How do you keep Langfuse high availability when tracing is in the critical path?
Langfuse high availability means running several Langfuse Web and Langfuse Worker replicas, separating ingestion traffic from user interface traffic, and making the stateful components that hold trace data survive a single failure: PostgreSQL, Redis or Valkey, ClickHouse and S3-compatible blob storage. The platform team's concern is concrete. Once LLM applications send traces to a self-hosted Langfuse, a gap in ingestion means traces are missing for the same incident engineers are trying to investigate. The availability target should be set by the engineers who rely on those traces during an outage, not by the uptime of the UI alone.
The Docker Compose path on a single VM is documented for testing and does not provide high availability. Langfuse's production guidance points to Kubernetes, deployed with Helm or Terraform on AWS, Azure or GCP. Choosing that path is a platform decision. Confirm which cluster, ingress controller and load balancer your organization already operates, who owns the Helm values and Terraform state, and whether each data store will run inside the cluster or as a managed service. That choice decides who patches, fails over and restores each component, and it determines how much of the availability design your team owns.
Adding replicas is the easier part. Web and worker pods are largely stateless, so a lost pod is replaced and its work resumes. The harder question is how each stateful store behaves when a node or zone fails. For PostgreSQL, confirm whether you will run a primary with a standby and automated failover, and how the application reconnects afterward. For Redis or Valkey, confirm whether the queued work it holds can be lost during a failover and what Langfuse does with work in flight. For ClickHouse, confirm the replication and coordination topology. For blob storage, confirm the durability and versioning your provider offers. Ask the vendor or your provider for written answers to each of these before you size anything.
A single-replica install can look healthy in every dashboard while it cannot absorb a node loss. The UI loads, test traces arrive and health checks pass, yet one drained node stops ingestion until a replacement pod schedules and connects. Test the failure, not the steady state. Before go-live, drain a node that runs both a web pod and a worker pod in a non-production environment, stop the primary PostgreSQL instance, and record how long ingestion takes to recover. Also record whether your application SDKs buffer or drop spans during that window. The results decide whether you need a retry or buffering layer in front of Langfuse.
Under heavy ingestion, Langfuse recommends splitting langfuse-web into separate ingestion and user interface deployments, so a burst of trace uploads does not slow engineers' dashboard queries. Decide on the split early because it changes ingress rules, autoscaling signals and the metrics you monitor. Langfuse recommends scaling worker containers on CPU load, which gives you a starting signal but not a measure of ingestion lag. Plan a check that compares trace arrival with trace creation during a rolling update, so you can confirm the ingestion path keeps accepting data while pods are replaced.
Which Langfuse components need redundancy, and what each one does
A self-hosted Langfuse deployment runs six moving parts, and a trace reaches the UI only after each part on its path accepts it. Each component fails in a different way, so redundancy decisions should start with what breaks visibly and what each one stores. The minimum resources below come from the scaling guide and are floors, not production sizing.
Langfuse Web (UI and APIs)
Langfuse Web serves the user interface and the public APIs, including the OpenTelemetry endpoint that SDKs and exporters call to send traces. When it is down, the UI is unreachable and instrumented applications receive request errors, so their retry or buffering behavior matters as much as the replica count. The scaling guide lists a minimum of 2 CPU and 4 GiB. Under heavy ingestion, the guide suggests splitting ingestion and UI traffic into separate deployments, so confirm how many replicas each path needs.
Langfuse Worker (asynchronous processing)
Langfuse Worker processes events after the web tier accepts them, then writes the results into the data stores. When workers are down, traces may be accepted but absent from the UI, and the backlog grows until processing resumes. The scaling guide recommends scaling worker containers on CPU load, and it lists a minimum of 2 CPU and 4 GiB. Ask your team to define how long a trace may take to appear and to alert when that delay grows.
PostgreSQL metadata store
PostgreSQL holds transactional metadata such as organizations, projects, users, API keys, prompts and configuration. When it is down, sign-in, project switching, prompt management and most administrative actions fail, and the outage is immediately visible to users. The scaling guide lists a minimum of 2 CPU and 4 GiB, and the v4 upgrade guide requires PostgreSQL 15 or later. Confirm the failover behavior, backup schedule and restore time of the managed service you choose.
ClickHouse analytical store
ClickHouse stores the trace and observation data that the UI searches and the analytics views aggregate. When it is down, trace search, dashboards and score queries fail, and ingestion can fall behind while workers wait for storage. The scaling guide lists a minimum of 2 CPU and 8 GiB, with at least 16 GiB of memory for larger deployments. The v4 guide requires ClickHouse 25.12 or later and recommends 26.4, so confirm replication and backup support for the version you plan to run.
Redis or Valkey cache and queue
Redis or Valkey carries work between the web and worker tiers and supports caching. When it is unavailable, confirm with the Langfuse documentation which request and processing paths stall, since that determines how visible the failure will be. The scaling guide lists a minimum of 1 CPU and 1.5 GiB, and the v4 guide requires Redis 7.0 or later. Check persistence settings, failover support and whether your managed offering replicates data across zones.
S3-compatible blob storage
Blob storage holds the uploaded event payloads and other objects that Langfuse writes and reads during processing. When it is unreachable, uploads and downstream processing fail, and the failure can look like a worker problem until someone checks the storage logs. The scaling guide does not give a minimum for blob storage, so ask your provider to confirm capacity and request limits. Verify versioning, replication to a second site or account, and lifecycle rules that could delete data still in use.
Which deployment decisions shape Langfuse availability and operating load
This table compares the main deployment choices that determine how well self-hosted Langfuse tolerates failures and how much ongoing work your team carries. Read each row by asking which side of the trade-off your team can operate, and where a managed or replicated option moves that work to your cloud provider. Managed and replicated options still need your own failover tests and backup restores, and you should confirm with Langfuse and your provider which replication features your versions support.
| Decision | When it fits | Operational cost to you |
|---|---|---|
| Docker Compose on one VM | Testing and evaluation only, where downtime is acceptable | No high availability. A VM or disk failure stops tracing until you restore from backup |
| Kubernetes with Helm or Terraform | Production deployments that need multiple web and worker replicas and rolling upgrades | Your team operates the cluster, chart values, secrets and upgrade sequence, including the Helm chart v1 to v2 step for bundled data stores |
| One combined langfuse-web deployment | Low to moderate trace volume where a single UI and ingestion path is enough | Heavy ingestion can slow dashboards and API reads, and both traffic types scale together |
| Separate ingestion and UI web deployments | Heavy ingestion, where Langfuse recommends splitting traffic | Two deployments to configure, scale and monitor, with routing and capacity planning between them |
| Self-run PostgreSQL, Redis or Valkey and ClickHouse | Teams with established database operations and in-house backup skills | You own patching, failover, replication, backups and restore tests for each store |
| Managed database services from your cloud provider | Teams that want the provider to handle hosting, patching and some failover | Provider-specific limits, maintenance windows and costs apply. Confirm supported Langfuse versions and connection settings before adopting |
| Single-instance ClickHouse | Pilot or small datasets where a rebuild from backup is acceptable | A node failure interrupts ingestion and queries. Recovery depends on your backup and restore process, and backfills need extra disk headroom |
| Replicated ClickHouse | Production workloads where query and ingestion continuity matters | Replication topology, keeper or coordination services and failover behavior need your own testing. Confirm supported replication setup with Langfuse and your provider |
| Blob storage in one bucket | Simple setups where losing event exports or uploads is recoverable from other copies | A bucket outage or accidental deletion can affect stored data unless you add backups |
| Replicated or versioned bucket policy | Regulated or high-value data where deletions must be recoverable | Added storage cost, lifecycle rules and restore procedures to design, test and document |
Why separating ingestion from UI traffic changes the design
Separating ingestion from UI traffic matters because the two loads arrive in different shapes. SDKs and OpenTelemetry exporters write traces continuously in the background, while engineers open dashboards and call APIs in bursts, often during an incident or a release review. In a single langfuse-web deployment, both kinds of request compete for the same CPU. Heavy ingestion can slow dashboards, and heavy dashboard queries can slow trace acceptance. Langfuse's scaling guidance starts with the worker containers, which handle asynchronous event processing and are scaled on CPU load. The split into separate web deployments is the next step, and it only earns its place when the web tier is the bottleneck.
Decide whether the split is needed from measurements, not assumptions. Look for three signals: ingestion latency observed from the SDK or exporter side that rises during traffic peaks, CPU saturation on langfuse-web while worker CPU is healthy, and dashboard timeouts that line up with the same peaks. Collect these over a period that includes your busiest day, using ingress logs and container metrics. If workers are saturated, add worker replicas first, because splitting the web tier will not help a worker bottleneck. Record which endpoints generate most of the load, so you can tell whether ingestion or interactive reads drive the peak.
Both deployments run the same Langfuse application and share the same data stores: PostgreSQL, ClickHouse, Redis or Valkey, and S3-compatible blob storage. Splitting the web tier therefore changes where requests are served, while the data layer stays common. Plan for that shared layer explicitly. Connection limits on PostgreSQL and ClickHouse must cover both deployments at their maximum replica counts. An expensive dashboard query still reaches ClickHouse, so it can affect ingestion indirectly. Environment variables, including encryption keys and secrets, should match across both deployments, so that each one reads and writes the same data consistently. The scaling guide's minimum resource figures apply per container, so confirm the sizing for each deployment against your expected peak.
Before you commit to the design, confirm several points with Langfuse for your version. First, whether ingestion endpoints, including the SDK ingestion API paths and the OTLP endpoint at /api/public/otel, can be routed at your ingress to the ingestion deployment, while all other paths go to the user interface deployment. Second, whether any UI-driven or public API calls depend on behavior that only the UI deployment performs. Third, how database migrations run during upgrades when two web deployments start, and whether a rolling upgrade can run without interrupting ingestion. Fourth, how the split is expressed in the Helm chart or Terraform module you plan to use. Write the routing rules and test them in a non-production environment before go-live. DeployOpen can design and test these rules in your environment as part of a scoped engagement, but the vendor's guidance for your version should settle the questions above.
How to build a highly available Langfuse deployment step by step
This sequence takes a self-hosted Langfuse deployment from a fixed platform and version matrix to a documented baseline in which web, worker and data stores can each fail without stopping trace ingestion. Each step closes with a check your team can run before moving on. Confirm the points flagged below with Langfuse and your platform vendors before you commit to a design.
Fix the target platform and versions
Your platform team confirms the Kubernetes path, since Langfuse documents Helm or Terraform deployments on AWS, Azure and GCP for production. Check the version matrix against the v4 requirements: PostgreSQL 15 or later, Redis 7.0 or later and ClickHouse 25.12 or later, with 26.4 recommended. Pin the Langfuse release you will install and confirm it against the upgrade guide, because the latest listed release on the GitHub releases page is v4.63.0. Done when a written version matrix is approved and every component has a named owner.
Choose the data store topology
Decide whether PostgreSQL, ClickHouse and Redis or Valkey will run as managed cloud services, operator-run clusters or self-managed replicas inside your network. Managed services move patching and failover work to the provider, but you still need to confirm the high availability and backup options they offer in your region. Ask Langfuse whether a bundled data store chart is acceptable for production, since Helm users with bundled data stores must move the chart to v2 before upgrading to v4. Done when a topology diagram names each store, its redundancy model and its owner.
Deploy data stores with redundancy and backups
Deploy PostgreSQL, ClickHouse and Redis or Valkey with their own replication, and size them against the minimums in the Langfuse scaling guide: 2 CPU and 4 GiB for 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. Enable scheduled backups for each store. If you plan a v4 upgrade with historic backfill, provision roughly 3x ClickHouse disk headroom, as the upgrade guide advises. Done when a restore test into a scratch environment succeeds for each store.
Deploy web and worker with replicas and resource requests
Deploy Langfuse Web and Langfuse Worker with at least two replicas each, setting CPU and memory requests no lower than the documented 2 CPU and 4 GiB minimum for each container. Scale workers on CPU load, as the scaling guide recommends. Confirm with Langfuse how concurrent replicas coordinate database migrations at first boot, because migrations run automatically at startup and two pods starting together could contend for the same schema changes. Done when every replica reports ready, the migration log shows one completed run, and a test trace appears in the UI.
Split ingestion and UI traffic if load requires it
Measure ingestion volume and UI response times under representative load before deciding on a split. If ingestion competes with the interface for capacity, separate langfuse-web into ingestion and user interface deployments, as the scaling guide describes for heavy ingestion. Confirm the routing paths for SDK and OTLP traffic with Langfuse, then keep both deployments on the same data store configuration. Done when a load test shows UI response times holding steady while ingestion runs, or when the team has recorded that no split is needed at current volume.
Configure S3-compatible blob storage and test access
Create the bucket in your S3-compatible store with the access policy the Langfuse configuration requires, and inject credentials from your secrets manager. Ask the storage team to confirm versioning, replication and retention settings for the bucket, since these determine what survives a failure. Then verify read and write from the application: send a trace with a payload, and retrieve it through the Langfuse API from a different replica. Done when reads and writes succeed from every web and worker replica and the bucket settings are recorded.
Place endpoints behind a load balancer with health checks
Front the web endpoints with a load balancer that checks the health endpoint documented for your Langfuse version. Set timeouts long enough for ingestion requests, configure TLS termination to your security standard, and confirm that the ingestion path is reachable from application networks. Remove any direct route to a pod that bypasses the load balancer. Done when the load balancer marks a deliberately stopped replica unhealthy and sends traffic only to ready replicas.
Run a rolling restart with a test trace in flight
Send a steady stream of test traces, then restart web and worker replicas one at a time. Watch for failed writes, dropped requests and migration messages during each restart. Confirm that the traces sent during each restart are present afterward. Once the test passes, record the configuration as the baseline: image versions, data store versions, replica counts, resource settings, backup schedule and the restore test result, and store it in your change management system. Done when the restart completes with no lost test traces and the baseline is stored.
What drives effort and cost for a highly available Langfuse deployment
This table lists the factors that change the work of running Langfuse with redundancy, why each one matters, and how to keep it manageable. Read each row as a question to answer in your own environment before asking for a quote, since the same driver can be cheap in one organization and expensive in another.
| Driver | Why it changes the work | How to keep it down |
|---|---|---|
| Trace volume and ingestion rate | Higher ingestion raises CPU load on worker containers and pushes ClickHouse memory and disk needs up. Langfuse recommends scaling workers on CPU load and splitting langfuse-web into ingestion and UI deployments under heavy ingestion. Paid for by the internal platform team in its cloud account. | Set expected trace volume per day and peak rate early. Test ingestion at realistic peaks before go-live and confirm scaling triggers with your platform team. |
| Number of environments | Test, staging and production each need their own web, worker, PostgreSQL, Redis or Valkey, ClickHouse and blob storage. Redundancy multiplies per environment, so the infrastructure bill and the number of failure paths grow together. Cloud provider charges for compute and storage; internal team operates them. | Decide which non-production environments need redundancy at all. Use smaller single-replica test setups where your risk allows, and reuse infrastructure definitions across environments. |
| Self-run data stores versus managed services | Running PostgreSQL, ClickHouse, Redis or Valkey yourself means your team owns patching, failover, backups and version upgrades. Managed services shift part of that work to the cloud provider but still need configuration checks. Confirm with the provider which parts of failover and backup they handle. | Choose managed services where your provider offers them and your data residency rules allow. For self-run stores, document the owner of each failover and restore step before the build starts. |
| Cloud or data center constraints | Air-gapped networks, restricted regions or on-premises hardware limit which managed services and images you can use, and change how blob storage and load balancing are built. Costs fall to your infrastructure team, and DeployOpen works within the constraints under a fixed scope. | Document network, region and storage rules before design. Ask DeployOpen to confirm the target platform in the written scope so the quote reflects your actual constraints. |
| Number of teams and SDKs sending traces | Each team using a different SDK, language or OpenTelemetry instrumentation adds integration work, configuration variation and support questions. Traffic patterns from many clients also make ingestion less predictable. Internal application teams carry most of this work. | Standardize on a small set of approved SDK patterns and a shared tracing convention. Onboard teams in waves so ingestion problems can be traced to one source at a time. |
| Backup and restore requirements | Backups must cover PostgreSQL, ClickHouse and blob storage, and they must be restorable within a target you set. Restore tests take time and need spare capacity, and ClickHouse backfills can need significant extra disk. Recurring storage cost and test effort both grow with retention length. | Write recovery objectives for each store, schedule restore tests, and agree retention periods with legal and compliance owners. Ask for restore tests to be included in the written scope. |
| Scope of failure testing | Testing only a clean restart is far cheaper than testing loss of a worker node, a database failover, a Redis outage or a ClickHouse node going down during ingestion. Each failure mode adds setup, observation and remediation time. The testing cost is paid by whoever owns the test environment, or by DeployOpen if it is in the fixed scope. | Rank failure scenarios by business impact and test the highest-impact ones first. Agree in writing which scenarios are in scope and what evidence each one must produce. |
| Upgrade path from an older major version | Moving from v1 or v2 to v3 or v4 is a migration project, not a routine update. Langfuse documents that v3 to v4 needs ClickHouse 25.12 or later (26.4 recommended), PostgreSQL 15 or later, Redis 7.0 or later, and about 3x ClickHouse disk headroom when backfilling. Helm users with bundled data stores must upgrade the chart from v1 to v2 first. Upgrade work is usually a one-time cost, but it can recur at each major version. | Check current versions against the Langfuse upgrade guides before planning. Confirm data store versions, disk headroom and rollback steps in a non-production copy. Plan major upgrades separately from routine within-version updates. |
How to protect the highly available path without weakening availability
Langfuse high availability adds replicas, separate ingestion and UI paths, and more places where secrets and backups live. The controls below apply the same protections to every replica and every copy of the data, so resilience does not create a weaker entry point.
Separate network exposure for ingestion and UI
When langfuse-web is split into ingestion and user interface deployments, each path can be exposed differently. The ingestion endpoint usually needs reachability from application workloads, while the UI should be limited to staff networks or an identity-aware proxy. Check that each service has its own ingress rules, load balancer scope and allowed source ranges, and that a failover target inherits the same restrictions.
Keep connection secrets out of manifests
Database, Redis or Valkey, and blob storage credentials should come from your secret manager and be injected at runtime, not written into Helm values or Kubernetes manifests in plain text. Confirm that every replica, including new workers added during scaling, reads the same secret source, and that rotating a credential does not require editing the chart. Ask who can read the secret store and whether access is logged.
Scope blob storage access to the Langfuse bucket
Langfuse uses S3-compatible blob storage for event and media data, so the credentials it holds determine what an attacker could reach if they were exposed. Create a dedicated identity that can read and write only the Langfuse bucket or prefix, with no list or delete rights on unrelated buckets. Confirm that lifecycle and versioning settings are managed by the platform team, not by the application identity.
Encrypt backups and restrict restores
Backups of PostgreSQL, ClickHouse and blob storage hold the same sensitive prompts and traces as the live system, so they need encryption at rest and a separate key policy. Limit restore rights to a small group, and require a documented approval step before restoring into a production namespace. Test restores into an isolated environment so the recovery path is verified without exposing data to more people.
Enforce SSO on every replica
Langfuse can disable password sign-in or enforce SSO for specific email domains. These settings must apply consistently across all web replicas and any UI deployment, so a failover or a rolled-back instance does not reopen a password login path. Verify the configuration on each replica after deployments. The provider setup itself is covered in the Langfuse SSO and identity integration topic.
Check which controls need an Enterprise key
Some protections, including data retention policies, server-side data masking, audit logs and project-level RBAC, require an Enterprise license key according to the Langfuse license key documentation. Confirm which of these your design depends on before go-live, since the open source edition will not enforce them. Data masking and retention are covered in the Langfuse security hardening topic.
How to test that tracing survives a failure before you rely on it
Test the design before you trust it. A high availability layout for Langfuse is only a claim until you have sent real trace traffic through it, broken the parts it depends on and measured what the SDKs and the UI actually did. Build the test plan from the components your deployment really uses: the Langfuse Web and Langfuse Worker containers, PostgreSQL, ClickHouse, Redis or Valkey, and the S3-compatible blob storage. Run it in a staging environment that matches production topology, including the separate ingestion and user interface deployments if you have split langfuse-web.
Start with a steady stream of test traces from an instrumented application that sends through the same ingestion path your production services use. Use a fixed rate and a tagged trace name so you can count what arrived. Then stop one web replica and one worker replica while the stream is running. Confirm that the loss stays within the window your SDK configuration allows, that it does not continue after the pods restart, and that traces sent after recovery appear in the UI and the API. Repeat the test with the failure happening during a burst, because that is where queues and retries show their weaknesses.
Next, drain a Kubernetes node that hosts Langfuse pods and confirm that the scheduler reschedules them and that the Deployments return to their desired replica counts. Then fail over or restart the PostgreSQL primary, Redis or Valkey, and a ClickHouse node in staging, one at a time. For each one, record what the SDKs report, whether the Langfuse UI loads, whether new traces are accepted and how long the system takes to return to normal. Confirm the failover behavior with your own managed database or cluster configuration, since the outcome depends on how those services are set up, not only on Langfuse.
Restore backups into a separate environment to prove that the backup set is usable. Restore PostgreSQL, ClickHouse and blob storage from the same point in time if your tooling allows it, then check that existing traces and prompts are readable, that prompt versions and labels match, and that new traces can be written to the restored instance. A backup that has never been restored is an assumption. Note any mismatch between the database and blob snapshots, because references from one to the other will break silently if they drift apart.
Record evidence as you go: timestamps for each injected failure and each recovery, error rates from the SDKs and the ingestion endpoint, and trace counts before and after every test, compared against the number you sent. Before you run the tests, ask Langfuse how its SDKs behave while the endpoint is unavailable, including retry counts, backoff, buffer size and what is dropped when the buffer fills or the process exits. That answer sets the acceptable loss window, and the test results should be judged against it, not against an assumed zero.
What to monitor, back up, upgrade and document after go-live
Going live starts the operating work for a high availability Langfuse deployment. The cards below cover the signals to watch, the backups and restore tests that protect trace data, the upgrade path, and the records your on-call team needs during an incident.
Web and worker health
Track CPU and restart counts for every Langfuse Web and Langfuse Worker container. Rising restarts often point to memory pressure or a failing dependency, so check them before adding replicas. Langfuse recommends scaling workers on CPU load, so use that signal to set scaling thresholds and confirm them with a load test that resembles your expected trace volume.
Ingestion latency and backlog
Watch ingestion latency and queue backlog, which usually show that workers cannot keep pace with trace volume before any container fails. A growing backlog delays traces in the UI and API even when every container reports healthy. If ingestion and UI traffic share one deployment under heavy load, Langfuse suggests splitting langfuse-web into separate ingestion and user interface deployments. Confirm which backlog metrics your Langfuse version exposes before you build alerts on them.
Data stores and blob storage
Monitor the health of PostgreSQL, Redis or Valkey, and ClickHouse, and track disk headroom against the sizing you chose, since a full volume stalls ingestion. Alert on error rates from the S3-compatible bucket, including failed writes and authorization errors after a credential rotation. Blob storage failures can leave data unavailable even when the database tier is healthy. The ClickHouse sizing and operations topic covers ClickHouse signals in depth.
Backups on separate schedules
Back up PostgreSQL, ClickHouse and blob storage on separate schedules, because each holds different data and needs different tooling. A single snapshot time can leave metadata and trace data out of step after a restore. Set schedules and retention from your recovery requirements, then restore regularly into a non-production environment, since an untested backup is unproven. The ClickHouse operations topic covers ClickHouse backup mechanics.
Upgrades within and across majors
Updates within a major version are non-disruptive, and database migrations run automatically at startup. Major upgrades follow the dedicated migration guide for each step, so plan them as projects rather than routine patches. The v3 to v4 guide advises about 3x ClickHouse disk headroom while backfilling historic data, so secure that capacity before the upgrade window. The upgrades and support topic covers version requirements and Helm chart steps.
Topology and failover runbooks
Document the topology, replica counts for web and worker, and the failover runbook for each data store in a form on-call engineers can follow during an incident. Record how each store fails over or is rebuilt, who owns each step, and the restore procedure linked to your latest backup test. Update these records after every upgrade or topology change, so the documentation matches the running system.
What buyers and engineers ask before building Langfuse high availability
Does high availability for self-hosted Langfuse need an Enterprise license key?
Based on the license key documentation, high availability is not on the list of self-hosted features that need an Enterprise license key, but confirm this with Langfuse for your specific version before you commit to a design. The features that do need a key include project-level RBAC roles, protected prompt labels, data retention policies, audit logs, server-side data masking, UI customization, SCIM and the instance management API. Your HA design still needs the platform pieces it depends on, including multiple Web and Worker replicas, PostgreSQL, ClickHouse, Redis or Valkey and S3-compatible blob storage, and those costs are separate from any license decision. Ask Langfuse to confirm the license position in writing as part of your scope.
How much effort does a Langfuse high availability build involve?
The effort depends mostly on four things: the deployment topology, who owns each data store, how far failure testing goes and how many environments must match. A design with multiple Web and Worker replicas on Kubernetes, with managed PostgreSQL, Redis and blob storage, is less work than one where your team also runs a clustered ClickHouse. Failure testing is often the largest variable, because each scenario you test (a lost Worker, a restarted Web pod, a failed database node, a restored backup) needs a runbook, a test run and evidence. Ask any provider for a written breakdown of effort by component, by environment and by test scenario, so you can compare proposals on the same basis.
How long does a Langfuse high availability project take to complete?
Duration depends on the environments in scope, the scope agreed in writing and how quickly your team can provide access, decisions and reviews. A single production environment with documented failure tests takes less time than several environments with separate data store ownership and a full restore drill. Langfuse's own guidance does not give a single timeline for HA work, so no reliable figure can be stated here. Ask for a written plan with phases, dependencies on your cloud, network and identity teams, and the points where your sign-off is required. Treat any duration that is not tied to that plan as an estimate to challenge.
What must our team provide for a Langfuse high availability build?
Your team needs to provide the following before work starts, and gaps here are the most common cause of delay:
- Cluster access, such as a Kubernetes namespace with the permissions needed to deploy and test.
- Cloud accounts or data center access for PostgreSQL, ClickHouse, Redis or Valkey and object storage.
- Decisions on data store ownership: managed services or self-run, and who holds backup and restore responsibility.
- A test application that sends traces, so failure tests produce realistic ingestion load.
- A named owner on your side who can make decisions and approve changes.
The named owner matters most, because failure testing needs someone who can approve a planned disruption in a live environment.
Can one engineer build Langfuse high availability without help?
One experienced engineer can build a single environment alone if the design is documented and the data stores are already available to them. Failure testing and backup restores are different, because they need a second person to observe, record results and confirm that the recovery worked as written. Without that second check, a test can pass on the engineer's assumptions rather than on evidence. Plan for at least two people with access to the environment, one who builds and one who reviews and runs the restore drill. Also make sure the knowledge sits in runbooks and not only with one person, since on-call coverage will depend on it.
What happens to Langfuse high availability during a major version upgrade?
Langfuse states that updates within a major version are non-disruptive and apply database migrations automatically at startup, so routine patch and minor updates are the easier case. Major upgrades, such as v3 to v4, follow dedicated migration guides and need planning. The v3 to v4 guide requires ClickHouse 25.12 or later (26.4 recommended), PostgreSQL 15 or later and Redis 7.0 or later, moves to an observations-first ClickHouse data model and advises about 3x ClickHouse disk headroom when backfilling historic data. Helm users with bundled data stores must upgrade the chart from v1 to v2 before upgrading to v4. Confirm with Langfuse how your replicas, rolling updates and failover behave during each step, and test the upgrade in a non-production environment first.
Should we separate ingestion from UI traffic in a Langfuse HA design?
Separating ingestion from UI traffic is a design choice to make when trace volume or ingestion load is high, and Langfuse recommends splitting langfuse-web into separate ingestion and user interface deployments in that case. The benefit is that heavy trace ingestion is less likely to slow dashboards and API reads, and each path can be scaled on its own. The cost is a second deployment to build, monitor and test, plus routing rules at your ingress or load balancer. Workers are usually scaled on CPU load, according to Langfuse's scaling guidance. Decide this with your expected trace volume and a load test, and document the scaling thresholds your on-call team will act on.
How DeployOpen works on Langfuse high availability
DeployOpen plans and builds a highly available Langfuse deployment inside your own cloud accounts, data centers or air-gapped network. Each step is agreed in writing, from the failure modes you need to survive through to the runbooks your operators use after go-live.
How we scope
- Map which Langfuse Web, Worker, PostgreSQL, ClickHouse, Redis or Valkey and blob storage components must survive a single failure
- Agree whether ingestion and user interface traffic get separate Langfuse Web deployments, based on your expected trace volume
- Confirm target environment, such as Kubernetes with Helm or Terraform on AWS, Azure or GCP, and who owns each data store
- Record availability expectations, recovery objectives and backup retention in the written, fixed-scope agreement before work starts
How we build
- Deploy multiple Langfuse Web and Worker replicas, with worker scaling tied to CPU load as Langfuse recommends
- Configure replicated or managed PostgreSQL, Redis or Valkey and ClickHouse topologies that match the scoped failure tolerance
- Place blob storage on durable, S3-compatible storage and verify that trace uploads and exports survive a node or zone loss
- Test failover for each component, including a rehearsed restore of PostgreSQL and ClickHouse from backup
How we hand over
- Deliver infrastructure as code, configuration, and Helm or Terraform values so your team can reproduce the deployment
- Document the topology, failover behavior, backup schedules and restore procedures in a runbook written for your operators
- Provide test evidence from failover and restore exercises, mapped to the scoped requirements
- Walk your operations team through alerts, scaling signals and the steps to take when ingestion falls behind
How we support
- Plan Langfuse upgrades around the migration guides, including ClickHouse and PostgreSQL version checks before each change
- Offer ongoing support, upgrades and continuous testing as a retainer, scoped separately from the build
- Re-run failover and restore tests after infrastructure changes, data store upgrades or major Langfuse version moves
- Confirm with Langfuse which support tier, such as its Self-Hosted Enterprise plan, your team needs for vendor escalation
