After six months of building and evaluating LLM-powered features with a patchwork of Google Sheets, CSV exports, and manual Python scripts, my team mandated a move to a dedicated evaluation platform. We selected Freeplay for a pilot project. Having now completed the transition, I can provide a concrete, throughput-oriented comparison of the two workflows.
**Previous Spreadsheet Workflow:**
* **Input Management:** New prompt versions and test case datasets (often 500-1000 rows) were stored as separate CSV files. Versioning was handled via filenames (`prompt_v2.3.csv`).
* **Execution:** A custom Python script looped through prompts and datasets, calling the LLM provider API. Results were written to a new CSV.
* **Evaluation:** We used a second script to apply evaluation functions (e.g., checking for JSON validity, keyword presence, custom model-graded checks) on the output CSV. Results were aggregated in a summary sheet.
* **Key Bottlenecks:** No parallel execution; a 1000-case run took ~45 minutes. Tracking the lineage between prompt changes, dataset changes, and resulting performance shifts was entirely manual and error-prone.
**Freeplay Workflow:**
* **Input Management:** Prompts are managed as templates with parameters in the UI. Datasets are stored as distinct entities within projects.
* **Execution:** The `freeplay` SDK allows for programmatic runs. Configuration is defined as code, which I prefer.
```python
import freeplay
run = freeplay.execute_prompt(
prompt_version="prod_email_extractor",
dataset="test_cases_v6",
environment="production-api"
)
```
* **Evaluation:** Custom evaluation functions (Python) are attached directly to the project. They run automatically on completion, with results visualized in the UI and accessible via API.
* **Throughput & Observability:** Freeplay handles concurrent execution, reducing our 1000-case run to under 8 minutes. The direct tracing of every output back to its prompt version and test case is the most significant operational improvement.
**Quantifiable Gains:**
* **Experiment Cycle Time:** Reduced from ~2 days (manual coordination) to under 3 hours.
* **Throughput:** API call concurrency increased effective throughput by ~5.6x.
* **Reliability:** Eliminated manual data-stitching errors.
**Architectural Trade-offs:**
The primary trade-off is flexibility versus governance. My ad-hoc scripts could do anything, but that became a liability. Freeplay imposes a structure of Projects, Prompts, and Datasets, which forces organization. The evaluation API, while good for automated checks, is less suited for one-off, exploratory analysis on a local machine. I now occasionally export results to a notebook for deep dive, but the core workflow is centralized.
For teams moving beyond initial prototyping and needing reproducible testing, the shift is justified. The time saved in orchestration and lineage tracking directly translates to more experimentation cycles. My next test will be integrating its annotation features into our CI/CD pipeline for gatekeeping prompt promotions.
1. I'm a product lead at a B2B SaaS company around 200 people, and we build LLM features into our customer support platform. We run our main copilot and email summarization features in production, and I've been through the same journey of evaluating from scripts to hosted platforms.
2.
- **Cost and Scale Reality**: With spreadsheets, our direct cost was just API calls plus engineering time, which was massive. Freeplay's model is per-seat plus usage tiers. For our team of 5 heavy users, it's about $600/month. The hidden cost with spreadsheets wasn't money, it was the 15-20 hours a week of a data engineer's time just to keep the evaluation runs and logs straight.
- **Iteration Speed on Prompts**: In our old flow, comparing a minor prompt tweak across our 700 test cases meant a dedicated script run, taking close to an hour. With Freeplay, I can queue that comparison and get results in under 10 minutes because of built-in parallelization. That's the difference between testing one idea per day versus testing a dozen.
- **Traceability and Debugging**: In Sheets, tracing a bad output back to the exact prompt version and dataset snapshot was a manual grep through folders. With Freeplay, every single output is automatically linked to the prompt snapshot, test case, and model parameters. This cut our root-cause analysis time for production issues from hours to literally minutes.
- **The Collaboration Breakpoint**: The spreadsheet system worked for 1-2 engineers. The moment we added a product manager and a designer who wanted to tweak prompts and see the impact, it collapsed. Freeplay gives them a UI to run scenarios without writing code, but that comes with the need for training and some guardrails to avoid runaway API costs.
3. I'd recommend Freeplay for any team that's moved past the initial prototype phase and has at least two people needing to iterate on prompts regularly. If the OP's team is still in early R&D with a single engineer exploring, the spreadsheet method is cheaper, but once you have a mandate to ship and collaborate, the switch is justified.
customer first
Totally get the time savings on prompt iterations. That parallel execution is a game changer when you're running hundreds of test cases.
I'm curious about the "Traceability and Debugging" part you started to mention. We've found that the real test is when an odd production output comes back and you need to trace it. With a hosted platform, can you easily link a live session ID back to the specific test run and prompt version that generated it? Or is there still a gap between the eval environment and prod logs?
Webhooks or bust.
That bottleneck with manual lineage tracking is exactly where we wasted so much time. "Tracking the lineage between prompt changes, dataset changes, and resulting performance shifts was entirely manual" - we had the same pain point.
Our solution was a janky homegrown versioning system in Airtable before we switched, and it still broke constantly. The second someone forgot to tag a dataset version correctly, the whole comparison was garbage. Freeplay's automatic versioning for prompts, datasets, and test runs was the biggest unlock for us - it cut out those "wait, which CSV was this?" debates.
Have you tried using their compare feature across different model parameters yet? We found swapping from GPT-4 to Claude for certain tasks, and being able to instantly compare scores side-by-side, saved us a ton.
Automate everything.