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 inference engineered 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
Should we use a hosted model API or run an LLM in our own environment?
Choose against the workload and operating constraints. A hosted API reduces responsibility for model-serving infrastructure, while its terms, data handling, availability and usage pricing still need review. Running a suitable licensed model in your environment gives you more infrastructure control but adds capacity planning, security, updates and operational support. Compare representative output quality, privacy requirements, response time and peak demand, then include idle capacity, engineering effort and support in total cost. Self-hosting is not automatically cheaper or better, and a mixed approach may suit different workloads.
What determines the monthly running cost of an AI application?
Monthly cost starts with request volume, model choice and the size of input context and generated output. Retries, tool calls and multi-step workflows can add more model usage than one visible user action suggests. Include inference infrastructure or API charges, retrieval and embedding work, data storage, network traffic, monitoring and support. For your own deployment, allow for spare and idle capacity as well as peak load. Forecast typical and busy periods using representative requests, then compare actual usage with those assumptions after launch.
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.
