PostHog
Weights & Biases
| Feature | PostHog | Weights & Biases |
|---|---|---|
| Pricing | Free / from $0/mo | Free / from $50/mo |
| Free Plan | ✓ Yes | ✓ Yes |
| Rating | 4.5 / 5 | 4.7 / 5 |
| Best For | developers, startups, product-teams, privacy-conscious-companies | ml-engineers, research-teams, ai-companies, data-scientists |
| Founded | 2020 | 2017 |
| Product Analytics | ✓ | ✗ |
| Session Replay | ✓ | ✗ |
| Feature Flags | ✓ | ✗ |
| Experiments | ✓ | ✗ |
| Surveys | ✓ | ✗ |
| Data Warehouse | ✓ | ✗ |
| Self Hosting | ✓ | ✗ |
| Experiment Tracking | ✗ | ✓ |
| Model Registry | ✗ | ✓ |
| Sweeps | ✗ | ✓ |
| Artifacts | ✗ | ✓ |
| Reports | ✗ | ✓ |
| Launch | ✗ | ✓ |
✓ PostHog Pros
- All-in-one analytics replacing multiple tools
- Generous free tier (1M events/month)
- Self-hostable for full data control
- Feature flags and experiments built-in
✗ PostHog Cons
- Can be complex to set up properly
- Self-hosting requires infrastructure maintenance
- Less polished UI than Amplitude
✓ Weights & Biases Pros
- Best-in-class experiment tracking
- Beautiful visualizations
- Great collaboration features
- Generous free tier
✗ Weights & Biases Cons
- Learning curve for full platform
- Can be expensive for large teams
- Requires integration work
The Verdict
PostHog is built for developers and startups, with a focus on product-analytics and session-replay. Weights & Biases targets ml engineers and research teams and leads with experiment-tracking and model-registry.
On pricing, PostHog is the clear winner for budget-conscious users — starting at $0/mo compared to $50/mo for Weights & Biases. That $50/mo difference adds up quickly for growing teams.
Both offer free plans, so you can test each with your real workflow before committing to a subscription.
Feature-wise, PostHog offers broader built-in capabilities (7 features vs 6), while Weights & Biases takes a more focused approach — which can mean a simpler, faster onboarding experience.
This is a genuinely close comparison. If you can, sign up for both free trials (where available) and run a one-week test with your actual team tasks before deciding.