Serverless auto-scaler for your underwriting models – enterprise volume at startup prices.
Small insurtech teams cannot scale custom underwriting models demanded by enterprise insurance clients without VC funding.
ScaleRiskAPI ingests your existing underwriting model code (Python/JS), auto-optimizes and deploys it on global serverless infra for enterprise traffic without ops team. Pay only for inferences at $0.001 each after $20 base. Handles spikes, monitoring, and SLAs out-of-box for small insurtechs chasing big clients.
Small insurtech teams without VC funding targeting enterprise insurance clients
Plug-and-play scaler for any underwriting code with insurance-optimized optimizations like batching for policy portfolios.
friendly
Upload Python/JS model, auto-containerize and deploy.
Global serverless with 99.9% SLA, scales to millions inferences.
Auto-batch, quantize models for insurance workloads.
Latency, error rates, cost dashboards.
Triggers for model retraining on data drifts.
Predict bills based on usage patterns.
A/B route traffic across versions.
CSV/PDF reports for clients.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| code_blob | text | No |
| status | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| model_id | uuid | No |
| url | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| endpoint_id | uuid | No |
| inferences | int | No |
| latency_avg | numeric | No |
| timestamp | timestamp | No |
Relationships:
/api/modelsUpload/deploy model
/api/endpoints/:id/inferenceProxy inference calls
/api/metrics/:endpointIdFetch usage stats
/api/billing/forecastPredict costs
/api/alertsSet monitoring alerts
No SLA
Basic support
None
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 15 | 13% | $39 | $468 |
| Month 6 | 120 | 18% | $432 | $5,184 |
Upload code, get global API. No ops, low cost, full SLAs.
Target GitHub repos with underwriting code via stars/forks; cold email authors offering free scaling; join insurtech Discords for live demos.
Easy ML deploy
Generic, no insurance opts
Underwriting-specific scaling at fixed low base
Production ML
Expensive for startups
$20 entry, insurance focus
Data from usage metrics trains better optimizers, creating performance moat.
Serverless maturity + rising insurtech deal sizes demand cheap scaling.
Model compatibility issues
Support Python/JS only
Infra costs overrun
Hard usage caps
High variable costs
Pass-through pricing
Success: All deploy <5min
Success: 100k inferences
Success: 20 users
Success: 50% retention
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