OpenTelemetry vs. Prometheus: You can’t fix what you can’t see

Monitoring
and
optimizing
application
performance
is
important
for
software
developers
and
enterprises
at
large.
The
more
applications
that
an
enterprise
deploys,
the
more
data
that
exists
for
collecting
and
analyzing.
Yet,
this
data
isn’t
worth
much
without
the
right
tools
for
monitoring,
optimizing,
storing
and—crucially—putting
the
data
into
context.

Organizations
can
make
the
most
of
application
data
by
deploying
monitoring
and

observability
solutions
that
help
improve
application
health
by
identifying
issues
before
they
arise,
flagging
bottlenecks,
distributing
network
traffic
and
more.
These
features
help
reduce
application
downtime,
provide
more
reliable
application
performance
and
improve
user
experience.

OpenTelemetry
and
Prometheus
are
both
open-source
projects
under
the
Cloud
Native
Computing
Foundation
(CNCF)
that
offer
observability
tools
for
application
monitoring.
Different
types
of
data
and
operations
require
distinct
solutions
that
depend
on
an
organization’s
goals
and
application
specifications.
Understanding
the
key
differences
between
platforms
like
OpenTelemetry
and
Prometheus
and
what
each
solution
offers,
is
important
before
you
choose
one
for
implementation.

It
is
also
valuable
to
note
that
OpenTelemetry
and
Prometheus
integrate
and
can
work
together
as
a
powerful
duo
for
monitoring
applications.
OpenTelemetry
and
Prometheus
enable
the
collection
and
transformation
of
metrics,
which
allows
DevOps
and
IT
teams
to
generate
and
act
on
performance
insights.

What
is
OpenTelemetry?

OpenTelemetry
or
OTel,
is
a
platform
that
is
designed
to
create
a
centralized
location
for
generating,
collecting,
exporting
and
managing
telemetry
data,
including
logs,
metrics
and
traces.
OTel
was
born
from
the
merger
of
OpenCensus
and
OpenTracing
with
the
goal
of
providing
APIs,
SDKs,
libraries
and
integrations
that
standardize
the
collection
of
disparate
data.
With
OTel,
the
wanted
monitoring
outputs
can
be
built
into
your
code
to
simplify
data
processing
and
make
sure
that
data
is
exported
to
the
appropriate
back
end.

Analyzing
telemetry
data
is
key
in
understanding
system
performance
and
health.
This
type
of
optimized
observability
allows
organizations
to
troubleshoot
faster,
increase
system
reliability,
address
latency
issues
and
reduce
application
downtime.

Here’s
a
quick
break
down
the
key
aspects
of
the
OpenTelemetry
ecosystem:

APIs:
OpenTelemetry
APIs
(application
programming
interfaces)
universally
translate
programming
languages.
This
capability
enables
the
APIs
to
collect
telemetry
data.
These
APIs
play
a
key
role
in
standardizing
the
collection
of
OpenTelemetry
metrics.

SDKs:
Software
development
kits
are
tools
for
building
software.
They
include
the
framework,
code
libraries
and
debuggers
that
are
the
building
blocks
of
software
development.
OTel
SDKs
implement
OpenTelemetry
APIs
and
offer
the
tools
that
are
needed
to
generate
and
collect
telemetry
data.

OpenTelemetry
collector:
The
OTel
collector
receives,
processes
and
exports,
telemetry
data.
OTel
collectors
can
be
configured
to
filter
specific
data
types
to
the
designated
back
end.  

Instrumentation
library:
OTel
provides
an
instrumentation
model
that
runs
on
all
platforms.
The
instrumentation
libraries
make
it
possible
for
OTel
to
integrate
with
any
programming
language.

Benefits
of
OpenTelemetry

The
OpenTelemetry
protocol
(OTLP)
simplifies
observability
by
collecting
telemetry
data,
like
metrics,
logs
and
traces,
without
changing
code
or
metadata.

Metrics:
Metrics
define
a
high-level
overview
of
system
performance
and
health.
Developers,
IT
and
business
management
teams
determine
what
metrics
are
most
useful
to
track
to
maintain
a
level
of
application
performance
that
meets
business
objectives.
Metrics
vary
depending
on
the
data
that
a
team
deems
important
and
can
include
network
traffic,
latency
and
CPU
storage.
Metrics
can
also
be
used
to
track
patterns
and
trends
in
application
performance.

Logs:
Logs
are
a
record
of
events
that
occur
within
a
software
or
application
component.
Logs
can
be
created
around
specific
aspects
of
a
component
that

DevOps
teams
want
to
monitor.
They
serve
as
historical
data
that
can
present
general
performance
information,
show
when
set
thresholds
are
surpassed,
or
display
errors.
Logs
help
monitor
the
overall
health
of
an
application
ecosystem.

Traces:
Traces
offer
a
more
zoomed
out
view
of
application
performance
than
logs
and
help
with
optimization.
They
are
also
more
focused
than
logs
and
follow
the
end-to-end
journey
of
a
single
request
as
it
moves
through
the
application
stack.
Traces
allow
developers
to
find
the
exact
moment
errors
or
bottlenecks
occur,
how
long
they
last
and
how
they
affect
the
user
journey.
This
information
helps
manage
microservices
and
improve
overall
application
performance.

OTel
can
take
these
three
different
types
of
telemetry
data
and
export
them
to
various
back
ends,
including
Prometheus.
This
capability
prevents
vendor
or
back-end
lock-in
and
allows
developers
to
choose
their
preferred
analysis
tools.
OpenTelemetry
supports
a
range
of
integrations
with
other
platforms,
including
Prometheus,
which
provide
greater
opportunities
for
observability.
OTel
supports
Java,
Python,
JavaScript
and
Go,
making
it
an
increasingly
flexible
solution.
It
also
allows
developers
and
IT
teams
to
monitor
performance
from
any
web
browser
or
location.

The
greatest
strengths
of
OpenTelemetry
come
from
its
ability
to
consistently
collect
and
export
data
across
many
applications
and
its
standardization
of
the
collection
process.
OTel
is
a
powerful
tool
for
observability
into
distributed
systems
and
microservices.

What
is
Prometheus?

Prometheus
is
a
toolkit
for
monitoring
and
alerting
that
was
created
to
collect
and
organize
application
metrics.
The
Prometheus
server
was
originally
developed
at
SoundCloud
before
it
became
an
open-source
tool.

Prometheus
is
a
time-series
database
for
end-to-end
monitoring
of
time-series
data.
Time-series
metrics
are
a
collection
of
data
that
is
taken
at
regular
intervals
such
as
monthly
sales
data,
or
daily
application
traffic.
Clear
visibility
into
this
type
of
data
offers
insights
into
patterns,
trends
and
predictions
for
business
planning.
Once
integrated
with
a
host,
Prometheus
gathers
application
metrics
that
are
related
to
dedicated
functions
that
DevOps
teams
want
to
monitor.

Prometheus
metrics
provide
data
points
that
consist
of
the
metric
name,
label,
timestamp
and
value
by
using
a
query
language
called
PromQL.
PromQL
allows
developers
and
IT
departments
to
aggregate
data
metrics
and
turn
them
into
histograms–graphs
and
dashboards
for
greater
visualization.
Prometheus
can
access
data
from
enterprise
databases
or
from
exporters.
Exporters
are
software
that
is
related
to
applications
that
work
to
pull
metrics
from
various
apps
and
endpoints.

Prometheus
collects
four
types
of
metrics:

Counters:
Countersmeasure
cumulative
numerical
values
that
only
increase.
Counters
are
used
to
measure
completed
tasks,
the
number
of
errors
that
occurred
during
a
defined
period,
or
the
number
of
running
processes
or
microservices.

Gauges:
Gauges
monitor
numerical
values
that
rise
and
fall
depending
on
external
factors.
They
can
monitor
CPU
and
memory
usage,
temperature,
or
the
size
of
a
queue.

Histograms:
Histograms
measure
the
duration
of
specified
events
such
as
request
duration
or
response
size.
They
then
divide
the
range
of
these
measurements
into
intervals
that
are
called
buckets
and
determine
how
many
of
these
measurements
fall
into
each
respective
bucket.

Summaries:
Like
histograms,
summaries
also
measure
request
durations
and
response
size,
but
also
provide
a
total
count
of
all
observations
and
a
total
of
all
observed
values.

Another
valuable
aspect
of
Prometheus
is
that
it
can
create
accessible
dashboards
and
graphs
based
on
the
collected
data.

Benefits
of
Prometheus

Prometheus
enables
real-time
application
monitoring
that
gives
you
accurate
insights
and
facilitates
quick
troubleshooting.
It
also
allows
for
the
creation
of
thresholds
that
are
related
to
specific
functions.
When
these
thresholds
are
met
or
surpassed,
it
triggers
alerts
that
can
reduce
the
time
that
it
takes
to
resolve
issues.
Prometheus
can
handle
and
store
large
volumes
of
metrics
data
and
make
the
data
available
for
analytics
teams
as
needed.
It
is
not
intended
to
be
a
long-term
storage
solution
but
a
tool
for
storing
data
that
is
needed
for
immediate
analysis.
The
standard
window
for
data
storage
with
Prometheus
is
between
two
hours
and
fifteen
days.

Prometheus
seamlessly
integrates
with

Kubernetes,
an
open-source
container
orchestration
platform
for
scheduling
and
automating
the
deployment,
management
and
scaling
of
containerized
applications.
Kubernetes
allows
enterprises
to
build
complex
hybrid
and
multicloud
environments
that
deploy
a
range
of
services
and
microservices.
Integrating
Prometheus
with
Kubernetes
brings
full-stack
observability
and
oversight
into
these
complex
systems.

Prometheus
is
also
compatible
with
Grafana,
a
powerful
visualization
tool
that
helps
transform
data
into
dashboards,
charts,
graphs
and
alerts.
When
paired
with
Prometheus,
Grafana
can
take
metrics
and
create
clear
visualizations.
The
compatibility
between
these
two
platforms
makes
complex
data
more
accessible
and
sharable
among
different
teams. 

Key
differences
between
OpenTelemetry
and
Prometheus

Prometheus
offers
tools
for
metrics
monitoring,
storage
and
visualization,
but
does
not
track
logs
or
support
traces,
which
are
used
for
root
cause
analysis.
Overall,
Prometheus
has
more
limited
use
cases
than
OpenTelemetry.

OpenTelemetry
can
process
and
trace
more
complex
metrics
than
Prometheus
through
programming
language-agnostic
integrations.
OTel
is
highly
scalable
and
has
greater
extensibility
than
Prometheus
by
offering
automated
instrumentation
models.
Unlike
Prometheus,
OpenTelemetry
does
not
offer
a
storage
solution
and
must
be
paired
with
a
separate
back-end
system.

A
quick
breakdown:

• Prometheus
  can
  measure
  cumulative
  metrics,
  giving
  you
  a
  sum,
  while
  OpenTelemetry
  can
  represent
  metrics
  as
  deltas.
• Prometheus
  provides
  short-term
  data
  and
  metrics
  storage
  while
  OTel
  does
  not
  natively
  support
  storage
  but
  can
  be
  paired
  with
  a
  separate
  storage
  solution.
• OpenTelemetry
  collects
  metrics,
  logs
  and
  traces
  by
  using
  a
  consolidated
  API
  via
  push
  or
  pull,
  and
  translates
  them
  into
  a
  common
  language,
  which
  Prometheus
  cannot
  achieve.
  Prometheus
  gathers
  metrics
  by
  pulling
  data
  from
  hosts
  and
  is
  primarily
  concerned
  with
  collecting
  and
  storing
  time-series
  metrics.
• OTel
  is
  language
  agonistic
  and
  can
  translate
  metrics,
  giving
  developers
  more
  flexibility.
  Prometheus
  uses
  PromQL
  to
  aggregate
  data
  and
  metrics.
• Prometheus
  provides
  web
  visualization
  for
  monitoring
  metrics
  coupled
  with
  customizable
  alerts.
  OpenTelemetry
  must
  be
  integrated
  with
  separate
  tools
  for
  visualization.
• OTel
  allows
  metric
  values
  to
  be
  expressed
  as
  integers
  rather
  than
  floating-point
  numbers,
  which
  provide
  more
  accurate
  value
  representations
  and
  are
  easier
  to
  understand.
  Prometheus
  cannot
  express
  metrics
  as
  integers.

Your
organization’s
needs
will
dictate
which
of
these
solutions
is
right
for
you.
If
you
need
a
more
holistic
understanding
of
your
data,
are
working
in
complex
environments
with
distributed
systems,
and
want
more
flexibility,
OpenTelemetry
might
be
a
more
appropriate
solution.
This
is
also
the
case
if
you
need
to
monitor
logs
and
traces.

If
you
need
to
monitor
individual
systems
or
operations,
and
are
looking
for
alerting,
storage
and
visualization
models,
Prometheus
might
be
the
right
option.

OpenTelemetry
and
Prometheus
integration

The
good
news
is
that
you
don’t
necessarily
have
to
choose
one
or
the
other;
OpenTelemetry
and
Prometheus
are
compatible
platforms.
OTel
SDKs
can
collect
metrics
from
Prometheus
data
models
and
Prometheus
supports
OpenTelemetry
metrics.
Using
these
platforms
together
gives
you
the
best
of
both
worlds
and
advanced
monitoring
options.
For
example:

• When
  coupled,
  OTel
  and
  Prometheus
  provide
  monitoring
  into
  complex
  systems
  with
  real-time
  insights
  into
  your
  application
  environments.
• You
  can
  pair
  OTel’s
  tracing
  and
  monitoring
  tools
  with
  Prometheus’
  alerting
  capabilities.
• Prometheus
  can
  handle
  large
  volumes
  of
  data.
  This
  feature
  coupled
  with
  OTel’s
  ability
  to
  consolidate
  metrics,
  traces
  and
  logs
  into
  a
  single
  interface
  creates
  greater
  efficiency
  when
  scaling
  systems
  and
  applications.
• PromQL
  can
  analyze
  the
  data
  that
  is
  collected
  from
  OpenTelemetry’s
  data
  captures
  and
  use
  it
  to
  create
  visualization
  models.

In
addition,
OpenTelemetry
and
Prometheus
integrate
with

IBM®
Instana
and

IBM®
Turbonomic
to
offer
additional
monitoring
tools.
With
Instana’s
powerful
dependency
map,
upstream/downstream
service
correlation
and
full-stack
visibility,
OTel’s
capabilities
are
optimized
to
make
sure
that
all
services
are
instrumented.
Instana
delivers
the
same
great
experience
with
OTel
data
as
it
provides
for
every
other
data
source,
giving
you
the
context
that
you
need
to
quickly
find
and
fix
application
issues.
With
Turbonomic,
you
can
use
Prometheus’
data
monitoring
tools
to
automate
resourcing
decisions
based
on
real-time
data
collection.
These
integrations
are
optimized
ways
to
promote
the
health
of
your
application
ecosystem
and
improve
overall
performance.

Explore
IBM
Instana
OpenTelemetry

Explore
Prometheus
integration
with
IBM
Turbonomic