The team at Daphnis Labs redefined what’s possible for Urbanface. They delivered a bespoke, animation-heavy website that remains incredibly quick and functional. The…
AI inferenceengineered for cost,latency and reliability.
Improve the serving layer of an existing AI product as usage grows.
- Product Request
- User Context
- Workload
Gateway
- Primary Model
- Fallback Model
- Cache
- Observability
What Can We Improve?
Explore AI servicesMixed Workloads
Provider Outages
Multiple Providers
Repeated Questions
Traffic Spikes
Workload Benchmarks
What You Actually Get
- Routing Configuration
- Provider Adapters
- Failover Rules
- Cache Configuration
- Queue Configuration
- Usage Budgets
- Request Dashboard
- Rollout Checks
- Deployment Package
- Operations Runbook
- Routing Configuration
- Provider Adapters
- Failover Rules
- Cache Configuration
- Queue Configuration
- Usage Budgets
- Request Dashboard
- Rollout Checks
- Deployment Package
- Operations Runbook
Self-hosted AI work
Example: Handle a Provider Timeout.
Step 1 / 6
Request
Product summary requested
- Request
- Primary Call
- Timeout
- Fallback Call
- Response
- Trace
Worked example · Step 0 of 6
What Does It Take to Build?
Get a custom estimatePilot
- One workload
- Limited provider set
- Baseline review
- Most popular
Production
- Multiple models
- Live application traffic
- Production rollout
Multi-Product
- Multiple applications
- Multiple providers
- High-volume traffic
- Ongoing engineering
- Founded in
- 2013
- Projects delivered
- 550+
- Client countries
- 43+
- Global offices
- 3
FAQs
What do you need for an initial review?
Representative request traces, model providers, traffic patterns, response-time targets and current usage costs help establish a baseline.
Do we need to change model providers?
No. We review the existing setup first. Provider changes are considered only where workload tests support them.
When is response caching appropriate?
When requests can reuse an answer without violating freshness, identity or data-access requirements. Personal or rapidly changing responses may need to bypass the cache.
How do you compare speed, cost and quality?
We test representative workloads against agreed answer-quality criteria, then compare response times, token usage and failure behaviour.
What affects scope and timeline?
Application count, traffic complexity, provider interfaces, available traces and rollout requirements shape the engagement.
Can changes be introduced gradually?
Yes. Routing and provider changes can be tested on a limited traffic path before a wider rollout, with a defined rollback route.
Where is your AI serving stack struggling?
Bring request traces, traffic patterns and your operating constraints.
