01
API costs eating gross margin
Token spend growing in step with customer growth.
LLM API bills that grow with every new customer
Commercial LLM APIs to self-hosted models on your own GPUs
When token volume is high and steady, serving an open-weight model with vLLM on your own GPUs can cost less than API pricing. We test quality on your prompts first, then move traffic gradually.
Symptoms
Signs your platform has this problem
If several of these sound familiar, the plan below is where we would start.
02
Data privacy requirements
Customers who won't allow sensitive data to be sent to a third-party model API.
03
Rate limits and provider outages
Throttling and outages at the provider breaking production features.
Remediation plan
How we fix it, step by step
Each phase ends with a measurement, so you can see what changed before the next one starts.
01
Model evaluation
Benchmarking open-weight models (for example Llama, Qwen or DeepSeek) against your current prompts and outputs.
02
GPU infrastructure
Provisioning GPU instances (A100, H100 or similar) in your cloud account with private networking.
03
vLLM serving
Configuring vLLM with PagedAttention, continuous batching and quantization for throughput.
04
OpenAI-compatible gateway
Routing traffic through an OpenAI-compatible endpoint, so application code barely changes, with the API as a fallback.
Technical checklist
Remediation checklist
What we check before a change goes to production:
- Benchmark open-weight models against the current API on your own prompts
- Run GPU instances inside your private cloud network
- Serve models with vLLM using PagedAttention and continuous batching
- Put an OpenAI-compatible gateway with automatic fallback in front
What we measure
We take a baseline first and report the same measurements after each change, from your own monitoring — evidence, not promised results.
- Cost per 1M tokens
- API pricing vs GPU cost at your real utilization
- Quality
- Scores on an evaluation set built from your prompts
- Time to first token
- Streaming latency at p95 under production load
Related service
AI development
LLM systems built for compliance review: schema-validated extraction, human-in-the-loop workflows, and audit trails — measured in cycle time, not demos.
Explore AI developmentQuestions
Questions about this remediation
When volume is high and steady enough to keep the GPUs busy. The break-even depends on model size, prompt and output lengths, and current API prices, so we model it with your traffic before recommending a move.
On narrow tasks such as structured extraction, classification and domain Q&A, a well-chosen or fine-tuned open-weight model often comes close. We measure it on an evaluation set built from your data before any traffic moves.
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Redis memory optimization and eviction prevention
Prevent Redis memory exhaustion and key evictions: audit memory usage, set missing TTLs, use compact data structures and shard with Redis Cluster where needed.
Want an engineer to look at this with you?
Send us the symptoms and any metrics you have. We'll reply within one business day, set up a call and agree what to measure before anything changes.