Baseten vs Qdrant Pricing (2026)
How do these two stack up on price? Here's what each one costs, what you get, and where the value sits.
Basic
- Dedicated deployments
- Model APIs
- Training
- Fast cold starts
- SOC 2 Type II and HIPAA compliant
- Email and in-app chat support
Pro
- Priority access to high-demand GPUs
- Dedicated compute
- Higher Model API rate limits
- Hands-on engineering expertise
- Dedicated support on Slack and Zoom
Enterprise
- Custom SLAs
- Self-host deployments
- On-demand flex compute
- Use existing cloud commitments
- Full control over data residency
- Advanced security and compliance
- Custom global regions
- Advanced RBAC with Teams
Free Tier
- Single Node Cluster
- 0.5 vCPU / 1GB RAM/ 4 GB Disk.
- Free Cloud Inference With Selected Models
Standard Tier
- Dedicated Resources
- Flexible Vertical and Horizontal Scaling
- Highly Available Setups
- Backup & Disaster Recovery
- Free Tokens for Paid Inference Models
- 99.5% Uptime SLA
Premium Tier
- SSO
- Private VPC Links
- 99.9% Uptime SLA
- Extra Support
Hybrid Cloud
- Local Data Residency
- Regulated Workloads
- Easy Operations in Your Own Cloud
- Data Stays in Your Network
- Fully Managed Through Qdrant Cloud
- Production-Grade Uptime
Private Cloud
- Large Enterprises
- Sensitive Workloads
- Air-Gapped Setups
- Custom SLAs
- Full Isolation
Baseten vs Qdrant FAQ
- Which one is cheaper?
- One or both use custom pricing, so it depends on your specific needs.
- Can I use either one for free?
- Both offer free plans, so you can try each without paying. Start with whichever fits your workflow better and upgrade when you hit the limits.
- How do they charge?
- Both use a usage-based model, so the comparison is straightforward — it comes down to features and limits at each price point.
- Which one is a better deal?
- Depends on what you need. Baseten: They're competing in the ML inference infrastructure space against Replicate, Modal, and cloud-native options like AWS SageMaker — positioned as a developer-friendly middle ground that's more flexible than managed APIs but less DIY than raw cloud. The GPU pricing is granular enough to appeal to cost-conscious ML teams who want to optimize spend. Qdrant: Qdrant is positioning as a serious infrastructure-grade alternative to Pinecone, sitting in the mid-to-premium range for managed vector DBs but with the added credibility of Hybrid and Private Cloud options that pure SaaS competitors can't match. They're clearly going after enterprise teams that want control over where their data lives, not just the fastest path to an API key.
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