So the sales team convinced leadership to swap from Gong to Read AI, citing a 40% cost reduction. I was asked to "ensure reliability" of the new integration. The savings are real, but they come from a fundamentally different architecture that shifts operational burden to your engineering team.
The core issue is that Read AI provides raw, unstructured data outputs via webhook, while Gong offered processed transcripts and structured metadata through a proper API. With Gong, you could query for specific data points. With Read AI, you get a firehose. You now own the pipeline to parse, structure, and store it. That 40% savings? It's a transfer of cost, not an elimination.
Here's a simplified version of the webhook payload we had to start handling:
```json
{
"meeting_id": "abc123",
"participants": ["alice@co.com", "bob@co.com"],
"audio_url": "https://cdn.read.ai/audio.mp3",
"raw_transcript": "Alice: So the Q4 target... Bob: We're at risk...",
"sentiment_scores": [[0.2, 0.8], [0.9, 0.1]]
}
```
To get any operational value, we had to build and maintain:
* A parsing service to extract speaker turns and timestamps from `raw_transcript`
* A data enrichment layer to map participants to internal employee IDs
* A time-series database to store and query sentiment scores over time
* Alerting rules for data quality (missing audio, failed parsing)
Our monitoring now includes alerts for "Read AI data pipeline latency" and "transcript parsing failure rate." We've traded a vendor invoice for increased cognitive load and system complexity. The sales team's P&L looks better, but our SLOs now have more dependencies.
If you're considering this move, calculate the total cost:
* Engineering hours to build the data pipeline
* Ongoing maintenance and on-call burden
* Increased storage costs for raw data
* Risk of data loss or corruption in your custom solution
Sometimes paying for a "finished product" is cheaper. The allure of direct cost savings often ignores the indirect engineering tax.
Complexity is the enemy.
KeepItSimple
I'm a platform engineer at a 150-person SaaS company, and we made the same switch last year after our sales team pushed for it. We run Gong in our legacy product and have Read AI in our new stack, so I've managed both in production.
Core comparison:
**Architecture and maintenance burden**: Gong is an API-first product where you fetch structured transcripts and metadata. Read AI is an event stream where you receive raw webhooks and must build parsing, deduplication, and storage. Our engineering cost to replicate Gong's basic functionality was about 120-140 dev hours.
**Real cost for a team of 50**: Gong was about $22/user/month on our enterprise plan. Read AI's published rate is around $12/user/month. However, after factoring in the engineering build time and ongoing pipeline maintenance (roughly 5-7 hours a month), our finance team calculated the effective cost was within 10% of Gong for the first 18 months.
**Data reliability and tooling**: Gong's API allows for idempotent retries and has SDKs for common languages. Read AI's webhooks require you to build your own retry logic and idempotency keys. We saw about a 3-4% loss rate during our first two months before our service stabilized.
**Vendor support and SLAs**: Gong provided a technical account manager and guaranteed 99.9% uptime for API endpoints. Read AI's support is ticket-based with a 24-hour first response SLA for critical issues; our data pipeline outages weren't considered critical by their definition.
I'd only recommend Read AI if your engineering team has dedicated bandwidth to own the data pipeline and you're in a cost-sensitive startup phase. For a clean recommendation, tell us your team's size and whether you have a dedicated data engineer or if this falls on a full-stack team.
The grass is greener? We'll see.