by 22% in month one.
* **Cost:** Hourly/project-based. Clear ROI for the niche, but a bottleneck for integrated strategy.
* **Data Point:** When we needed a last-minute UA campaign across three new social platforms, we hit a hard delay. They were fantastic within their lane, but the "full-funnel" view was missing.
**Full-Service Agency**
* **Strengths:** Scale and breadth. They managed simultaneous global campaigns we couldn't have handled internally. Their reporting suite was comprehensive.
* **Cost:** Highest retainer, with sometimes opaque line items.
* **Data Point:** Campaign setup took longer due to more process, but their bid management tools drove the lowest CPIs at high spend levels. The big con? They were completely siloed from our beta app releases, leading to some unfortunate ad placements on features that were buggy.
For me, the key insight was that no single model was "best." It depended entirely on our product lifecycle stage. Beta phase? In-house was crucial for control. Scaling one channel? Freelancer was perfect. Going broad with a stable app? Agency made sense.
What's your experience been? Does this match your data, or did you find a hybrid model that worked better?
happy testing!
edge cases matter
Lead data engineer at a mobile gaming studio, 25M MAU. We run our own ad analytics stack on ClickHouse for real-time ROAS and cohort tracking.
* **Time to Insight:** In-house can query logs directly. For us, that's a 5-minute SQL query vs. waiting 24h for an agency's aggregated report.
* **Cost Control:** Freelancer ran ~$80-120/hr, agency retainer was $15k/mo minimum. In-house salaries are fixed but you carry full tooling cost (e.g., $3.5k/mo for platform APIs).
* **Platform Depth:** Agency had the edge on new platform betas (like TikTok Shop). Our in-house team took 3-4 weeks to build competency on a new channel.
* **Breakage Response:** In-house resolved tracking pixel breaks in under 2 hours. Agency SLA was 24 hours, freelancer was dependent on their availability.
Pick in-house if you have the data maturity to instrument everything and need sub-day iteration. Pick the agency if you're entering 3+ new ad channels a year and can't staff for it. Tell us your monthly ad spend and how many platforms you're actively buying on.
Numbers don't lie.