What a LiteLLM implementation involves
A planned LiteLLM rollout covers five pieces of work: deploying the self-hosted proxy, provisioning PostgreSQL and Redis beside it, connecting approved providers and private models, creating teams and keys, and onboarding the first applications. PostgreSQL is required because virtual keys, budgets and spend tracking are stored there. Redis becomes necessary once more than one proxy instance runs, since it holds shared rate limits, router state and cache. Confirm both versions and sizing against the current LiteLLM production guide before you write the plan.
Organizations usually start this work for one of three reasons. Provider API keys are scattered across repositories, laptops and CI variables, with no owner for any of them. Finance cannot see spend by team or application. Or a data-residency rule requires that prompts and responses pass only through infrastructure the organization controls. Any one of these is enough to justify a scoped plan. Write down which trigger applies, because it decides which controls go into the first phase and which can wait.
Build in-house when your platform team already runs PostgreSQL, Redis and Kubernetes well, has someone who can own the gateway configuration, and can absorb a steady stream of releases. Bring in a partner when the first rollout must land across several provider accounts and applications within a fixed window, or when the team lacks Kubernetes or database operations depth. Either way, the decisions are the same: which providers and private models are approved, who owns each team and budget, and what must be in place before the first production key is issued.
What the implementation includes
A written scope for a self-hosted LiteLLM gateway usually breaks into the packages below. Each one ends in a named deliverable, and the exact boundaries are agreed in writing before work starts.
Proxy infrastructure and deployment
The LiteLLM proxy is deployed into your Kubernetes cluster or virtual machines, with image pinning, resource requests and a configuration repository you own. The deliverable is a running proxy and the deployment code that produced it. Check which image tag and release line the scope names, since LiteLLM publishes -stable tags and signs its Docker images with cosign.
PostgreSQL and Redis setup
PostgreSQL is the primary supported database and holds virtual keys, budgets and spend data. Redis becomes necessary once more than one proxy instance runs, for shared rate limits and router state. The deliverable covers both databases, their connection settings, backup configuration and a documented sizing basis. Confirm who operates the database service after handover.
Provider and private model connections
Each approved provider account, such as OpenAI, Anthropic, Gemini, Bedrock or Azure, and each private or self-hosted model endpoint is configured as a model entry with credentials held outside the config file. The deliverable is a tested model list and a record of which credentials belong to which account. Confirm which providers your procurement has approved before work starts.
Team and key structure at design level
The initial design maps teams, applications and environments to virtual keys, budgets, rate limits and model access lists. Detailed key issuance for every application is usually a later phase, so the scope should state how many teams are in the first design. The deliverable is a written key hierarchy and an access matrix that your owners sign off.
Kubernetes or virtual machine deployment
The deployment target is either a Kubernetes environment you control or VMs, and the choice drives manifests, load balancing and upgrade effort. LiteLLM recommends one Uvicorn worker per pod and scaling horizontally, which shapes the replica plan. The deliverable is the deployment manifests or playbooks, plus a short note on capacity assumptions. Confirm the cluster version and network policies in writing.
Logging and observability baseline
A baseline sets which request, error and spend events are emitted, where they go, and how long they are kept. It also decides which prompt content is logged, since prompts and keys should not end up in the wrong sinks. The deliverable is a logging configuration and a dashboard or alert set agreed with your security team. Confirm retention rules against your own policy.
Test report and handover documentation
Testing covers routing, fallback, budget enforcement and key restrictions against the agreed design, and results are recorded in a test report. Handover adds a runbook for restarts, secret rotation and database maintenance, plus the configuration repository. Ask whether the scope includes operator training and who signs off acceptance.
What drives cost and duration
This table lists the factors that most change LiteLLM rollout effort, how each one changes the work, and what to do to keep it contained.
| Driver | Why it changes the effort | How to keep it down |
|---|---|---|
| Provider accounts and private models | Each provider account needs its own credentials, model aliases, pricing checks and routing rules, and each private model needs endpoint testing and failover planning. | Start with the providers and models approved for the first wave, and add the rest in later phases after routing and budgets are tested. |
| Teams and applications moving onto the gateway | Every team needs virtual keys, budgets, rate limits and model restrictions, and every application needs a cutover and rollback step. | Migrate a small number of applications first, and use a shared key and budget template so each new team is configuration rather than design work. |
| Environments (development, staging, production, air-gapped) | Each environment needs its own deployment, secrets, database, Redis where required, and validation, and air-gapped sites need offline image and dependency handling. | Use one deployment template with environment-specific values, and confirm early whether an air-gapped site needs a separate image and update path. |
| PostgreSQL hosting choice | LiteLLM's production guide names PostgreSQL as the primary supported database, and virtual keys need it, so the hosting model affects backups, upgrades and high availability work. | Use the database platform your team already operates, and confirm backup, restore and major-version upgrade ownership before go-live. |
| Redis hosting choice | Redis 7.0 or newer is required once more than one proxy instance runs, for shared rate limits, router state and cache, so a single-instance pilot may defer it but production usually does not. | Decide on Redis at the start of design, and confirm the managed or self-hosted option, version and network path with your platform team. |
| Kubernetes versus VM deployment | LiteLLM recommends one Uvicorn worker per pod on Kubernetes and horizontal scaling, which adds manifests, ingress, secrets and autoscaling work. VM deployments need process management, load balancing and patching processes instead. | Choose the platform your organization already runs and operates well, and reuse existing ingress, secret and monitoring patterns. |
| Sizing of proxy workers | The production guide suggests 1 vCPU and 4 GiB memory per worker as a starting point, but real traffic, logging and guardrail load change the requirement, so sizing needs load testing. | Start from the suggested baseline, run a load test that reflects your expected traffic, and confirm final sizing in your own environment. |
| Compliance evidence requirements | Assessors may ask for key management records, access reviews, logging configuration, change history and backup tests, and each one adds collection and documentation work. | Agree the evidence list with your auditors before build starts, and design logging and change records to produce it by default. |
| Enterprise features in scope | SSO beyond five users, SCIM, JWT authentication, audit logs, team RBAC, IP allowlisting and external secret manager integrations require an Enterprise license and extra configuration and testing. | Confirm which governance requirements truly need Enterprise features, and request a written quote that separates those items from the open-source scope. |
Implementation phases and outputs
The rollout runs in eight ordered phases, each closing with a named output and a decision the client makes before the next phase starts.
Confirm scope, sources and constraints
DeployOpen and your team agree which model providers, private models, applications and identity sources are in scope, along with hosting boundaries and data rules. Your team supplies provider contracts, network constraints and named owners. The step closes with a written scope that sets the schedule and the quote, which depends on the drivers listed above.
Design architecture and size capacity
DeployOpen drafts the target architecture for proxy pods, PostgreSQL, Redis and the secret manager, with sizing based on expected request volume, concurrency and retention. Your team decides where the gateway runs and who operates it. The output is an approved architecture document with sizing assumptions you can challenge.
Build the environment and core services
DeployOpen provisions the PostgreSQL database, Redis, the LiteLLM proxy and the secret manager in your cloud account, data center or Kubernetes cluster, then configures health checks and the master key handling. You provide cluster access, accounts and secret manager entitlements. Done means a deployed proxy that starts cleanly, reaches its database and cache, and passes basic health checks.
Connect providers and private models
DeployOpen configures each approved provider and self-hosted model endpoint through the proxy, storing credentials in the secret manager and testing requests in the OpenAI-compatible format. You supply provider credentials, approved model lists and data terms. The output is a tested model inventory with aliases and routing defaults.
Design teams, keys and budgets
DeployOpen maps your organization into teams, users and virtual keys, with budgets, TPM and RPM limits and model restrictions agreed with your owners. You decide who approves budgets, who may request keys and how keys rotate. The output is a documented key model and a set of test keys that prove each restriction works.
Pilot with one application
DeployOpen onboards one selected application to the gateway, replacing its direct provider calls with the proxy endpoint and virtual key. Your application team runs its normal tests and reports spend and error patterns. The step closes when the pilot runs without unexplained failures and budgets report correctly.
Cut over production and hand over
DeployOpen moves the remaining agreed applications in stages, runs final verification of routing, logging and failover, and transfers runbooks, configuration and admin access to your operators. You confirm the cutover criteria and accept the handover in writing. The output is a production gateway, documented operations and a named support model.
Which parts teams keep and which they outsource
This table separates the LiteLLM rollout work that usually stays with the organization from the build work often handed to a partner, with the skills each part needs.
| Part of the implementation | Who usually owns it | Skills it needs |
|---|---|---|
| Business ownership of providers and models | In-house | Contract and procurement knowledge, LLM provider account management, data classification for approved models |
| Team, key and budget decisions | In-house | Governance design, finance and cost allocation, knowledge of which teams and applications will hold virtual keys |
| Application changes to call the gateway | In-house | Application development in the languages each product team uses, OpenAI-compatible client configuration, release coordination |
| Platform build and proxy configuration | Often outsourced | LiteLLM proxy configuration, model list design, container image handling and version pinning |
| Kubernetes manifests and deployment | Often outsourced | Kubernetes operations, Helm or manifest authoring, one-worker-per-pod patterns, horizontal scaling setup |
| PostgreSQL and Redis setup | Often outsourced | PostgreSQL administration for virtual key and spend data, Redis 7.0 or newer configuration for shared state |
| Secret manager wiring | Often outsourced | Cloud IAM, secret manager integration, handling of the master key and provider credentials |
| Testing and documentation | Shared | Test design for routing, budgets and key scopes, technical writing, runbook authoring |
What goes wrong and how to prevent it
Most LiteLLM gateway problems are visible in the plan long before go-live. Each pattern below has a specific check that should appear in the build and the acceptance criteria.
Single proxy without shared Redis
Running one proxy instance works for pilots, but once traffic grows and a second instance is added, rate limits and router state diverge across pods. LiteLLM's production guide requires Redis 7.0 or newer once more than one instance runs. Plan Redis from the start and test that a limit set on one instance is enforced on all of them.
Master key stored outside a secret manager
LITELLM_MASTER_KEY must start with sk- and grants administrative control of the gateway. If it lives in a values file, a shell history or a plain Kubernetes manifest, every operator with repository access can issue keys and change budgets. Plan retrieval from a secret manager at pod start, and check that the key never appears in version control or container logs.
Providers connected before key design
Adding provider credentials before the team and virtual key model is agreed leads to shared keys, unclear spend attribution and models exposed to teams that should not use them. Agree the team structure, per-key model restrictions and budgets first, then connect providers in that order. Verify that a test key is blocked from a model it should not reach.
Undersized database connection pool
PostgreSQL backs virtual keys, budgets and spend records, and connection exhaustion shows up as slow or failed requests under load. Size the connection limits per proxy instance against the database maximum, and confirm that worker count and pod count do not multiply past it. Load test with the planned pod count before go-live.
Unclear ownership of provider accounts
When no one owns the OpenAI, Anthropic, Azure or Bedrock accounts behind the gateway, quota changes, key rotation and billing disputes stall. Name an owner per provider account, record who can create and revoke its credentials, and set alert recipients for spend and quota thresholds before traffic is routed.
No staging environment
Routing, fallback and budget changes made directly in production can take down model access for every application at once. Run a staging gateway with the same configuration shape, test routing and cooldown behavior there, and promote configuration changes through the same path as code. Confirm that staging uses separate provider keys and a separate database.
Applications onboarded before logging is verified
Moving applications onto the gateway before spend tracking and request logging are confirmed means cost and usage data are missing or attributed to the wrong team from the start. Verify that each test key produces a spend record tied to the correct team, and that logs contain what your data handling policy allows, before onboarding any production application.
How the work changes by starting point
This is the most standard starting point. The proxy runs as a deployment on a cluster you already operate, and the build centers on how the stateful services are supplied.
- Database choice: decide early whether PostgreSQL is a managed service or self-run in the cluster. Virtual keys depend on PostgreSQL, so backup, restore and failover ownership must be settled before the first team receives a key.
- Redis: once more than one proxy instance runs, shared rate limits and router state need Redis 7.0 or newer. Confirm whether your platform team will operate it or whether a managed offering is preferred.
- Secrets: the master key must start with sk- and should sit in a secret manager, so the secret store integration is a first-week decision, not a later hardening task.
- Inputs to provide: cluster version and namespace policy, ingress and certificate approach, the chosen PostgreSQL and Redis model, and the list of provider accounts that will be connected.
Without Kubernetes, the gateway runs as a service on hosts or containers you already manage. The work shifts toward process supervision, rolling restarts and load balancing that a cluster would otherwise provide.
- Process model: plan one Uvicorn worker per instance and run several instances behind your load balancer. Decide how instances are started, restarted and replaced without a manual step.
- Health checks and routing: the load balancer must remove unhealthy instances, so define the health endpoint and the failure behavior before go-live.
- Configuration delivery: choose how configuration and secrets reach each host, since copying files between hosts is the most common source of drift.
- Inputs to provide: host operating system and container runtime, service manager or orchestrator in use, load balancer type, and who patches the hosts and on what schedule.
When provider calls cannot leave your network, the gateway must reach approved private endpoints rather than public provider APIs. Connectivity and image supply become the first decisions, not the last.
- Provider endpoints: list each approved private endpoint and confirm which provider features it exposes. Do not assume a feature available publicly is available privately, and confirm this with the provider.
- Image and package supply: container images, Python packages and model metadata must be mirrored into your registry. Confirm the signature verification approach with your security team before mirroring.
- Egress rules: document every outbound destination the gateway needs, including telemetry or license checks you choose to keep enabled, and get them approved before deployment.
- Inputs to provide: the private endpoint inventory, registry and mirror location, egress policy owner, and the change process for updating mirrored artifacts.
When several business units need their own provider accounts and spending limits, the structure of teams and keys becomes the main design task. The technical build is similar, but ownership and reporting decisions move to the start.
- Team model: agree how business units map to LiteLLM teams, and who may create keys and budgets for each team before the first deployment.
- Provider accounts: decide whether each unit has its own provider credentials stored per team or whether accounts are shared, since this changes blast radius and billing attribution.
- Access and identity: define how administrators from each unit sign in and what they can see. Confirm which role separation features your edition supports, since some controls sit in the Enterprise edition.
- Inputs to provide: the list of business units, named budget owners, provider contract boundaries, and the reporting cadence each unit expects for spend.
What the client needs to provide
The implementation cannot start design work until a named owner exists for the gateway and each input below is either decided or has a date by which it will be. Name one accountable platform owner, one security approver, and one finance contact for budgets. Provider accounts and contracts belong to the client, so the client must confirm which accounts exist for OpenAI, Anthropic, Gemini, Bedrock or Azure, who holds the billing relationship, and whether enterprise terms cover the data handling the business requires.
Several technical decisions shape the build and should be made during scoping. Confirm the approved model and provider list, the Kubernetes cluster or virtual machines that will host the proxy, the DNS names and network paths from applications to the gateway, and any egress rules to provider endpoints or private model servers. Decide where PostgreSQL and Redis will be hosted, whether managed or self-run, and which secret manager will hold LITELLM_MASTER_KEY and provider credentials. Redis is needed once more than one proxy instance runs, so the team should not treat it as optional for a production design.
Identity and approval contacts also need names. Identify who approves admin UI sign-in, who assigns teams and budgets, and who signs off on the first applications and teams in scope. If a decision is pending, the usual result is a delay in the first deployment, not a lower cost, because the work for those items is held rather than removed. Ask the DeployOpen team to record open decisions in the written scope with an owner and a due date, so that unresolved items are visible to procurement and to the steering group.
At the start of scoping, confirm which LiteLLM release line the client will run. Check the current stable release tag on the BerriAI/litellm releases page and confirm the supported minor version policy in the vendor's enterprise documentation. The plan should name the version it targets, because the migration and upgrade work that follows depends on that choice.
Frequently asked questions
What drives the cost of a LiteLLM implementation?
The biggest drivers are how many providers and private models you connect, how many teams and applications need keys, and how much of the existing traffic must move onto the gateway. Cost also rises with the production footprint: a PostgreSQL database for virtual keys and spend, Redis once more than one proxy instance runs, Kubernetes environments to build for, and the identity and logging integrations your security team requires. Ask for a quote that lists each of these as a separate line so you can see what is one-time work and what recurs.
Can DeployOpen implement LiteLLM on a fixed-scope basis?
Yes. Most DeployOpen engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts. A written scope for LiteLLM should name the providers and private models in scope, the teams and keys to issue, the environments and the applications to move onto the gateway. Anything outside that list, such as additional providers or custom guardrails, is usually quoted as a change to the scope. Ongoing support and upgrades are available as a separate retainer.
How long does a LiteLLM rollout take?
Duration depends on the number of providers, the number of applications to migrate and how many environments you run, so no single timeline applies. The work usually runs in phases: design of the architecture and key model, build and configuration, testing of routing and budgets, a staged cutover of applications, and handover. Ask for a timeline tied to each phase in the written scope, and confirm which items depend on your own approvals, such as provider account access or network changes.
Can our own engineering team build a LiteLLM gateway without a partner?
Yes, a capable platform team can deploy LiteLLM, since it is open source under the MIT license for the code outside the enterprise directory. The harder parts are the decisions that shape production: database and Redis sizing, key and budget design, routing and failover behavior, secret handling for the master key, and a migration plan for existing applications. Assess whether your team has time for those decisions and for ongoing upgrades within the four supported minor versions.
What must our team provide during the engagement?
Your team typically provides access to the cloud accounts, data centers or clusters where the gateway will run, provider accounts and credentials handling rules, the identity provider settings for admin sign-in, and named owners for each team that will receive keys. You also need an approver for the key model, a contact for each application being migrated and a test window for the cutover. List these in the written scope so delays from missing access are visible early.
Do we need LiteLLM Enterprise for the rollout?
Not for every rollout. The core gateway, virtual keys, budgets and routing are available in the open-source edition. SSO for the admin UI is free for up to 5 users from v1.76.0, and an Enterprise license is needed beyond that. Enterprise also adds SCIM, JWT authentication, audit logs, organization and team RBAC, IP allowlisting and secret manager integrations. Decide which of these your controls require before the design phase, and confirm current licensing terms with LiteLLM.
What does the handover include?
A typical handover covers the deployed architecture, configuration of the proxy, database and Redis, the key and team structure, the routing and budget settings, runbooks for restarts, key rotation and provider changes, and the upgrade procedure. Ask for documentation of the tests run against routing, budgets and failover, and confirm whether the written scope includes training or a support period. Optional support after go-live is available as a retainer.
How DeployOpen scopes, delivers and hands over LiteLLM work
DeployOpen plans, implements and hands over self-hosted LiteLLM gateways inside your own cloud accounts, data centers or air-gapped networks. The work is scoped in writing before the build starts, so the cost drivers are visible to you first.
How we scope
- The written scope names the providers and private models to connect and the teams that receive keys.
- Engagements are fixed-scope, so deliverables, timelines and responsibilities are agreed in writing before work starts.
- Ask for the Kubernetes or hosting environments, PostgreSQL and Redis topology, and the data classes each route may carry.
How we build
- Stand up the proxy, PostgreSQL and Redis in your environment, with one Uvicorn worker per pod on Kubernetes.
- Connect each provider and private model, then test routing, retries and budgets before any application traffic arrives.
- Keep the master key in a secret manager and verify published image signatures before deployment.
How we hand over
- Hand over runbooks for key issuance, budget changes, model onboarding and proxy restarts, written for your operators.
- Deliver configuration in version control with the deployment manifests, so changes can be reviewed and reproduced.
- Walk your team through the app migration status, open items and the cost-driving settings agreed in the scope.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers, scoped separately from the build.
- Ask whether the retainer covers upgrades to new -stable releases and the four supported minor versions.
- Confirm with LiteLLM which Enterprise support terms apply before you agree on response expectations.
