Memtro vs LiteLLM
An open-source gateway you run, with virtual keys and money budgets, versus a hosted endpoint that adds memory, tools and per-team token allowances.
This comparison describes LiteLLM as we understand it at the time of writing from its public documentation. Products change; check the current details before deciding.
LiteLLM is an open-source AI gateway: a proxy you run yourself, under the MIT licence, that exposes one OpenAI-compatible endpoint in front of more than a hundred providers. It is operations software for teams that run models at scale — virtual keys per user, team and project, each with its own models, budgets and rate limits; the cost of every request recorded; logs shipped to Langfuse, Datadog, OpenTelemetry or Prometheus; load balancing, fallbacks and retries between deployments; guardrails for content and PII. Deployment is Docker, Helm or Terraform with a Postgres database, and SSO, audit logs and multi-team management are sold as enterprise features.
Memtro is a hosted service that is also a gateway: one OpenAI-compatible endpoint and an Anthropic Messages endpoint in front of the providers you hold keys for, with routing by difficulty, fallbacks per level and token allowances per team. What it adds is the reason it exists: your organisation's memory is injected into every request and learned from, and the model can read the systems you have connected. There is nothing to deploy.
Where they differ
| Memtro | LiteLLM | |
|---|---|---|
| Shape | Hosted endpoint plus MCP server; one price per organisation by headcount | Open-source proxy you deploy and operate; enterprise tier for SSO, audit logs, multi-team |
| Providers | Those you hold keys for, including Claude Code and Codex subscriptions | 100+ providers behind one endpoint |
| Keys | Personal and organisation API keys; permissions and connection grants per team | Virtual keys per user, team and project, each with models, budgets and rate limits |
| Routing | By difficulty on a ladder per level, with a second and third choice behind each | Load balancing, fallbacks and retries between deployments |
| Cost control | Token allowances per level and choice, organisation and team; the cheaper choice answers when one runs out | Budgets in money per key, team and user; the cost of each request recorded |
| Observability | Request log by user, team, model and day, with CSV export | Logging to Langfuse, Datadog, OpenTelemetry, Prometheus and others |
| Guardrails | Permissions and grants decide what a request may read or change | Content filters, PII masking and policies on requests and responses |
| Memory and tools | A bitemporal knowledge graph of the company; 144 connectors, reading by default and acting where granted | None; the proxy relays what the client sends |
| Hosting | Hosted | Self-hosted; your database, your upgrades |
Honestly, where LiteLLM is stronger
If what you need is a gateway in the infrastructure sense, LiteLLM is the more complete one. It supports far more providers, it is yours to run inside your own network, and its control plane is built for platform teams: a virtual key for every project with a money budget and a rate limit, spend broken down by tag, logs flowing into whatever observability stack you already run, guardrails applied before a prompt leaves the building. None of that depends on a vendor's roadmap, and the licence lets you read and change the code.
LiteLLM also prices in money, from the provider rates it knows. Memtro deliberately counts tokens instead, because it does not know what your provider charges you, and an invented price is worse than none.
Where Memtro is stronger
LiteLLM moves requests; Memtro changes what is in them. Before a prompt reaches a model, the relevant memories and graph context are added; after the answer, the durable facts in it are kept with the tool, record and URL they came from; and during the answer the model can read the CRM, the ledger or the support desk through connector tools. A LiteLLM deployment has no memory and no tools of its own — your application brings those, which means your developers build and maintain them.
Routing differs in kind as well. LiteLLM balances load and fails over between deployments of a model you named; Memtro decides which model answers at all, by scoring each request on a five-level scale and walking a ladder you set, with the decision returned on the response. The same ladder is what cost control caps, so a team's allowance and its model choice are one configuration rather than two.
For a team without a platform engineer, the operational difference is the whole decision: Memtro is an endpoint and a key, not a service to run.
When to choose which
Choose LiteLLM when you run models in production, want the gateway inside your own infrastructure, and need money budgets, enterprise logging and guardrails across many providers and many applications.
Choose Memtro when the gateway is the smaller part of the problem: you want the tools a team already uses to share one memory of the company, to read its systems, and to have their model chosen and capped per team, without deploying anything. If you already run LiteLLM, Memtro can sit behind it as an OpenAI-compatible upstream, or in front of it as a provider.