Compare·Open-source LLM proxy and SDK
Continuum
Multi-agent workbench
VS
LiteLLM
Open-source LLM proxy and SDK

Continuum vs LiteLLM

Build the gateway yourself, or get the coding-agent governance outcome without running one.

LiteLLM is the reference open-source answer to the question every platform team eventually asks: how do we put one endpoint, one key system, and one budget in front of every model our company uses. Its site advertises 140+ LLM providers and 1,892 unique models, and the open-source tier is free to self-host with virtual keys, budgets, teams, load balancing, RPM and TPM limits, guardrails, and logging into Langfuse, Arize Phoenix, LangSmith, or OTEL. The repository carries 56.8k GitHub stars. Enterprise adds SSO, SCIM, OIDC and JWT auth, RBAC, audit logs, 24/7 support with SLAs, and SOC 2 Type 2 and ISO 27001, at custom pricing with a 30-day instant trial key. Continuum is not a proxy and does not try to be one. It is the free workbench where a developer runs Claude Code, Codex, Cursor, Grok, and OpenCode under their own subscriptions, one git worktree and branch per session, with plan gates, per-hunk diff review, and pull requests in the session. It reaches the same governance outcomes for coding agents, weekly spend caps, model policy, member approvals, and cost by repo, without anyone deploying and paging for a gateway. LiteLLM wins when you are building AI infrastructure. Continuum wins when you want the result and not the infrastructure.

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Workflow Monday path Matrix Decide Money FAQ

Updated 2026-08-03 · Mac stable · Win/Linux desktop beta

Choose Continuum when

Pick Continuum when the goal is governed, observable coding-agent work today: worktree isolation, plan gates, diff and PR review, live quota gauges, weekly spend caps, and cost by repo, with nothing to deploy or operate.

Choose LiteLLM when

Pick LiteLLM when your team is building its own AI platform and needs a self-hosted proxy across 140+ providers with virtual keys, budgets, load balancing, guardrails, and your choice of observability backend.

Snapshot adjacent job
Dimension Continuum LiteLLM
Product class Packaged coding-agent workbench Self-hosted proxy plus Python SDK
Who operates it Nobody: it is an app Your platform team runs and pages on it
Provider reach Claude, Codex, Cursor, Grok, OpenCode agents 140+ providers, 1,892 models per the site
Governance Model policy · weekly caps · member approvals Virtual keys · budgets · teams · RPM and TPM limits
Observability Cost by repo, provider, model, day Langfuse, Arize Phoenix, LangSmith, OTEL
Repository work Worktree, branch, plan gate, diff, PR Not in scope
Price Free app · optional hosted tiers Free open source · Enterprise custom priced
Mac stable · web · iPhone · Watch · Win/Linux desktop beta · free app
01

Run a proxy vs get the outcome

In Continuum

Governed agent work with no gateway

09:10

An engineer starts three agents in three worktrees under subscriptions the company already pays for. No key is minted and no proxy is touched.

12:40

The org admin sets a weekly spend cap and a model policy, then approves a new member. The controls apply without a network hop.

15:20

A plan is approved from the phone, the resulting diff is reviewed hunk by hunk, and the pull request opens from the session.

17:30

Cost by repo shows where the week went, and the quota gauges say whether tomorrow's batch will fit in the window.

In LiteLLM

Standing up a company model endpoint

09:10

Deploy the LiteLLM proxy and confirm the providers your services actually call are covered by the 140+ integrations.

12:40

Mint virtual keys per team with budgets and RPM and TPM limits, and route logs into Langfuse or OTEL alongside existing telemetry.

15:20

Add guardrails and load balancing, then test failover behavior under a deliberately broken upstream.

17:30

Scope the Enterprise conversation if SSO, SCIM, RBAC, audit logs, or SLA-backed support are required, and start the 30-day trial key.

02

Monday path

Monday with the build-versus-outcome question made explicit

Install, connect, first session - steps you can run the same day.

01

Name the buyer. A platform team standardizing model access company-wide is a LiteLLM buyer. An engineering team that wants coding agents governed this week is a Continuum buyer.

02

If you are the platform team, deploy the LiteLLM proxy locally and confirm the providers you actually use are among the 140+ supported, not just the famous ones.

03

Mint virtual keys with budgets and RPM or TPM limits for two real teams, and wire logging into whichever backend you already run.

04

Price the operating cost honestly: who owns the deployment, who is paged when it fails, and whether SSO, SCIM, RBAC, and audit logs push you into a custom-priced Enterprise contract.

05

If you are the engineering team, install Continuum and start two worktree sessions on one bounded ticket under the subscriptions already on the machine.

06

Set a weekly spend cap and a model policy at the org level, and add a member approval, then confirm they hold without a proxy in the path.

07

Read cost by repo, provider, model, and day at the end of the week, and check the live quota gauges before launching the next batch.

08

Decide by scope. If the requirement is every model for every service, that is LiteLLM. If it is coding agents specifically, the proxy is a large detour.

How Continuum governs agents without a proxy

How Continuum gets governance without a gateway in the path

  • Sessions run the provider's real agent under the developer's own login, so there is no proxy to deploy, secure, scale, or page on.
  • Cost is reconstructed locally from agent logs and priced by repo, provider, model, and day, which is why BYOK sessions need no inference proxy to be visible.
  • Org controls apply at the account layer: model policy, weekly spend caps, and member approvals, rather than at a network hop.
  • Each managed session gets its own git worktree and branch, so isolation is a repository property rather than a routing rule.
  • Plan mode is read-only and approval-gated, which is the control a proxy structurally cannot offer because it only sees tokens.
  • Live quota gauges read each provider's own rate-limit window, including subscription windows that a metered gateway never sees.
  • Optional hosted inference exposes an OpenAI-compatible endpoint at https://continuumcode.ai/v1 and an Anthropic-compatible bare origin with cont_sk_ keys and an auto model router, for teams that do want one endpoint.
03

Proxy and workbench matrix

Cells use product nouns on both sides. Continuum is the multi-provider workbench; they win where their loop is the product.

Capability Continuum LiteLLM
Nothing to deploy or operate Installed app, no server in the request path Self-hosted proxy your team runs
Breadth of provider integrations Five agent providers plus an optional hosted router 140+ providers and 1,892 models per litellm.ai
Open source and forkable Free app, not an open-source proxy Public repository with 56.8k GitHub stars
Virtual keys with per-team budgets Org weekly spend caps and model policy, not key minting Virtual keys, budgets, teams, RPM and TPM limits
Coding-agent sessions in git worktrees Worktree and branch per managed session A gateway has no repository layer
Plan approval, diff review, PR merge Plan gate, per-hunk review, PR in session Out of scope
Cost attributed to a repository Repo, provider, model, and day, locally Spend tracking is per key, project, and user
Sees subscription usage, not just API tokens Live gauges on each provider's own quota window A proxy only sees traffic that passes through it
Pluggable observability backends Built-in local analytics rather than exporters Langfuse, Arize Phoenix, LangSmith, and OTEL
SSO, SCIM, RBAC, and audit logs Org member approvals and model policy; verify current enterprise scope Enterprise tier adds SSO, SCIM, OIDC and JWT, RBAC, audit logs
Compliance attestations Not asserted here; ask for the current posture Enterprise page states SOC 2 Type 2 and ISO 27001
Try before buying The whole app is free with your own subscriptions Free open source, plus a 30-day instant Enterprise trial key
04

Where build and buy actually split

01 · Layer

One is a component you run, the other is a product you open.

LiteLLM ships a proxy and an SDK. Someone on your team deploys it, secures it, scales it, and gets paged when it breaks. Continuum ships an application. The comparison is only meaningful because both end up answering the same executive question about who spent what on models.

02 · Scope

Every model for every service versus coding agents specifically.

LiteLLM's 140+ providers and 1,892 models exist because a platform endpoint must serve every workload in the company. Continuum covers Claude Code, Codex, Cursor, Grok, and OpenCode because those are the agents developers run against repositories. If your requirement is the former, the narrower product is not a substitute. Either way a model list is worth reading as models rather than as a count, which is what our model directory is for.

03 · What a proxy cannot see

Subscription work never crosses a gateway.

The dominant cost of coding agents at many teams is subscription seats, not metered API tokens, and that traffic never passes through a proxy. Continuum reads agent logs locally and prices them by repo, provider, model, and day, which is why it can report on work LiteLLM structurally cannot observe.

04 · Control surface

Keys and rate limits versus plans and diffs.

A gateway's controls are the ones a request boundary allows: virtual keys, budgets, RPM and TPM caps, guardrails. Continuum's controls are the ones a workbench allows: a read-only plan that must be approved before writes, per-hunk diff review, and a pull request gate. Neither set is a superset of the other.

05 · Cost of ownership

Free software is not free operations.

LiteLLM's open-source tier is genuinely free and genuinely capable, and 56.8k stars say people run it. The real price is an owner, a deployment, an on-call rotation, and a probable Enterprise contract once SSO, SCIM, RBAC, and audit logs are required. Count that before calling it the cheaper option.

05

Coexistence

Stack recipe

How people run both.

These sit at different layers and often coexist. A platform team can run LiteLLM as the company's model endpoint while its engineers operate coding agents through Continuum under their own subscriptions. Continuum does not ship a LiteLLM adapter, and its BYOK sessions deliberately talk to the provider directly, so treat any pairing as an infrastructure decision on your side rather than a shipped integration.

Download Continuum Providers
06

Pick by who operates it

Continuum

Choose Continuum to skip the infrastructure

  • You want governed coding-agent work this week and have nobody to own a proxy deployment.
  • Most of your agent spend is on subscriptions that would never cross a gateway anyway.
  • You need cost attributed to repositories and providers, not only to keys and projects.
  • Worktree isolation, plan approval, diff review, and in-session pull requests are part of the requirement.
  • Weekly spend caps, model policy, and member approvals cover your governance need.
  • You would rather add an optional hosted endpoint later than run one from day one.
LiteLLM

Choose LiteLLM to own the platform

  • You are building an internal AI platform that every service will call.
  • You need one endpoint across many providers, well beyond the coding-agent five.
  • Virtual keys, per-team budgets, and RPM and TPM limits are the required control shape.
  • You already run Langfuse, Arize Phoenix, LangSmith, or OTEL and want LLM traffic in the same place.
  • Self-hosting, including air-gapped deployment, is a hard requirement.
  • SSO, SCIM, RBAC, audit logs, SOC 2 Type 2, or ISO 27001 must come from the gateway itself.
07

What you pay and what you operate

Continuum

App + your labs

$0 for the app on Mac, iPhone, Watch, web, and the Windows and Linux desktop beta, running under the provider subscriptions you already pay for. Optional hosted inference is $25 per month for Plus, $100 for Max 100, $200 for Max 200, and $500 for Ultra, with weekly hosted-usage allowances of $25, $100, $200, and $1,000.

LiteLLM

Their bill

The open-source proxy is free to self-host, with 140+ provider integrations, virtual keys, budgets, teams, load balancing, RPM and TPM limits, guardrails, and logging to Langfuse, Arize Phoenix, LangSmith, and OTEL. Enterprise is custom priced with no public figure; it adds SSO, SCIM, OIDC and JWT auth, RBAC, audit logs, 24/7 support with response-time SLAs, and SOC 2 Type 2 and ISO 27001, and offers a 30-day instant trial key with no sales call.

How to compare

Total cost of work

Neither line item is the real number. LiteLLM's cost is mostly operational: the engineer who owns the deployment and the Enterprise quote that arrives with the compliance requirements. Continuum's cost is your existing provider subscriptions, plus a hosted tier only if you want one.

LiteLLM plans checked 3 August 2026 against their pricing page. Vendors reprice often - if you spot a stale figure, tell us and we will fix it. Continuum never marks up a session it runs under your own login.

Continuum ladder: Free app · Plus $25/mo · Max 100 · Max 200 · Ultra - full pricing. Also analytics, multi-account, devices.

08

Questions

Continuum vs LiteLLM.

Deep dives: docs, providers, sessions.

Only if what you wanted from LiteLLM was governed, observable coding-agent work. Continuum delivers weekly spend caps, model policy, member approvals, and cost by repo with nothing to deploy. It is not a substitute for a company-wide proxy across 140+ providers serving every service you run.

They sit at different layers, so yes in practice: a platform team can run LiteLLM as the company endpoint while engineers operate coding agents through Continuum under their own subscriptions. Continuum ships no LiteLLM adapter, and its BYOK sessions talk to the provider directly, so treat the pairing as your infrastructure decision.

The open-source proxy is free to self-host. Enterprise pricing is not published: the site offers a custom quote and a 30-day instant trial key. Budget for operating cost as well, since someone has to own the deployment.

The site claims 140+ LLM providers and 1,892 unique models. The GitHub README uses the narrower phrasing of 100+ LLM APIs, so check the provider table for the specific ones you need rather than trusting either headline.

Because the largest share of coding-agent spend at many teams runs on subscriptions, and that traffic never crosses a proxy. Continuum prices local agent logs by repo, provider, model, and day instead, so BYOK sessions are visible without routing them anywhere.

Yes, if you want one. Optional hosted inference is OpenAI-compatible at https://continuumcode.ai/v1 and Anthropic-compatible at the bare origin, using cont_sk_ keys with an auto model router. It is opt-in; the workbench works fully without it.

Begin

Run your agents
in Continuum.

Free app. Your subscriptions. Optional hosted inference. Mac stable - Windows and Linux desktop are beta.

Download for Mac See pricing

vendor-neutral · local-first · multi-device