Can we run it inside our own network?

Yes. The router runs as a container or a Helm release on your infrastructure, and a routing rule keeps regulated data on the models you allow.

Will this lock us into another vendor?

No. The router is Apache-2.0 open source, your provider keys stay yours, and the API is OpenAI-compatible, so leaving is a change of base URL.

How much can we save on our LLM bill?

On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.

Enterprise

One decision layer in front of every model, for the whole organisation.

Usage per workspace, tenant, key and member; budgets that refuse instead of surprising; a policy no score can override; a hash-chained audit trail; SSO and roles; the browser extension and desktop app rolled out to every desk with the settings pinned. Hosted, in your cloud or on your servers.

Get a free API key Route this request in the playground

Free plan, no card. Open source under Apache-2.0; self-hosting is free forever.

From the benchmark

100%

of requests traced, attributed and auditable

Every metered request carries its key, member, tenant, target, tokens, cost and outcome; the governance report shows every control in force with a posture score and the findings behind it.

The whole benchmark, with the cases the router lost

The problem

Every team bought its own AI. Nobody can see the whole bill, and nobody can enforce a rule.

Keys in spreadsheets, a different model per department, personal data in prompts nobody reviewed, and an invoice that arrives after the quarter closed. The platform team is asked for a policy and has nowhere to apply it.

Spend is visible per vendor, not per team or purpose
A data-handling rule is a document, not a control
An auditor asks where a request went six months ago

How the router answers it

Three things it does for every request.

Every dollar attributed, every limit enforced
Usage is recorded against the key, the member, the tenant and the workspace; daily and monthly budgets per workspace and tenant are ceilings that refuse once reached.
Decide once what may happen
Data boundary, region, cost and latency ceilings, allowed and denied targets, PII handling and payload logging apply to every key before any model is scored. Tenants carry their own constraints.
On the record, for the organisation
A hash-chained audit trail of decisions and outcomes, members with roles, single sign-on, an organisation's verified addresses, and managed rollout of the extension and desktop app with policy files.

Watch one decision

Illustrative: the lowest cost that still clears the quality the task needs. The live quote below is the real one.

A real request

The workspace policy, as the governance page writes it.

It is not a prompt, a suggestion or a weight: a target that fails it is not a candidate, and the trace lists it under policy rejections with the reason.

Route it in the playground and read the trace

Exampleyaml

data_boundary: private        # never a public endpoint
region: eu                    # EU targets only
contains_pii: true            # treat every request as if it may carry PII
deny_targets: [llm-frontier]  # never this one, whatever the score
max_cost_per_1k: 0.01
daily_budget_usd: 250
log_payloads: false           # prompts and answers are not kept

Where this stands next to the alternatives, and how to go deeper.

Install

curl -fsSLO https://opensmartroute.ai/download/helm && helm install router opensmartroute-*.tgz

The enterprise architecture guide

Or no install at all: the hosted API answers a plain curl with the decision, and the browser extension shows the price and the router's pick under the composer of the AI chat sites you already use.

For the business

What it does for the whole organisation.

Cloud, self-hosted or on-premises
Run it where the data is
The same router ships as a hosted platform, a container image and a Helm chart; the data boundary is a routing rule.
Deployment options
One URL, every vendor
No vendor lock-in
Point your OpenAI-compatible client at one address; add, swap or retire providers in the catalogue without touching application code.
Every vendor side by side
89% less
Lower cost per request
On the 150-prompt benchmark the router spent 89% less than sending every prompt to the reference model, and the judged quality went up, not down.
Read the benchmark

Can we run it inside our own network?: Yes. The router runs as a container or a Helm release on your infrastructure, and a routing rule keeps regulated data on the models you allow.
Will this lock us into another vendor?: No. The router is Apache-2.0 open source, your provider keys stay yours, and the API is OpenAI-compatible, so leaving is a change of base URL.
How much can we save on our LLM bill?: On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.

Route your first request in under a minute.

OpenSmartRoute is open source and the free plan keeps the full trace of every decision. Create a workspace, mint a key and send the request above.

Create a free account Read the benchmark

Free plan, no card. Fifteen thousand decisions a month with the full trace.

data_boundary: private # never a public endpoint region: eu # EU targets only contains_pii: true # treat every request as if it may carry PII deny_targets: [llm-frontier] # never this one, whatever the score max_cost_per_1k: 0.01 daily_budget_usd: 250 log_payloads: false # prompts and answers are not kept

One decision layer in front of every model, for the whole organisation.

Every team bought its own AI. Nobody can see the whole bill, and nobody can enforce a rule.

Three things it does for every request.

Every dollar attributed, every limit enforced

Decide once what may happen

On the record, for the organisation

The workspace policy, as the governance page writes it.

Where this stands next to the alternatives, and how to go deeper.

What it does for the whole organisation.

Run it where the data is

No vendor lock-in

Lower cost per request

Route your first request in under a minute.

One decision layer in front of every model, for the whole organisation.

Every team bought its own AI. Nobody can see the whole bill, and nobody can enforce a rule.

Three things it does for every request.

Every dollar attributed, every limit enforced

Decide once what may happen

On the record, for the organisation

The workspace policy, as the governance page writes it.

Where this stands next to the alternatives, and how to go deeper.

What it does for the whole organisation.

Run it where the data is

No vendor lock-in

Lower cost per request

Route your first request in under a minute.