Moving from a demo to a dependable LLM API is mostly discipline. Cap what you send and what you return. Keep queues short. Efficiency in resource management and computational speed is crucial for production LLM APIs. Log the numbers against defined performance expectations. Practice failure so incidents feel routine. Use proven techniques for optimizing deployment and reliability.
Model retraining or fine-tuning should occur periodically to keep the model up-to-date and maintain performance. Fine-tuning involves training a model on a smaller, high-quality dataset tailored for a specific task or domain, ensuring the model remains relevant and effective. Pre-training, on the other hand, involves training a base model on a vast, unsupervised text corpus to learn general language patterns, forming the foundation for fine-tuning.
Try Compute today
Launch a vLLM inference server on Compute in France or UAE. You get a dedicated HTTPS endpoint with OpenAI‑style routes. Set context and output caps, place it near users, and measure TTFT/TPS before rollout.
Client hygiene (ship stable clients)
- Pin SDK versions and record them with each request.
- Set timeouts: request, connect, and stream idle timeouts.
- Retries with jitter on 429/5xx/timeouts. Keep a max attempt count.
- Idempotency keys for retried writes or tool calls.
- Streaming by default so users see progress and queues stay healthy.
- Small, consistent system prompts; trim history; keep max_tokens tight.
- Log request IDs and surface them in the UI for support.
- Specify input data types and formats for client requests to ensure correct processing and compatibility.
- Test client-side logic to assess reliability and catch issues before deployment.
- Validate client requests before sending to the server to ensure correctness and compliance. Implementing data validation checks throughout the pipeline ensures data quality and integrity.
- Use commands for client operations and automation, such as passing secrets or setting profiles.
- The function of client-side logic is to ensure stable and predictable interactions with the server.
Server settings (cap, protect, and guide)
- Context length set from real needs, not marketing max.
- Output caps per route; reject oversized asks with helpful errors.
- Token‑aware rate limits to prevent a single user from freezing others.
- Fair scheduling for long prompts vs short ones.
- Clear error schema with type, code, message, and request_id.
- Specify log and error response formats (e.g., JSON, YAML) for consistency across systems.
- Securely handle server-side operations such as encryption and decryption of data keys.
- Monitor and optimize server resource usage to ensure efficient operation and cost control.
- Configure server settings to support scaling for increased users and traffic, including load balancing.
- Ensure server settings comply with data privacy regulations (e.g., GDPR). Data for LLMs should be diverse, ethically sourced, and properly licensed, avoiding personally identifiable information to comply with privacy regulations.
- Document and enforce compliance requirements in server configuration to safeguard data and meet regulatory standards.
- TLS everywhere; HSTS on; modern ciphers.
- Logs: counts and timings, not raw text by default.
- Region placement close to users (EU in France; ME in UAE).
Reliability patterns (make steady the default)
- Health and readiness probes at the gateway.
- Circuit breakers and backpressure when queues stretch.
- Graceful shutdown to drain streams on deploys.
- Warm spares or a second node for predictable peaks.
- Sticky sessions only if cache reuse is material and safe.
- Monitoring tools to track reliability in real time and monitor system health.
- Implement monitoring for reliability metrics and system health to detect issues early.
- Cross region load shedding to maintain reliability during traffic spikes and distribute load efficiently.
- Identify reliability risks and address them proactively to ensure optimal operation. Risk assessments help uncover potential failure points and vulnerabilities during the testing process.
- Define measures for reliability and track them over time to quantify improvements.
- Establish an evaluation framework for reliability patterns to assess and ensure system robustness. Creating an evaluation framework is essential for measuring how well an LLM performs based on specific metrics.
Failure drills (practice, then automate)
Establishing a standardized procedure for failure drills is essential to ensure consistent and reliable system behavior during unexpected events. Red teaming assesses model vulnerabilities and potential misuse by employing security experts to probe the model, further enhancing system resilience.
- Timeout spike: verify retries and user messaging. If the failure drill fails, document the issue and escalate for further investigation.
- Out‑of‑memory: confirm caps hold and alerts fire.
- Node restart: check stream recovery and quick warm‑up.
- Gateway failover: prove DNS/health checks switch traffic.
- Cancel storms: regularly perform cleanup drills to ensure KV‑cache blocks are freed.
- Hot reload/model swap: canary first; verify metrics and quality.
There are various methods for conducting failure drills, such as automated scripts, manual interventions, or adversarial testing. Integrating failure drills into the overall testing process helps validate system resilience and identify areas for improvement.
Change management (avoid surprises)
- Develop structured change management strategies to ensure consistent and reliable updates.
- Version models and params; use stable deployment names.
- Shadow traffic before you flip defaults.
- Canary rollout with automatic rollback on TTFT/TPS regression.
- Changelogs tied to dashboards and on‑call notes. Clearly explain the difference between major and minor changes to help teams understand the impact and scope of each update.
- Track improvements and their impact on deployment to maintain backward compatibility and measure ongoing progress.
- Access controls on who can ship models and change caps.
- Identify areas for improvement during change management reviews to refine processes and optimize system performance. CI/CD pipelines streamline testing, deployment, and model versioning to ensure consistency during deployment.
Security & privacy (basics that matter)
- Per‑service keys, with strong key management practices including regular key rotation, access controls, and monitoring to ensure secure encryption and decryption operations.
- Managed security services for encryption and key management, facilitating secure handling of encryption keys and supporting operational security in complex environments.
- Proactively address security vulnerabilities, data access challenges, and system performance issues to maintain effective and secure LLM deployment.
- Ensure compliance with data privacy and security regulations such as GDPR, and maintain robust data governance measures.
- Comply with all relevant legal and regulatory requirements to avoid fines and protect data privacy rights.
- IP allowlists for admin surfaces; HTTPS only for inference.
- Short retention for logs; no raw prompts by default.
- DSR path to find/delete user‑tied records.
- Secrets manager; no secrets in code or chat.
- Supplier DPAs and a maintained subprocessor list (see EU checklist).
Observability (measure what users feel)
- TTFT p50/p95 and TPS p50/p95 with traffic overlay. Continuously monitor these key metrics to track real-time performance.
- Queue length, GPU memory headroom, cache hit rate. Optimize for efficiency by monitoring resource usage and minimizing bottlenecks.
- Prefill vs decode time to diagnose prompt vs output issues.
- Error rates by type (OOM, timeouts, 4xx/5xx).
- Sensible alerts: TTFT p95 > target, TPS drop, low memory, error spikes. Set clear performance expectations by defining alert thresholds and targets.
Try Compute today
Deploy a vLLM endpoint on Compute. Choose your region, set caps, and point your OpenAI client at the new base URL. Keep data local and performance predictable.
Documentation and knowledge management (keep your team and users in sync)
Good documentation isn't just nice to have—it's what keeps your LLM API running smooth when things get complex. As you scale, clear docs keep everyone on the same page and stop small mistakes from becoming big problems.
- Write down how you deploy things step by step. Cover testing, rollouts, rollbacks, and what to do when things break. Make it easy to find and update when you need to.
- Keep one place for the truth about configurations, environment settings, and deployment details. This stops teams from working with different information as your system grows.
- Build simple guides for the stuff you do most: setting up test environments, running your tests, doing careful deployments. Show examples and what should happen next.
- Track your decisions in a shared space so you remember why you made choices. Teams change. Requirements change. Context shouldn't disappear.
- Update your docs after big deployments or when you improve how things work. Old information causes mistakes and wastes time.
- Share access with everyone who needs it—developers, QA folks, operations, support teams. Everyone should see the latest information and procedures.
Good documentation multiplies your team's effectiveness. It keeps testing and deployment smooth, prevents repeated mistakes, and helps your system grow without breaking as your business expands.
Ship dependable LLM APIs with a simple checklist
Achieving success with dependable LLM APIs requires following a consistent checklist. Cap tokens, stream, and place the endpoint near users. Watch TTFT/TPS and memory headroom. Practice failure and keep rollbacks one click away. These moves lower incidents and lower cost at the same time. Continuously identify areas for optimization and focus on ongoing improvement to ensure long-term reliability and effectiveness.
FAQ
What is a good TTFT target for chat?
Aim for ≤800 ms p95 for short prompts in‑region. If you are over, trim prompts, cap outputs, and check cache headroom before changing hardware.
Where should rate limits live—client or server?
Both. Clients should back off; servers should enforce token‑aware limits to protect everyone else.
Do we need multi‑region from day one?
No. Start in the region where most users live. Add a second region when latency, regulation, or redundancy demands it.
How often should we rotate keys?
Set a regular cadence (e.g., 90 days) and rotate immediately after incidents or staffing changes.
What is the safest way to update models?
Use deployment names, shadow traffic, and a short canary. Roll back on TTFT/TPS regression or quality drift.
Can streaming increase costs?
No—streaming usually reduces waste by keeping max_tokens tight and letting users stop early.
