Skip to main content
Actualyze AI LogoActualyze AI Logo
Get started
All comparisons
ComparePlatform-adjacent gateway

Actualyze vs LiteLLM

Actualyze. The whole package for governing AI, with masking from the first request and no unknown cost as $0.

LiteLLM is an AI gateway that puts many providers behind one API, and Actualyze is the AI gateway and AI governance platform for the enterprise. On top of the gateway, Actualyze gives each model its own approver and finance a month it can close.

Competitor facts checked against LiteLLM's documentation on .

In short

Start 30-day free trialBook a demo
each gateway's governance

Actualyze

Your organization

policy from Team Owners and Budget Managers

Inference

  • public APIs
  • brokers
  • GPU clouds
  • your own servers

LiteLLM

LiteLLM proxy

policy from virtual key settings

Inference

  • supported providers
  • OpenAI-compatible endpoints
  • your servers, such as vLLM

Side by side

LiteLLM is an AI gateway that gives applications one OpenAI-compatible API to many model providers. LiteLLM gives each caller a virtual key that carries the caller's models, budgets, and rate limits.

Actualyze and LiteLLM, side by side
ActualyzeLiteLLM
Pricing and routing
Where cost figures come from

Priced per model and provider. Unknown cost is never $0

LiteLLM's model cost map, with overrides per deployment

A deployment is one model at one provider, and any price in the map can be set on it.

One model, many providers

One model through any provider the caller can use

Deployments that share a model name are load-balanced

LiteLLM calls them a model group and grants access to the group as a whole.

Routing across models

Sizes each request, picks the cheapest model strong enough

A Virtual Model, one name over several models, picks only among models the caller can already use.

A beta Auto Router scores each request into a tier

Each tier names one model or a pool of models, from simple to reasoning.

Provider prompt caches

Stays on the cached model unless moving saves money

Opt-in sticky sessions keep a conversation on one deployment

LiteLLM suggests them where deployments don't share state, such as a provider's prompt cache.

Access and spend
Who approves access and spend

Each model names its own approver. Budget Managers own spend

Proxy admins grant model groups and set budgets

Enterprise team admins can lower or raise a team's budget only where a proxy admin allows.

Budgets and limits

Budgets nest. A call past the limit is rejected

Budgets on keys, users, teams, and team members

LiteLLM checks each request against its budgets before calling the provider.

Chargeback and the month

Finance closes the month on one statement

Daily spend by model, provider, and API key

Protection
On from the first request

Personal data masked from the first request

The default guardrail masks personal data in prompts and responses, and nobody can delete it.

Nothing is scanned until a guardrail is defined

A guardrail marked default_on then runs on every request.

Weakening protection

Protection can't be quietly switched off

Enterprise admins can stop teams toggling guardrails

LiteLLM documents no written reason or alert when a guardrail is weakened.

Records and reach
Audit record

Each routed call records which model answered and why

The record can't be edited, and it names the model the app asked for.

Spend logs name the model group and serving deployment

What the controls reach

Any LLM, anywhere, behind an OpenAI- or Anthropic-style API

Your own servers too, through a relay you run that only connects outward.

Supported providers and OpenAI-compatible endpoints

Bringing a model online

Each model goes live only after its own approval

Admins add and test models in the UI or API

Test Connection checks a model against its provider with the stored credentials.

Deployment
Deployment

Hosted today. On-prem deployment coming in 2027

Self-hosted on AWS, Google Cloud, Azure, or Kubernetes

Pricing

Actualyze. Priced at what you really pay, down to the call.

Actualyze

modelnew-model-preview

througha GPU cloud

tokens1.4M

cost

unknown, not $0

statusserved, and flagged to FinOps

Actualyze serves it from day one and never books it as $0.

LiteLLM

modelnew-model-preview

througha GPU cloud

tokens1.4M

cost

$0.00

logThis model isn't mapped yet

LiteLLM records $0 spend for a model it hasn't mapped.

Actualyze flags every unpriced call, because an unknown cost is never $0.

When a team starts on a model with no published price, Actualyze serves the call, keeps its token count, and marks the cost unpriced for your FinOps team to see. Confirm a rate, such as one you negotiated, and Actualyze prices every call with it from then on. No gap ever passes for a free model.

LiteLLM prices each deployment from its model cost map.

In LiteLLM, a deployment is one model at one provider, and a deployment can override the map's prices. A percentage discount per provider covers negotiated rates. A model missing from the map logs a warning and records $0 spend.

Protection

Actualyze. Personal data is masked from the first request.

Actualyze

  1. workspace createdthe default guardrail is already on

first request

Reply to [REDACTED:EMAIL_ADDRESSES]

Actualyze masks the email from the first request.

LiteLLM

  1. a PII guardrail is definedPresidio, in config.yaml
  2. default_on is set to trueor it is attached to keys or teams

each request

Presidio masks or blocks the PII

LiteLLM scans nothing until a guardrail is defined.

Actualyze protects personal data on day one, before anyone writes a policy.

Every Actualyze workspace is created with masking already on, for prompts on the way out and responses on the way back. The customer's email in that first-day prompt is masked before the provider sees it. The default guardrail can't be deleted, and pausing it means writing down why, with every Workspace Admin notified. Actualyze gives every workspace the safest starting point in AI governance.

LiteLLM scans nothing until a guardrail is defined.

A LiteLLM guardrail set to default_on runs on every request. Otherwise a guardrail runs only where it is attached to a key, a team, or a request. LiteLLM's Presidio guardrail masks or blocks PII in the request.

Approvals

Actualyze. The leading delegation and approval system for AI spend.

Actualyze

use Model A
raise the Support budget
Support's Team Ownernamed by Model A's rule
Engineering's Budget Managerowns the parent budget
Model A
Engineering
Support

Actualyze sends each ask to the person who owns it.

LiteLLM

use Model A
raise the Support budget
a proxy adminupdates each team's models and budget
the team's models
the team's budgetor an Enterprise team admin, if enabled

A LiteLLM proxy admin sets the team's models and its budget.

In Actualyze, each model names its own approver and each budget has its own owner.

A team's model request goes to whoever that model's rule names, the Team Owner who heads the requesting team or a Workspace Admin. A bigger team budget needs a yes from the Budget Manager one level up, the person who answers for the department's money. Budgets nest, so a department gives its teams room and keeps the final say. Each request lands with its owner, and no central admin queue holds anyone up.

LiteLLM proxy admins grant models and set budgets.

A LiteLLM proxy admin, the role for the people who run the gateway, grants each model group to keys, users, and teams. On LiteLLM Enterprise, a team admin manages the team's members, keys, and member budgets. A team admin changes the team's own budget only where a proxy admin allows.

Routing

Actualyze. The best model routing in the field.

Actualyze

a customer's question
support-answeran Actualyze Virtual Model
a routing model we train sizes itthen picks the cheapest model strong enough
  • Model A$0.15 / Mnot strong enough
  • Model B$0.60 / Mstrong enough
  • Model C$3.00 / Mstrong enough
    • OpenAI
    • Azure
    • Anthropicstandby
    • Bedrockserving
    • Vertexstandby
    • Google
lowest cost, strong enoughModel B through Bedrock

Actualyze answers with the cheapest model strong enough.

LiteLLM

a customer's question
a LiteLLM Auto Router, in beta
scores it into a tiera heuristic scorer, an LLM or JEV call, or a plugin
  • Model Asimple
  • Model Bmedium, load-balanced
  • Model Ccomplex
    • OpenAI
    • Anthropic
    • Bedrock
    • Anthropic
that tier's answer

LiteLLM returns the answer from that tier's model.

Actualyze sends each request to a model strong enough for it, with no tiers to map.

An Actualyze Virtual Model is one name for several models. There are no tier rules to write and no classifier to pick, because Actualyze brings a routing model of its own, trained to judge what each request needs. Each model you place behind the name is served over all the provider routes you connect, with standby routes ready when one fails.

LiteLLM's Auto Router scores each request into a tier.

LiteLLM's beta Auto Router classifies each request with a heuristic scorer, an LLM call, JEV, or a plugin, then sends it to the model or pool set for that tier. A tier's model name can spread across several deployments, and LiteLLM falls back to another model when a call fails.

Evaluate

Six questions every AI gateway should answer live

If your AI gateway can't tell you who approved a model or what last month cost each team, it's just moving traffic. Put these questions to every vendor on your list, and ask to see each answer working in the product. Actualyze answers every one today, and each comes with a check you can run in your own trial.

  1. Who approves a team's access to each model?

    The leading delegation and approval system for AI spend starts here. In Actualyze, each model names its own approver.

    Request a model that needs approval as a team member, and check who receives the request and where the decision is recorded.

  2. What decides which model answers each request?

    In Actualyze, a routing model we train sizes each request and picks the cheapest model strong enough, the best model routing in the field.

    Send an easy question and a hard one to one Virtual Model, then see which model each was routed to, and why.

  3. What happens to a call when its budget runs out?

    Actualyze rejects it before any model is contacted, so a budget is a real limit and never a surprise on the bill.

    Set a small test budget and confirm the next request is rejected once it reaches its limit.

  4. Does an unknown cost ever show up as $0?

    An unknown cost is never $0 in Actualyze, which marks it unpriced and flags it for FinOps, so no spend hides behind a zero.

    Connect a third-party endpoint with no catalog price, call it, and check the call reads unpriced, not $0.

  5. What is protected before anyone writes a policy?

    Personal data is masked from the first request in Actualyze, in prompts and responses, by a default guardrail nobody can delete.

    Open a new workspace's guardrails before writing any policy, and confirm the default guardrail is already enforcing.

  6. Can finance close the month on AI spend?

    Finance closes the month on AI spend with one Actualyze statement, a row for each budget, reviewed and then settled.

    Generate a statement for a month with traffic, then move it from draft to reviewed to settled.

Common questions

What is the difference between Actualyze and LiteLLM?
The difference between Actualyze and LiteLLM is who sets the rules. Actualyze takes them from the people accountable for each model and budget, the approver each model's rule names and the Budget Manager of each budget. LiteLLM takes them from the settings on each virtual key, which its admins manage.
Does Actualyze route for cost as well as quality?
Yes. A routing model Actualyze trains sizes each request and sends it to the cheapest model strong enough, so routine questions stop paying frontier prices. Each call is priced at the provider that served it, so the savings show up on every team's budget.
How do applications move from LiteLLM to Actualyze?
Each application moves with three settings and no rewrite. It takes a new base URL, an Actualyze API key, and a model name, and from its first call it runs under approvals, budgets, masking, and an audit trail. Give each application or agent its own API key, and its spend lands on the right team and budget.

Sources

24 pages, checked

LiteLLM and the other product names on this page are trademarks of their owners. Actualyze is not affiliated with or endorsed by them.

Head to head

More comparisons

Give every model an approver and every call a budget.

Connect the providers you already use and test every claim on this page against your own traffic. Or book a demo. We'll walk you through it, and send the Virtual Model engine whitepaper if routing is on your list.

Start 30-day free trialBook a demo