Prometheus vs OpenTelemetry: when each one fits
Prometheus alone fits a team whose goal is metrics: a platform group that needs to collect, store and alert on time series from services that already expose a metrics endpoint. Prometheus pulls metrics over HTTP from targets found through service discovery or static configuration, stores them in its local time series database, queries them with PromQL and sends alerts to Alertmanager. It is Apache-2.0 licensed with no paid edition of the project itself, so the cost is the infrastructure, the storage and the people who run it. OpenTelemetry is the better starting point when you need a single instrumentation standard across metrics, traces and logs, or when you expect to change backends later. The OpenTelemetry Collector receives, processes and exports telemetry and can run as an agent beside services or as a gateway, but OpenTelemetry does not store or visualize data itself, so you still need a backend.
Most mature estates end up using both. OpenTelemetry instrumentation or the Collector can receive application data and expose metrics that Prometheus scrapes, or export to Prometheus-compatible endpoints, so Prometheus stays the metrics store while OpenTelemetry carries the traces and logs. Confirm the exact exporter and receiver names, and their stability level, against the Collector component README and your chosen Collector distribution before you design around them.
Three questions decide the shape of the choice. First, are traces and logs in scope now or within the next planning cycle? If not, Prometheus alone may be enough. Second, how many services need instrumentation, and how many of them already expose metrics? A small fleet can be instrumented by hand, while a large one benefits from one shared standard and Collector configuration. Third, who will run the pipeline? Someone must own the Collector configuration, its security settings and its upgrades, and must keep Prometheus storage and Alertmanager highly available. Write those owners down before you pick the stack.
Which scenarios favor Prometheus, OpenTelemetry or both
This table maps common estate shapes to the better starting point, Prometheus, OpenTelemetry or both together. Read each row for the condition that changes the answer, because Prometheus stores and alerts on metrics while OpenTelemetry generates and exports telemetry for metrics, traces and logs.
| Scenario | Better fit | Condition that makes it so |
|---|---|---|
| Kubernetes estate with mostly metrics | Prometheus | Prometheus covers scraping, PromQL and Alertmanager for metrics. OpenTelemetry adds value only if you also need traces or logs from the same services. |
| Mixed estate with traces and logs | Both | Prometheus handles metrics and alerting, while OpenTelemetry generates and exports traces and logs. Confirm which backend stores traces and logs, since neither project is a full observability backend. |
| Team that must avoid lock-in to one backend | OpenTelemetry, with Prometheus as one exporter target | Instrumenting with OpenTelemetry SDKs and the Collector keeps the export destination a configuration choice. Prometheus remains a valid destination for metrics, so the backend can change without re-instrumenting. |
| Small team with no instrumentation yet | Prometheus first, OpenTelemetry later | Prometheus exporters and scrape configs deliver useful metrics quickly with a small surface area. Add OpenTelemetry when traces or logs become a requirement, and check log SDK maturity for your languages first. |
| Workloads needing exact per-event counts | Neither alone, confirm the design | The Prometheus overview says it favors reliability over precision and is not suited to 100% accuracy such as per-request billing. Ask whether counts must be exact before choosing a metrics pipeline. |
| Long-term metric retention beyond one server | Prometheus plus a remote storage system | Local Prometheus storage is not clustered or replicated and defaults to 15 days of retention. Remote write to external long-term storage is the usual path, and the OpenTelemetry Collector can also export metrics there. |
| Strict pipeline security and config control | Both, with Collector hardening | The Collector supports authenticator extensions for incoming and outgoing connections. Confirm receiver authentication, exporter credentials and the configuration best practices before production use. |
| Teams with mixed language stacks | OpenTelemetry | Trace APIs and SDKs are stable in most languages, including Go, Java and Python, while log SDK maturity varies. Check the status page for each language in your estate. |
How each one is built and what that means for operations
Prometheus and OpenTelemetry solve different halves of the telemetry problem, and their components map onto different operational duties. The cards below cover what each piece stores, how data moves between them, and which parts your team must size, secure and monitor.
Prometheus server and local storage
The Prometheus server writes samples into a local time series database built from two-hour blocks with a write-ahead log. That storage is not clustered or replicated, so a server failure means a gap in history unless you run a second instance or send data elsewhere. Check how much disk each server needs, how long you keep data, and whether your team can treat each server like any other single-node database with backups and restore tests.
Default 15-day retention
Prometheus keeps data for 15 days by default, and you can change retention by time or by size. Anything older is lost unless it has been forwarded to external storage. Teams that expect to compare quarterly trends or answer audit questions about past incidents need to set retention deliberately and confirm where the older data lives. Ask whether dashboards and alerts depend on history beyond the local window.
Pull model with service discovery
Prometheus pulls metrics over HTTP from targets it finds through service discovery or static configuration. This makes the server the controller of scrape timing and target lists, and it gives you a clear signal when a target disappears. For short-lived batch jobs, Prometheus offers a push gateway as an intermediary. OpenTelemetry instead expects applications or agents to send data out, so teams moving between the two must confirm which services are reachable from the scraper and how firewalls and network policies are set.
Alertmanager as a separate component
Prometheus evaluates alerting rules but hands fired alerts to Alertmanager, which handles grouping, inhibition and silences. Alertmanager runs as its own process and can form a cluster for high availability. Prometheus should list every Alertmanager instance directly rather than sit behind a load balancer. Your operations team needs to deploy, monitor and back up Alertmanager as a second system, and confirm who owns routing and on-call changes.
Remote write to external storage
Remote write and remote read let Prometheus send samples to, and read them from, external long-term storage systems. This is the usual way to extend retention or add redundancy beyond one server. It also adds a network path, a receiving system to size, and a window where samples can be lost if the link fails before the write completes. Confirm the receiving product, its licensing and its failure behavior before you rely on remote write for audit or capacity planning.
OpenTelemetry SDKs and Collector roles
OpenTelemetry SDKs generate traces, metrics and logs inside applications, and the Collector receives, processes and exports them. It can run as an agent beside each service or as a gateway that accepts data from many sources. The Collector is a pipeline you must size, secure and monitor, including its queues, memory limits and exporter failures. Check which components your version marks as stable, because their maturity varies. Its authenticator extensions can secure incoming connections and outgoing exporter requests.
Prometheus vs OpenTelemetry side by side on capability
This table compares the two projects row by row on role, signals, storage, query, alerting, instrumentation, neutrality, ingestion identity and licensing. Read it as a split of responsibilities: OpenTelemetry moves and processes telemetry, while Prometheus stores metrics and evaluates alerts. Most rows where the two overlap depend on how you deploy them, so check the cells marked for confirmation against your design.
| Capability | Prometheus | OpenTelemetry |
|---|---|---|
| Primary role | Metrics collection, local time series storage, query and alert evaluation | Framework and Collector for generating, exporting and processing telemetry; not a storage backend |
| Signals covered | Metrics only | Traces, metrics and logs, with maturity varying by signal and language |
| Storage | Local time series database in two-hour blocks, default 15-day retention, not clustered or replicated; long-term storage via remote write | No storage of its own; exports to a separate backend such as a metrics, trace or log store |
| Query language | PromQL | None; querying happens in the backend you export to |
| Alerting | Alert rules in Prometheus, with Alertmanager handling grouping, inhibition and silences | No alerting engine in the Collector; confirm alerting in your chosen backend |
| Instrumentation model | Pull over HTTP from targets, with a push gateway for short-lived jobs | SDKs and Collector receivers; data can be pushed to the Collector agent or gateway |
| Vendor neutrality | Open project with a standard exposition format; backend choice is tied to what remote write supports | Described as vendor- and tool-agnostic; confirm exporter support for your target backend |
| Identity on ingestion | Scrape authentication depends on target and configuration; confirm TLS and credentials per job | Authenticator extensions can check incoming connections, for example with OIDC, and add credentials to outgoing exports, for example with oauth2client |
| Licensing | Apache-2.0, with no paid edition of the project itself | Collector repository is Apache-2.0; confirm the license of any contrib component you add |
Prometheus vs OpenTelemetry licensing and editions
Prometheus is licensed under Apache-2.0, and the project has no paid enterprise edition, so the license itself adds no cost. Its release model is what you plan around. A new minor release cycle begins every 6 weeks, and a minor release generally stops receiving bug fixes after those 6 weeks. For a longer support window, the project publishes long-term support (LTS) releases that receive bug fixes for one year. The current LTS line is 3.13, released July 1, 2026 and supported until July 31, 2027, while LTS 3.5 reached end of life on July 31, 2026.
- Apache-2.0 license: You may run, modify and redistribute the software without a license fee.
- No enterprise edition: Paid capability must come from a third party or from your own staff, so ask any vendor exactly which features they add.
- Short minor-release window: Plan upgrades on the 6-week cycle unless you pin to the LTS line.
- One-year LTS support: The 3.13 line is supported until July 31, 2027, which sets your first forced upgrade point if you stay on it.
- Costs sit elsewhere: Infrastructure, long-term storage, implementation and the people who operate the service carry the real spend.
Confirm your upgrade cadence against the release cycle page before you commit to a version, and budget the staff time for that cadence explicitly.
The OpenTelemetry Collector repository is licensed under Apache-2.0, and it is a vendor-agnostic way to receive, process and export telemetry. It can run as an agent beside services or as a gateway that accepts data from many sources. Collector v0.163.0 was released on October 16, 2026, after v0.162.0 on September 29 and v0.161.0 on September 16, 2026, so the release rhythm is frequent. Each release ships the contrib distribution with signed artifacts, SHA256 checksums and SBOM files, alongside the Collector builder and OpAMP supervisor.
- Contrib distribution: The contrib build bundles community components, so check which components you rely on for stability.
- Signed artifacts and checksums: Verify signatures and SHA256 checksums before you deploy, and keep the SBOM with your change records.
- SBOM files: The bill of materials helps your vulnerability and compliance teams trace components without rebuilding the artifact.
- Builder for custom builds: If you need a smaller binary, the builder lets you assemble your own distribution, which then becomes your responsibility to maintain.
- Mixed component stability: Core components have different stability levels, documented in each component's README.
If you buy a vendor-supported Collector distribution, check its support terms, signing practices and release window separately, because they are not covered by the upstream project.
OpenTelemetry is a Cloud Native Computing Foundation project formed by merging OpenTracing and OpenCensus. It is an observability framework and toolkit for generating, exporting and collecting traces, metrics and logs. It is not an observability backend, so storage and visualization are separate purchases or separate open-source components. Stability differs by signal and language: trace APIs and SDKs are stable in most languages, including Go, Java, Python and .NET, while log SDK maturity varies more, with several languages still in development.
- Framework, not a product: Nothing in the project is sold as a license, so the cost is in instrumentation, backends and operations.
- Signal maturity varies: Trace support is broadly stable, while log SDKs differ by language, so check each language your teams use.
- Backend choice stays open: Because storage is separate, you choose where traces, metrics and logs land and can change that later.
- Authenticator extensions: The Collector can authenticate incoming connections, for example with OIDC, and add credentials to outgoing requests, which affects how you handle identity in your pipeline.
- Vendor distributions: Any commercial distribution, managed service or support contract is a separate agreement with its own terms.
Ask each instrumentation and backend owner which signals and language SDKs they depend on, and confirm those against the status page before you standardize.
What drives the running cost of each
The running cost of a Prometheus plus OpenTelemetry stack is mostly engineering time, compute and storage, because neither project carries a license fee. Prometheus is Apache-2.0 with no paid enterprise edition, so the recurring spend sits in the servers or Kubernetes capacity that run the Prometheus servers and Collectors, the disks that hold local time series data, and the people who operate them. Storage growth is the component that surprises most teams. Prometheus keeps data in two-hour blocks with a default retention of 15 days, and the footprint grows with the number of active series and the retention you configure. Because local storage is not clustered or replicated, any long-term copy you add through remote write is a second recurring bill. Ask any quote to state the assumed series count, scrape interval and retention, and to price a replica or external store separately.
The Collector pipeline adds a capacity line that is easy to undercount. An agent running beside services costs CPU and memory on every node, while a gateway that receives data from many sources needs sizing for peak ingest and for the processors you enable. Authentication through extensions such as OIDC or oauth2client adds configuration and renewal work, and each Collector release line that you track adds upgrade effort. Confirm which Collector distribution and which components your scope uses, because the component stability levels documented in each README affect how much testing and pinning you must budget for. The one-time costs are instrumentation design, dashboard and alert rule creation, and the initial rollout. The recurring costs are upgrades (Prometheus minor releases stop receiving bug fixes after six weeks, so you either track the current minor or move to the LTS line), certificate and credential rotation, and any third-party backend contract. A written quote from DeployOpen or any other consultant should itemize these as separate lines, name who operates each component after handover, and state what is included in upgrades and support.
Moving between Prometheus and OpenTelemetry without losing history
This sequence shows how to add the OpenTelemetry Collector in front of an existing Prometheus estate or move a Prometheus-only setup toward OpenTelemetry-first instrumentation, with clear records of what carries over and what must be rebuilt. Each step ends with a check the team can verify before cutover.
Inventory current metric names, labels and alert rules
The platform team exports the current Prometheus configuration, including scrape targets, recording rules, alerting rules and the metric names and label sets that dashboards and alerts depend on. Owners of each alert and dashboard confirm which series they use. The step is done when the team holds a versioned inventory that lists every series used by an alert or dashboard, and each entry has a named owner.
Deploy the Collector beside existing scrapers
The team installs the OpenTelemetry Collector, starting with a Collector release that matches the supported build they have chosen (the Collector repository is Apache-2.0 and releases ship signed artifacts and SBOMs). In the first phase, the Collector receives a small set of non-critical targets, and Prometheus keeps scraping everything else. The step is done when the Collector's own health metrics show it running, and the exported series for those targets appear in the destination backend.
Map metric names and resource attributes
Engineers compare the names and label sets produced by the Collector's Prometheus receiver or exporter with those in the inventory. They record every difference, such as changed units, dropped or added resource attributes, and label renames, and decide where a relabeling or transform step restores the original names. This mapping is the document that says what history continues and what starts a new series. The step is done when each inventory entry is marked as preserved, renamed with a mapping, or retired.
Run parallel collection and compare results
The team sends the same targets to both pipelines for an agreed observation window, with Prometheus remaining the source for alerts. Engineers compare query results for the key dashboards and alert expressions, and they check the storage side: Prometheus's local storage is single-node, so long-term retention depends on where remote write sends samples. The step is done when the comparison shows matching values for every critical query, and any gaps are documented with a cause.
Rebuild dashboards and recording rules
Dashboards and recording rules are rewritten against the mapped names, because they rarely carry over unchanged. Teams keep the original Prometheus expressions in version control until the new versions match the parallel results. Exporter configuration that is replaced by Collector receivers is retired in the same change so that nothing is scraped twice. The step is done when every dashboard and recording rule has a tested replacement and the old exporter configuration is removed from the change record.
Test alert routing and authentication before cutover
Alert rules are moved only after they fire correctly from the new data path, and Alertmanager routing, grouping and silences are tested with synthetic alerts. The team also reviews how the Collector authenticates incoming and outgoing connections, using the authenticator extensions documented for the Collector where required by their security model. The step is done when test alerts reach the correct receivers and the authentication design has been reviewed and signed off by the security owner.
Cut over and keep a rollback path
The team switches alert ownership and dashboard links to the new pipeline one service group at a time, and keeps the previous Prometheus scrape configuration available until the change is accepted. Each group has a named rollback step and an owner who confirms that the change works. The step is done when all groups are on the new path and the rollback steps have been tested in a non-production environment.
Retire old paths and record what changed
After the agreed observation window passes with no unexplained gaps, the team removes the redundant scrapers and documents the final mapping, including which history remains queryable and where it lives. Teams that need to confirm retention or ingestion limits for the chosen backend should check its documentation before retiring the old store. The step is done when the change record lists every retired component, every renamed series and the retention source for each historical data set.
Where Prometheus vs OpenTelemetry projects usually run long
The schedule risk in this comparison is rarely the software. It is the decisions nobody has made before work starts. Agreeing a metric naming and label policy comes first, because every service team will otherwise name the same measurement differently, and dashboards, recording rules and alerts then break when one team renames a series. Settle the naming convention, the required labels such as service and environment, and who approves exceptions before the first exporter is configured. The same policy decides whether OpenTelemetry instruments emit metrics that Prometheus can consume without rework, so it belongs in the design phase, not the rollout phase.
Label cardinality is the second common slip. Prometheus stores each unique label combination as its own series, so a label carrying user IDs, request paths or container IDs can multiply storage and query cost quickly. Ask each service owner which labels they plan to add, reject unbounded values at review, and test the proposed label set in a non-production environment before it reaches production. Instrumentation across languages adds further variance. Trace APIs are stable in most languages, but log SDK maturity varies and some languages are still in development, so confirm the status of each language your teams use on the OpenTelemetry status page before you commit to logs through OpenTelemetry.
Two more checks belong in the scope. First, review the README of every Collector component in your pipeline, because core components have mixed stability levels and a component that is still in development may change its configuration between releases. Second, plan authentication for receivers and exporters early. The Collector's authenticator extensions can validate incoming connections, for example with OIDC, and add credentials to outgoing requests, but each of these needs identity provider settings, secrets handling and a test pass. The team must supply named service owners, a test environment that mirrors production paths, and an alert owner for every rule, because alerts without an owner are not maintained. No duration should be accepted until the scope is written and agreed, and the written scope should state which languages, components and receivers are included.
Questions buyers ask about Prometheus vs OpenTelemetry
Does OpenTelemetry replace Prometheus?
No. OpenTelemetry is a framework and toolkit for generating, exporting and collecting telemetry, and it is not an observability backend, so storage and visualization are left to separate tools. Prometheus is one of those backends: it pulls metrics over HTTP, stores them in a local time series database and is queried with PromQL. The two overlap when a team uses OpenTelemetry instrumentation for metrics and then exports them to Prometheus, which is a common arrangement. Replacing Prometheus would mean choosing a different backend, and the cost of that choice depends on how many dashboards, alerts and recording rules already depend on PromQL. Confirm the export path and the backend your team will operate before deciding anything.
Does Prometheus handle traces as well as metrics?
Prometheus is built for metrics and alerting, so it does not store distributed traces the way a trace backend does. Teams that need traces, metrics and logs in one pipeline usually pair OpenTelemetry instrumentation with a trace backend of their choosing and keep Prometheus for metrics. The Prometheus overview describes its scope as metrics collection, storage and alerting. Whether a given trace or log backend fits depends on your query needs, retention and the data volume your services produce, so list those requirements before selecting components. Confirm each backend's current capabilities directly with its vendor or documentation.
What does an OpenTelemetry Collector add to a Prometheus setup?
The Collector sits between applications and backends, receiving, processing and exporting telemetry without tying the services to one vendor. It can run as an agent beside each workload or as a gateway that accepts data from many sources, and it can drop, batch, transform or route data before it reaches Prometheus or another store. The Collector repository is Apache-2.0 licensed, and the project publishes signed contrib artifacts, SHA256 checksums and SBOM files with each release. Its components have mixed stability levels, documented per component, so check the status of every receiver and exporter you plan to use. Authenticator extensions can secure incoming connections and outgoing exporter requests, which matters when telemetry crosses network zones.
How much effort does instrumentation take, and what drives it?
Effort depends mostly on how many services exist, which languages they use and how much custom metric naming already exists. Trace APIs and SDKs are stable in most languages, including Go, Java, Python and .NET, while log SDK maturity varies and several languages are still in development, so mixed estates will need more review. Other drivers include the number of exporters, the amount of label cardinality you allow, how many dashboards and alerts must be rewritten, and whether teams need consistent naming conventions across services. Collector configuration also grows with each pipeline. Ask for an inventory of services and languages before estimating, because that inventory determines most of the work.
Can our own team do this work without outside help?
A capable platform team can deploy Prometheus and a Collector, but the work is usually larger than a first pilot suggests. Prometheus local storage is not clustered or replicated, so high availability for alerting, retention planning and remote write to long-term storage need deliberate design. Alertmanager should be run as a cluster with Prometheus pointed at every instance rather than a load balancer. Honest self-assessment should cover on-call capacity, experience with PromQL, Collector configuration security and upgrade routines. Prometheus LTS releases receive bug fixes for one year, so plan upgrades on that cycle. A team with these skills in place can own the work, while a team without them may want outside help for the design phase.
What does a consultant need from our team before starting?
Most engagements are fixed-scope, so the consultant needs written inputs before quoting: a service inventory with languages and frameworks, current monitoring and alerting tools, the number of hosts and clusters, retention and data residency requirements, and the systems that consume metrics today. Access to existing dashboards, alert rules and runbooks helps define what must be preserved. The consultant will also need to know who approves changes, which network zones telemetry crosses and what evidence your auditors or internal reviewers expect. DeployOpen plans, implements and documents self-hosted platforms inside your own infrastructure, and the written scope sets deliverables and timelines. A quote follows from that scope, and DeployOpen does not publish a price list.
How long does a project take, and what sets the timeline?
No duration is fixed by the supplied research, so the honest answer comes from the written scope rather than a general figure. Timelines are set by the number of services to instrument, the number of languages involved, how many backends and exporters the design includes, and how much existing alerting must be migrated and validated. Change approval cycles, change windows in regulated environments and the availability of your staff for reviews also add time. The most reliable way to learn the timeline is to define a pilot scope, agree deliverables in writing and then phase the remaining services. Ask any provider to show the assumptions behind its timeline and what would extend it.
How DeployOpen helps choose and implement the telemetry stack
DeployOpen plans, implements, customizes, integrates, hardens, documents and supports self-hosted open-source platforms inside your own infrastructure, so the Prometheus and OpenTelemetry decision is tested against your estate before anything is built. The work runs as a fixed-scope engagement with scope and deliverables agreed in writing first, and ongoing support is available as a retainer.
How we scope
- Inventory which metrics, traces and logs your teams need and which backends already exist.
- Decide where Prometheus scrapes directly and where an OpenTelemetry Collector sits in the pipeline.
- Agree in writing which receivers, exporters, storage targets and alert routes are in scope.
How we build
- Deploy Prometheus, Alertmanager and any Collector agents or gateways in your cloud accounts or data centers.
- Configure remote write or Collector exporters, with authentication and service discovery set for your environment.
- Test alert routing, metric cardinality and failure behavior before any team depends on the pipeline.
How we hand over
- Document instrumentation guidelines so service teams can add metrics without locking into one backend.
- Provide runbooks for Prometheus local storage limits, Alertmanager clustering and Collector upgrades.
- Walk your operators through configuration files, dashboards and the evidence needed for compliance reviews.
How we support
- Offer ongoing support, upgrades and version tracking for Prometheus and the Collector as a retainer.
- Review pipeline changes and retention settings as your estate grows or your storage plans change.
- Confirm with your team which support window and release track fits your own change control process.
