Technology · Model routing
LiteLLM
Unified API · Proxy · Budgets · Usage tracking
LiteLLM puts one interface in front of every model provider, and a proxy in front of that. The immediate benefit is portability — swapping a model stops being a code change. The lasting benefit is governance: once every call passes through one place, spend, rate limits and fallbacks become things you can see and set, rather than things you discover on an invoice.
How TrivialWorks uses it.
We reach for LiteLLM when a system uses more than one model, which most serious systems eventually do — a capable model for hard requests, a cheap one for bulk classification, a self-hosted one for sensitive data. Routing that through a single proxy means the choice is policy rather than architecture. It is also where cost control genuinely lives: budgets per team and per feature, enforced before the spend happens rather than reported after it.
Decision guide
Should your project use LiteLLM?
Practical selection guidance — the conversation we would have with you before writing a line of code.
When it’s the right choice
- Systems using several models — tiered by capability, cost or data-residency requirements
- Organisations that need AI spend attributed per team, per feature or per client, and capped before it is exceeded
- Products needing provider fallbacks, so one vendor's outage degrades the service rather than stopping it
- Teams who want to swap or trial models without touching application code each time
When it isn’t
- A single feature calling a single provider — a proxy in the middle is one more component to run and one more thing between you and a working request
- Ultra-low-latency paths where an extra network hop is a measurable share of the budget
- Teams unwilling to operate the proxy: put it in the critical path of every AI call and it inherits the availability requirements of everything behind it
Consider Provider SDK directly — One provider, one model, no governance requirement — the SDK is simpler and there is nothing to keep running.
Consider LangChain — You need orchestration and tool use as well; its abstractions already give some provider portability.
Best use cases
Where LiteLLM makes practical sense.
Model tiering by workload
Expensive models reserved for hard requests and cheap ones handling bulk classification, with routing rules rather than scattered conditionals.
Spend governance
Budgets and rate limits per team, feature or client, enforced at the proxy — so overspend is prevented, not reported.
Provider resilience
Automatic fallback to an alternative model when a provider is degraded, so a vendor incident is a slower answer rather than an outage.
Hybrid private and commercial routing
Sensitive workloads pinned to self-hosted models and everything else routed commercially, as one enforced policy.
Proof
Where it shows up in our work.
No published case study names this stack yet — most of our work ships under NDA, and we would rather show you nothing than invent something. The work ledger shows how we handle that honestly.
Related services
Services that commonly use it.
Questions
LiteLLM, asked straight.
Why route through a proxy instead of calling providers directly?
Because the second model always arrives. Once a system uses more than one, the proxy is where routing, fallbacks, budgets and usage tracking live in one place instead of being reimplemented in each service. For a single model and a single provider, calling directly is the better answer.
Does it help control what we spend on AI?
It is the most practical lever we know of. Per-team and per-feature budgets are enforced at the point of the call, and usage is attributed rather than arriving as one aggregate invoice. Combined with model tiering, that is usually where the largest savings are found — the expensive model was answering questions the cheap one could have.
What happens when a provider goes down?
Configured fallbacks route to an alternative model automatically, so the feature degrades in quality rather than failing. That is worth designing deliberately: silently answering with a weaker model is fine for classification and not fine everywhere, so the policy is set per feature.
Does it add latency?
A small amount — one extra hop, typically negligible against the model's own response time, and recoverable through caching at the proxy. On genuinely latency-critical paths we measure it rather than assume, and will bypass the proxy where the numbers justify it.
Thinking about LiteLLM?
Send the requirement and you get back a functional specification — screens, data model, stack and an estimate — at no charge. If LiteLLM is the wrong choice for it, that will be in there too.