What a Langfuse implementation costs and how long it takes
The cost and duration of a Langfuse implementation depend on the written scope, the deployment target, trace volume and the number of applications to instrument. Those four inputs shape almost every other line of the estimate, so no reliable figure exists until they are fixed. Teams usually start this work when LLM tracing, prompt management and evaluations must stay inside their own infrastructure. Common triggers include trace payloads that contain customer data, a security review that requires data to remain in the company's accounts, or an existing observability tool that no longer fits the organization's data handling rules.
The software itself carries no license fee. Langfuse states that all core features and APIs are available in the MIT-licensed open source version without limits, and self-hosted deployments use the same codebase as Langfuse Cloud. The cost sits in three places: infrastructure, engineering and internal time. Infrastructure recurs every month and covers Langfuse Web, Langfuse Worker, PostgreSQL, ClickHouse, Redis or Valkey, and S3-compatible blob storage. Langfuse's scaling guide lists minimum resources of 2 CPU and 4 GiB for web, worker and PostgreSQL, 1 CPU and 1.5 GiB for Redis or Valkey, and 2 CPU and 8 GiB for ClickHouse, with at least 16 GiB of ClickHouse memory for larger deployments. These are minimums, so sizing for your trace volume is a separate step to confirm before you buy capacity.
Engineering effort is mostly one-time: deployment with Helm or Terraform in your cloud, SSO, network controls, backups and instrumentation of each application. Instrumentation often takes more effort than teams expect, because each service needs consistent trace structure, user and session attributes and a agreed approach to scores. Ask any partner to price the work per application or per environment, and state whether test and production are both in scope. Recurring engineering work is upgrades. Within a major version, database migrations run automatically at startup. Major upgrades, such as the v3 to v4 path, require ClickHouse 25.12 or later (26.4 recommended), PostgreSQL 15 or later and Redis 7.0 or later, plus about 3x ClickHouse disk headroom when backfilling historic data. Budget for that work and disk, not only the first install.
Duration is set by the written scope and by dependencies outside the project. Ask for a phase-by-phase schedule that shows when the cloud accounts, data stores and DNS entries will be ready, and when your security and change approval steps fall. If you need project-level roles, protected prompt labels, data retention policies, audit logs or SCIM, the Self-Hosted Enterprise license key is required. Langfuse lists that plan with custom pricing, so confirm whether the key and its support terms appear in the quote. In-house delivery suits teams that already run Kubernetes, ClickHouse and PostgreSQL in production and can staff upgrades. A partner shortens the timeline when those skills or people are thin. Ask every quote to itemize the scope, the volume assumptions, the environments, the exclusions, the handover criteria and the support options after go-live.
What a Langfuse implementation includes and delivers
A self-hosted Langfuse implementation bundles several infrastructure components with application changes, and each one ends in something the client keeps. The cards below list the work in the order it usually happens, with the deliverable the client receives for each item so the cost of every component can be checked against a concrete output.
Architecture and sizing
Before any build, the work fixes the topology: which Langfuse Web and Langfuse Worker replicas run, how PostgreSQL, ClickHouse, Redis or Valkey and S3-compatible storage are hosted, and what trace volume each component must absorb. Sizing draws on Langfuse's published minimums and scaling guidance, then on the client's expected ingestion. The deliverable is a sizing record with the assumptions written down, so the client can see what changes if volume grows.
Deployment of core services
This covers installing Langfuse Web, Langfuse Worker, PostgreSQL, ClickHouse, Redis or Valkey and blob storage in the client's own cloud account, data center or air-gapped network. Langfuse documents Docker Compose for testing and Kubernetes with Helm or Terraform for production, and the choice between them drives much of the effort. The deliverable is a deployed environment with versions pinned and health checks passing.
Identity and network configuration
This work sets how users sign in, which network paths reach the web tier, and how the data stores are reachable only from inside the cluster or network. Langfuse supports SSO through several identity providers and can enforce SSO for given email domains, so the identity provider and domain list must be confirmed early. The deliverable is a configuration record covering sign-in method, ingress rules and secrets handling.
Application instrumentation
Traces only have value once applications send them, so this card covers wiring the client's LLM applications to Langfuse through native SDKs or OpenTelemetry over OTLP. The effort depends on how many services exist, which frameworks they use, and whether user and session attributes must be propagated. The deliverable is instrumented applications with traces visible in the environment and a short note on the chosen integration method for each service.
Backups and tested restores
Each data store needs a backup method that matches its role, and a backup that has never been restored is not a control. This work sets backup schedules for PostgreSQL, ClickHouse and blob storage, then runs restore tests into a separate environment. Restore timings and the data checked during the test should be confirmed with the provider before go-live. The deliverable is a backup configuration plus a restore test report.
Documentation and handover
The final card covers the material the client's operators need after the team leaves: a runbook for routine operations, upgrade steps, the sizing record, configuration records and the known limits of the setup. Upgrade paths matter here, since Langfuse applies database migrations at startup within a major version and major upgrades follow separate guides. The deliverable is a runbook and handover session with named owners for each operational task.
What drives Langfuse implementation cost and duration
This table lists the factors that move effort and cost in a self-hosted Langfuse rollout, why each one matters, and one way to keep it contained. Read each row as a question to settle in scoping, because most of these drivers are fixed once the architecture and access model are agreed.
| Driver | Why it moves effort | How to keep it contained |
|---|---|---|
| Trace volume and ingestion rate | Higher volume drives ClickHouse disk and memory sizing, worker scaling and backfill planning, and may require splitting langfuse-web into separate ingestion and UI deployments. | Agree expected trace volume per day and peak rate in writing, and load test a representative workload before go-live. |
| Number of applications and frameworks | Each application needs instrumentation, naming conventions and review of which user and session attributes are sent, and OTEL-based frameworks add mapping work. | Start with a short list of priority applications and standardize one instrumentation pattern before onboarding the rest. |
| Deployment target | A single VM Docker Compose test has no high availability, while Kubernetes production needs Helm or Terraform work, ingress, storage classes and scaling policies on the chosen cloud. | Use the single VM setup for a bounded evaluation only, and fix the production target platform before design starts. |
| Number of environments | Each environment needs its own data stores, secrets, SSO configuration and upgrade path, so effort repeats with every added environment. | Keep non-production environments on a shared pattern defined once in code and promote the same configuration to production. |
| Identity integration | Connecting an identity provider such as Okta, Entra ID or Keycloak involves redirect configuration, domain enforcement and testing of sign-in and disabled password access. | Confirm the identity provider and its OIDC settings early, and decide whether SCIM and project-level roles are needed. |
| Data residency or air-gapped constraints | Restricted networks require internal registries, mirrored images, private blob storage and documented data flows, which lengthens design and verification. | Confirm residency and network rules with your security team before selecting hosting, and list every external dependency that must be removed. |
| Retention and masking requirements | Retention policies and server-side data masking are Enterprise license features, and masking rules need testing against real trace shapes and prompts. | Define retention periods and sensitive fields in a written policy, then limit the first release to the features that policy requires. |
| Enterprise license features in scope | Features such as project-level RBAC, audit logs, data retention, server-side masking and SCIM require an Enterprise license key and a procurement step, which adds coordination time. | List the required Enterprise features during scoping and confirm current licensing terms directly with Langfuse before the plan is finalized. |
Choosing the deployment target before you price the work
The deployment target decides most of the scope, so settle it before anyone estimates effort. Langfuse documents two patterns. Docker Compose on a single virtual machine is documented for testing and has no high availability. Kubernetes, deployed with Helm or with Terraform-based configurations on AWS, Azure or GCP, is the documented production path. A team that prices a Compose trial and then runs production on Kubernetes will find that most of the work was never costed.
The target changes the networking, secrets, storage and scaling work. On Kubernetes you define ingress, TLS termination, network policies between the application containers and the data stores, and how the web and worker pods reach PostgreSQL, ClickHouse, Redis or Valkey and S3-compatible blob storage. Secrets such as database credentials, the encryption key and the blob storage keys move into your secret manager or Kubernetes secrets. Persistent volumes need storage classes that match the performance and retention you chose for ClickHouse. Each of these is a line item with its own owner and review.
Scaling is also target-specific. Langfuse recommends scaling worker containers on CPU load. Under heavy ingestion it recommends splitting langfuse-web into separate ingestion and user interface deployments, so trace writes do not compete with people working in the UI. Confirm the current autoscaling settings and thresholds in the Langfuse scaling guide, and ask your platform team how they will observe CPU load and queue lag, because those signals determine when the split is worth the extra deployment.
The scaling guide gives minimum resources of 2 CPU and 4 GiB for web, worker and PostgreSQL, 1 CPU and 1.5 GiB for Redis or Valkey, and 2 CPU and 8 GiB for ClickHouse, with at least 16 GiB of ClickHouse memory for larger deployments. Treat these as a floor for the smallest viable setup, not as a sizing answer. Your trace volume, retention period and query load determine the real figures. Confirm the current numbers in the scaling guide before you write them into a quote or an infrastructure request.
Ask the implementation partner to write the target decision into the statement of work: the cluster or VM platform, the cloud or data center, the ingress and identity boundary, the secrets approach, the storage classes and the scaling design. Each choice then maps to a deliverable and a verification step. If your organization has no production Kubernetes platform yet, that gap belongs in the plan as a separate workstream with its own cost.
Langfuse implementation phases and the output of each
The sequence moves from an agreed scope to a tested, instrumented and documented Langfuse instance in your infrastructure. Each phase closes with a named output, so your team can check progress before the next phase starts. The schedule is set in the written scope and depends on the factors in the effort drivers.
Discover and scope the deployment
DeployOpen and your team identify the applications that will send traces, the expected trace volume, the sensitivity of prompts and trace data, the identity provider, the hosting target and whether any features listed in the Enterprise license key documentation are required. Your security and platform leads confirm these answers. Output: a written scope document covering deliverables, responsibilities, assumptions and the schedule. Work does not start until that scope is agreed in writing.
Design architecture and size components
DeployOpen maps the target architecture: the Langfuse Web and Worker containers, PostgreSQL, ClickHouse, Redis or Valkey, and S3-compatible blob storage. Each component is sized against the expected ingestion volume, using the minimum resources in Langfuse's scaling guide as a starting floor. The team also decides whether langfuse-web should be split into separate ingestion and user interface deployments. Output: an architecture decision record and a sizing sheet, reviewed and approved by your platform and security teams before infrastructure is built.
Build test then production environments
The test environment comes first. Langfuse documents Docker Compose on a single VM for testing, which has no high availability, so it suits functional checks only. The production environment is then built on Kubernetes using Helm or Terraform in your cloud accounts or data centers, inside your network controls and secret management. Output: two working environments provisioned as infrastructure as code, configuration kept in version control, secrets held in your secret store and a deployment runbook your team can operate.
Configure identity and access
The instance is connected to your identity provider using one of the supported SSO providers, such as Entra ID, Okta or a generic OIDC provider. SSO can be enforced for specific email domains and password sign-in can be disabled. Before relying on project-level roles or SCIM, the team confirms which of these need an Enterprise license key. Output: a tested SSO login for test users, a documented role model and an access matrix showing who can administer the instance and each project.
Instrument applications for tracing
Each in-scope application is instrumented using either a Langfuse native SDK or OpenTelemetry exporting to Langfuse's OTLP endpoint, which suits frameworks such as LangChain or LlamaIndex. The team agrees how users, sessions and environments are named so traces can be searched and filtered. Output: pilot services sending traces to the test environment, and a short convention document covering the attributes, tags and naming that developers apply across services.
Validate load and ingestion
Representative trace volume is replayed against the test environment. The team watches worker CPU, ingestion queue lag, and the resource use of PostgreSQL and ClickHouse, and checks that every trace arrives complete. Worker scaling and resource limits are tuned from the results. Output: a load test report with measured ingestion behavior, the scaling settings that met the agreed target, and alert thresholds for when ingestion falls behind.
Test backup and restore
Backups are configured for PostgreSQL, ClickHouse and blob storage. The team then performs a full restore into a separate environment and verifies that traces, prompts and projects come back intact. The recovery steps and the people responsible for them are recorded. Output: the backup configuration, a restore test record with the observed results and a recovery runbook that your operations team can follow without the build team present.
Go live and hand over
Production traffic is moved to the instance only after the earlier checks pass. Monitoring and alerts are confirmed as active, and operational knowledge is transferred to your team through walkthroughs and written documentation. Output: a go-live checklist signed off by your team, as-built documentation, handover session records and a support arrangement agreed in writing. Planning for later major version upgrades is handled separately from this implementation.
Which parts of a Langfuse rollout your team keeps and which a partner takes
This table compares, part by part, the skills an implementation needs and whether each piece is usually kept in-house or handed to a partner during the rollout. Read the last column as the work your internal team must own once Langfuse is live, because that ownership continues long after go-live.
| Part of the rollout | Skills needed | Usually kept internal or outsourced | Owned by your team after go-live |
|---|---|---|---|
| Platform engineering for Kubernetes and data stores | Kubernetes, Helm or Terraform, cloud networking, storage classes, secrets management | Often outsourced for the build, with internal review of the cluster design | Cluster upgrades, node capacity, Helm values and rollback procedures |
| Database and ClickHouse administration | PostgreSQL and ClickHouse operations, memory and disk planning, backup and restore testing | Build often outsourced; the sizing decisions should be signed off internally | Disk and memory headroom, backup verification, version upgrades and backfill planning |
| Identity integration | OIDC or SAML configuration, identity provider administration, email domain policy | Usually outsourced for configuration, with internal identity admins approving the setup | Group changes, SSO enforcement settings and access reviews |
| Application instrumentation across teams | Python or JavaScript SDK use, OpenTelemetry concepts, trace attribute conventions | Split: a partner can set the pattern, while each product team usually instruments its own services | Instrumentation standards, SDK version tracking and new service onboarding |
| Security review of prompts and traces | Data classification, privacy review, masking and retention design, audit evidence | Usually kept internal, because the data policy belongs to the organization | Retention settings, masking rules, access reviews and evidence for assessors |
| Ongoing operation and upgrades | Monitoring, incident response, reading upgrade guides, testing in a non-production environment | Kept internal unless a support retainer is agreed in writing | Monitoring ingestion lag, scheduled upgrades, on-call and vendor support requests |
What goes wrong in self-hosted Langfuse projects and how to prevent it
Most self-hosted Langfuse problems start as planning gaps rather than software faults. The cards below cover the failure modes we see most often in data sizing, recovery, deployment, instrumentation, network exposure and access control, each paired with a prevention step tied to the implementation plan.
Underestimating ClickHouse memory and disk
ClickHouse holds most of the trace data and is the component most often undersized. Langfuse's scaling guide lists 2 CPU and 8 GiB as minimums and at least 16 GiB of memory for larger deployments, and its v3 to v4 guide advises about 3x disk headroom when backfilling historic data. Prevention: in the sizing phase, estimate trace volume per day with your team, then set memory, disk and backfill headroom in writing before procurement.
Skipping backup restore tests
Teams often configure backups for PostgreSQL and ClickHouse but never restore them. A backup that has not been restored into a separate environment cannot be relied on for recovery, and the two stores must be restored to a consistent point to make sense together. Prevention: include a timed restore test for both PostgreSQL and ClickHouse in the operations phase, and record the procedure and the owner in the runbook.
Running the test Compose setup in production
Langfuse documents Docker Compose on a single VM for testing, and that setup has no high availability. Moving it into production without redesign leaves a single point of failure for tracing and the stores beneath it. Prevention: decide the production target (Kubernetes with Helm or Terraform on AWS, Azure or GCP, or your own equivalent) during design, and treat the Compose file as a local test artifact only.
Instrumenting apps without shared attributes
When each team names users and sessions differently, traces cannot be filtered, scored or compared across applications, and the cost of fixing this grows with every service instrumented. Prevention: agree on user and session attribute names, value formats and the data allowed in them during the instrumentation phase, then add them to a shared SDK wrapper or OpenTelemetry configuration before rollout.
Exposing the UI without network controls
The Langfuse UI and API hold prompts and traces that may contain sensitive data, and a public endpoint without restriction widens the exposure. Prevention: place the web service behind an ingress or load balancer with network allow-listing or private access, confirm the ingestion path that applications use, and document which networks may reach the UI and API in the deployment design.
Leaving SSO enforcement until later
Shipping with local accounts and planning to add SSO after go-live tends to leave password sign-in in place for longer than intended, and users may keep those accounts. Prevention: configure your identity provider and SSO connection during the deployment phase, decide which email domains must sign in through SSO, and disable password sign-in before onboarding real users. Confirm which controls need an Enterprise license key.
How the Langfuse implementation changes by starting point
When the organization has no cluster for Langfuse yet, the implementation covers the cluster build alongside the application stack. Langfuse documents Helm and Terraform-based deployments on AWS, Azure and GCP for production, so the first decisions concern the cloud account structure, network layout and managed or self-operated data stores. The scope grows because the team must also decide how secrets, ingress and certificates are handled from day one.
The work usually includes:
- Cloud foundation: accounts or subscriptions, VPC or virtual network design, cluster provisioning and node pools sized for Langfuse Web, Worker and the data stores.
- Data stores: PostgreSQL, ClickHouse, Redis or Valkey, and S3-compatible blob storage, each with a chosen hosting model and backup approach.
- Ingress and identity: domain, TLS, and SSO configuration with your identity provider.
- Provisioning and access: who runs Terraform or Helm and who holds cloud credentials.
Your cloud team must provide accounts, networking approvals, and budget owners. Confirm with your cloud provider which managed services are available in your region and which quotas apply before the build starts. Check first that node sizing and the ClickHouse memory plan were agreed in writing, because they are the hardest items to change after data arrives.
When an internal platform team already runs Kubernetes, the work narrows to fitting Langfuse into existing standards. The team usually brings its own ingress controller, secrets management, observability and GitOps pipeline, so the implementation adapts the Helm or Terraform deployment to those conventions instead of building a new foundation. Most of the effort moves into namespace design, resource quotas and access policies.
The work usually includes:
- Standards fit: mapping Langfuse onto existing storage classes, ingress, certificate issuance and secret stores.
- Capacity review: confirming that the cluster has room for the web, worker and data store resources described in Langfuse's scaling guide.
- Pipeline integration: adding Langfuse manifests or Helm releases to the platform team's GitOps repository and change process.
- Data store decisions: whether PostgreSQL, ClickHouse and Redis run on the platform or on managed services.
Your platform team must provide cluster access, change approvals, and a named owner for upgrades. Confirm with the platform team whether a shared cluster can meet ClickHouse disk and memory needs, since noisy neighbors can affect ingestion. Check first that the data store backup routine is wired into the platform's existing backup tooling and tested with a restore.
A single VM running Docker Compose is the documented route for testing Langfuse, and it carries no high availability. Teams often start here to prove value with a few instrumented applications, then promote the same codebase toward production. The starting point is fast to stand up, but the promotion step is where most of the rework sits, because the VM was never designed for durability, separation of traffic or recovery.
The work usually includes:
- Scope of the proof: agreeing which applications send traces and what data may enter the test instance.
- Promotion plan: deciding which components move to Kubernetes or managed services, and how data from the test instance is kept or discarded.
- Hardening gap review: listing what the VM lacks, such as backups, TLS, restricted network access and SSO.
- Sizing reset: re-running the resource estimates from Langfuse's scaling guide for production volumes.
Your team must provide the VM, a decision on whether test data is retained, and an owner for the promotion. Confirm with your security team whether test traces containing prompts or user data can remain on the VM during the proof. Check first that the production build is not simply the proof VM copied forward, since single-node settings rarely match production load or recovery needs.
When the network cannot reach public registries or cloud blob services, the implementation must provide every dependency inside the boundary. Langfuse needs S3-compatible blob storage and container images, so the team must mirror images, Helm charts and any package sources to an internal registry, and supply a storage service that speaks the S3 interface. Upgrades depend on this mirror as much as the initial install.
The work usually includes:
- Image and chart mirroring: copying Langfuse Web, Worker, data store images and charts into an internal registry, with a process for each new release.
- Internal blob storage: providing an S3-compatible service and validating that Langfuse can read and write to it.
- Offline data stores: installing PostgreSQL, ClickHouse and Redis or Valkey from internal sources.
- Licensing and SSO: confirming how the Enterprise license key is applied and how the identity provider is reached from inside the network.
Your infrastructure and security teams must provide the internal registry, storage platform, and approval for transferring software into the enclave. Confirm with your security team how media transfer and vulnerability scanning of mirrored images will work. Check first that every upgrade path, including the v3 to v4 requirements for ClickHouse and PostgreSQL versions, can be satisfied from internal sources before the first deployment.
What your team must provide before a Langfuse implementation starts
The most common reasons a self-hosted Langfuse project slips are not technical. They are missing owners and data decisions nobody has made yet. Before the implementation starts, your team needs to supply a defined set of access, infrastructure details and decisions, and to name the people who will approve them. Treat this list as a gating checklist: if an item is open, the schedule in the fixed-scope plan cannot start on time. Access comes first. DeployOpen needs access to your cloud accounts, data center or air-gapped network, granted through least-privilege roles scoped to the Langfuse environments only. Confirm with your cloud or infrastructure team which roles can create compute, networking, databases and storage, and which actions require a change ticket. Agree in advance how secrets will be delivered, since the deployment needs database credentials, the Langfuse encryption and salt values, and the S3 access keys.
The platform itself needs a home. Identify the target Kubernetes cluster or the virtual machine that will run the Docker Compose test setup, and confirm its capacity and network policies with the platform team. For production, you also need S3-compatible storage details (endpoint, bucket names, region, and the access model), a DNS name for the Langfuse URL, and a TLS certificate issued by your internal or public certificate authority. Ask your certificate team how long issuance takes, because it often sits on the critical path. For identity, provide the details of the identity provider that will sign users in: the provider type, the OIDC issuer and client registration details, and the email domains that should be forced onto SSO. Also provide an inventory of the applications and frameworks to instrument, such as LangChain, LlamaIndex or custom services, with the owning team for each. Pair it with a trace volume estimate per application, covering expected request rates and how much payload each trace carries. Without this estimate, sizing of ClickHouse and PostgreSQL cannot be defended. Two decisions carry the most risk. The first is data classification: state which prompts, completions and user identifiers may appear in traces, and which of them must be masked or excluded. The second is retention: set how long traces and blob data must be kept, and who signs off on deleting them. Finally, name one owner each for approvals, security review and on-call. Confirm these names in writing before kickoff, and confirm the escalation path with the owners' managers, because unnamed or unavailable owners are the usual source of delay.
How long does a Langfuse implementation take and what does it cost?
Does self-hosting Langfuse require a paid license?
No. The core Langfuse platform is MIT Expat licensed, and Langfuse states that all core features and APIs are available in the open source version without limits. The exception is code under the ee/, web/src/ee/ and worker/src/ee/ directories, which falls under a separate enterprise license. Some features in self-hosted deployments need an Enterprise license key, including project-level RBAC roles, protected prompt labels, data retention policies, audit logs, server-side data masking, UI customization, and SCIM with the org management API. Confirm the exact feature list against Langfuse's license key documentation before you finalize scope.
Do we need the Enterprise license to use SSO with Langfuse?
Not necessarily. Self-hosted Langfuse supports SSO through Auth.js providers such as Okta, Entra ID, Keycloak, Auth0 and generic OIDC, and it can disable password sign-in or enforce SSO for specific email domains. Whether SSO alone needs a key depends on the Langfuse version and provider you choose, so confirm that in the current docs. What clearly needs an Enterprise key is project-level RBAC and SCIM provisioning, so if you need role separation by project or automated user lifecycle, budget for the license.
What drives the total cost of a self-hosted Langfuse implementation?
Cost has three main parts: the infrastructure you run, the people who build and operate it, and any license or support you choose. Infrastructure depends on trace volume, which sets ClickHouse, PostgreSQL, Redis or Valkey and blob storage sizing, plus whether you run Kubernetes, Docker Compose on a single VM for testing, or a production cluster on AWS, Azure or GCP. Labor depends on the number of environments, the number of applications you instrument, and whether you need SSO and backups built in. DeployOpen does not publish a price list. Your quote follows from a written scope, so ask for line items by phase and by recurring cost.
Can our own team implement Langfuse without a partner?
Yes, if you have engineers comfortable with Kubernetes or Docker, managed databases, object storage and observability tooling. Langfuse publishes self-hosting, scaling and upgrade guides, so a capable team can follow them. The effort usually sits in the parts the guides leave to your environment: sizing ClickHouse for your ingestion, wiring identity, network exposure, backups and restore testing, and instrumenting applications consistently. A partner is useful when you want those decisions made and documented against a fixed scope, or when internal capacity is limited.
How much internal time should we budget for a Langfuse rollout?
Plan for time from platform engineering, security, and the teams that own the instrumented applications. Platform engineering carries deployment, database sizing, backups and upgrades. Security reviews identity, network paths, secrets and data handling. Application teams add tracing to their code and agree on naming for users, sessions and scores. We have not been supplied with a duration figure for this work, so ask for an effort estimate in the written scope and check it against your own capacity and the number of environments.
What do we need to provide before implementation starts?
Expect to provide cloud accounts or data center access, network design for ingress and private database connectivity, a DNS name and TLS certificates, identity provider details if you use SSO, and a decision on the Langfuse edition. You also need your expected trace volume and retention needs, which determine ClickHouse and storage sizing, and a named owner for approvals. Ask the partner which of these inputs must be ready before work begins, since missing inputs are a common cause of schedule slips.
Which costs are one-time and which recur after go-live?
One-time costs include scoping, initial deployment, integration of identity and instrumentation, and acceptance testing. Recurring costs include the database and storage infrastructure, backups, monitoring, the Enterprise license or support plan if you buy one, and staff time for upgrades. Upgrades matter because Langfuse applies database migrations automatically within a major version, while major upgrades such as v3 to v4 need dedicated migration work and extra ClickHouse disk headroom. Ask for the recurring items to be listed separately in the quote.
How DeployOpen scopes, delivers and hands over a Langfuse implementation
DeployOpen plans, implements, hardens, backs up, documents and supports self-hosted Langfuse inside your own cloud accounts, data centers or air-gapped networks. Engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts, and ongoing support is available as a retainer.
How we scope
- The written scope names the target platform, Docker Compose for testing or Kubernetes for production.
- Trace volume and sizing inputs for PostgreSQL, ClickHouse, Redis or Valkey and blob storage are captured before any build begins.
- Application instrumentation, SSO scope and whether Enterprise license features are in or out are listed as explicit deliverables.
How we build
- Build covers the Langfuse Web and Worker containers, PostgreSQL, ClickHouse, Redis or Valkey, and S3-compatible blob storage.
- Resource sizing follows Langfuse's scaling guidance, and sizing choices are documented with the reasons behind them.
- Testing includes ingestion from instrumented applications, SSO sign-in and a backup restore before go-live.
How we hand over
- Handover includes runbooks for deploy, upgrade, backup and restore, and the ClickHouse operating signals your team watches.
- Deployment code, configuration and secret locations are delivered into your repositories and accounts.
- Your team receives a walkthrough of the architecture and the decisions recorded during scoping and build.
How we support
- Ongoing support is available as a retainer, covering upgrades and changes to the deployment after go-live.
- Major version upgrades, such as v3 to v4, are planned as separate work with their own written scope.
- Ask for a quote that separates one-time build from recurring support, so both costs are visible before you commit.
