A SaaS team releases a new version of its backend on Tuesday morning.
The web application is updated at the same time, and everything looks healthy. Tests pass, dashboards remain green and the new functionality works exactly as expected.
Then mobile users begin reporting that their order history no longer loads.
The reason is surprisingly small. The updated backend replaced an old response value, "paid", with a more precise value, "settled". The new web client understands it. An older mobile app still installed on thousands of phones does not.
The backend release itself works. The new client works. What failed was the contract between the new software and something that had not been updated yet.
That is the problem backward compatibility solves.
Backward compatibility means introducing a newer version of software while preserving the ability of existing users, clients, integrations, data or dependent components to continue working as expected.
It matters because software rarely changes all at once. Users may keep an older mobile client for months. Partner integrations may upgrade on their own schedules. Database records created years ago still need to be read. During deployments, old and new service versions can even operate at the same time.
A safe update therefore has to consider more than whether the new version works by itself.
It needs to answer:
What already depends on the behavior we are changing?
For Levelworks, this is primarily a Compatible App Health concern, closely supported by Seamlessly Updatable and Tested. Software becomes easier to maintain when teams can improve it without forcing every consumer to change at exactly the same moment.
The phrase is sometimes used as though it applies only to old versions of an application.
In practice, compatibility boundaries appear in several places.
An update might need to remain compatible with:
Microsoft's current .NET compatibility guidance distinguishes several forms of compatibility, including binary, source, behavioral, backward and forward compatibility. It also notes that a change can compile successfully yet still behave differently at runtime. Microsoft: How code changes can affect compatibility
That distinction is important.
A release can be technically installable and still be incompatible with what users or dependent systems expect.
Breaking compatibility does not require a large architectural rewrite.
Suppose an API originally returns:
{
"customer_id": 1942,
"status": "active"
}
A team decides that status is ambiguous and changes the field to:
{
"customer_id": 1942,
"account_status": "active"
}
The change is understandable from the perspective of the new API.
But every existing consumer expecting status can now fail.
Microsoft classifies modifications to public contracts and externally visible behavior as potential compatibility changes, including alterations that affect existing callers even when internal implementation remains valid. Microsoft: Breaking changes
This is why backward compatibility begins with treating observable behavior as a contract.
Once other software depends on something, renaming, removing or changing it is no longer purely an internal refactor.
One of the safest compatibility principles is simple:
Add before you remove.
Instead of replacing:
{
"status": "active"
}
with:
{
"account_status": "active"
}
a transitional version might return:
{
"status": "active",
"account_status": "active"
}
New clients can begin using account_status while existing clients continue reading status.
Once adoption of the new contract is sufficiently complete, the old field can move through a deliberate deprecation process.
Google's API design guidance describes this philosophy directly: many API improvements can be introduced backward-compatibly by adding properties or entities that old consumers can safely ignore, rather than immediately creating a new incompatible version. Google Cloud: API versioning
The same principle applies beyond APIs.
Instead of changing a database column in one step, add the new representation first. Instead of requiring a newly introduced field immediately, allow an old default during migration. Instead of replacing a message type abruptly, let producers and consumers tolerate both formats during the transition.
Additive change creates room for different parts of the system to move at different speeds.
Compatibility becomes much easier when consumers do not assume they know every field they will ever receive.
Consider a client that receives:
{
"name": "Priya",
"email": "priya@example.com"
}
A later server release adds:
{
"name": "Priya",
"email": "priya@example.com",
"preferred_language": "en"
}
A tolerant old client should simply ignore the unfamiliar field.
If instead the client's parser rejects any response containing unexpected properties, even an additive server change can become breaking.
Protocol Buffers is deliberately designed around this form of evolution. Its documentation explains that new fields can be added using fresh field numbers while old code continues reading newer messages and ignores fields it does not know about. New code can likewise read older messages where the new fields were never present. Protocol Buffers: Updating a message type
That does not mean every application should use Protocol Buffers.
The useful principle is broader: design contracts so reasonable additions do not force every consumer to upgrade immediately.
Adding a field sounds backward-compatible until the server suddenly expects every existing client to send it.
Suppose a checkout request originally contains:
{
"product_id": 482,
"quantity": 2
}
A new version introduces:
{
"product_id": 482,
"quantity": 2,
"delivery_preference": "standard"
}
If delivery_preference is optional and the server applies a sensible default when it is absent, older clients continue working.
If the server begins rejecting every request without the field, those clients break.
A common compatibility strategy is therefore:
The transition period is what protects users.
When software publishes versions, those numbers can communicate how much compatibility consumers should expect.
The official Semantic Versioning 2.0.0 specification defines the familiar MAJOR.MINOR.PATCH convention:
The value is not the numbering itself.
It is the promise attached to it.
If consumers know that 3.6.0 should remain compatible with software using the 3.x public contract, they can make upgrade decisions with more confidence.
Of course, a major version number does not magically make a breaking change safe. It simply communicates that consumers should expect migration work.
Compatibility still has to be managed.
Sometimes a requirement genuinely cannot be introduced without changing the existing contract.
Perhaps:
In that case, explicit versioning can allow old and new consumers to coexist.
Stripe provides a useful real-world model. Its current API documentation distinguishes major releases containing changes that may not be backward-compatible from monthly releases that contain backward-compatible changes. Stripe also allows customers to test a newer API version before making the upgrade permanent. Stripe: API versioning
A simplified migration could look like:
Existing clients ───────→ API v1
│
New clients ─────────────→ API v2
migration period
Remaining clients ───────→ API v2
│
retire v1
Versioning gives consumers time.
It should not become an excuse to maintain every old interface forever. Each supported version creates additional testing and maintenance work, so older contracts eventually need a planned retirement.
[Internal Link: “API Deprecation: What Happens When a Third-Party Service Changes?”]
Application releases often change both code and database structure.
That creates a particular compatibility challenge during deployment because old and new application versions may coexist temporarily.
Imagine the current application expects:
customers.name
The new release replaces it with:
customers.first_name
customers.last_name
If the deployment immediately removes name, any old application instance still serving traffic can fail.
A safer transition usually separates the change into stages.
Add the new structure while preserving the old one.
name
first_name
last_name
Update application code and existing data while ensuring both versions can function during the transition.
Only after old code no longer depends on name should the obsolete representation be removed.
This pattern is often called expand and contract.
The exact implementation depends on the database and architecture, but the compatibility objective remains the same: do not make the old version invalid before you know it is gone.
That is especially important for rolling deployments, where old and new application instances intentionally overlap.
Teams sometimes design compatibility as though deployment happens instantaneously.
Production rarely works that way.
During a rolling update:
Server A → version 7.2
Server B → version 7.2
Server C → version 7.3
Server D → version 7.3
For a period, both versions are operating simultaneously.
That means version 7.3 may write data that version 7.2 later reads.
If the newer release produces something the older release cannot understand, failures appear only during the deployment window—precisely when engineers may already be watching several other signals.
Backward compatibility makes rolling updates substantially safer because the system can tolerate temporary version overlap.
This is one reason Seamlessly Updatable App Health and Compatible App Health reinforce each other.
Server deployments may complete in minutes.
Client software is different.
A web application can usually deliver its new frontend whenever a user reloads the page, although cached assets can still create temporary version overlap.
Mobile applications are far less controllable.
Some users update immediately. Others postpone updates for weeks or months. Organizations using managed devices may have their own release schedules.
If a backend change assumes every mobile client upgraded yesterday, older clients can stop functioning.
Backward-compatible server changes therefore need to reflect the actual client upgrade curve, not the release date of the new app.
Before removing old behavior, use production data to determine whether affected client versions are still active.
Backward compatibility is not only about keeping field names and function signatures unchanged.
Behavior matters too.
Suppose an API historically returns an empty list when no records exist:
[]
A new implementation begins returning:
null
The endpoint name did not change. Neither did the request.
Existing clients may still fail because they assumed an iterable collection.
Microsoft's compatibility guidance explicitly separates behavioral changes from source and binary compatibility. Existing code can continue compiling and loading while behaving differently at runtime.
Other subtle examples include:
When reviewing compatibility, ask not only:
“Can old software still call this?”
but also:
“Will the result still mean what old software expects it to mean?”
A common testing mistake is validating only the new application against the new backend.
That proves the future configuration.
Backward compatibility requires testing the transition configuration as well.
Depending on the system, useful combinations include:
Producer / Client
Consumer / Server
Why test it?
Old
Old
Existing baseline
New
New
Intended future state
Old
New
Backward compatibility
New
Old
Relevant during mixed-version operation
The last combination is not always required. It depends on whether new producers can interact with old consumers during rollout.
But old client → new server is particularly important whenever you cannot force every client to update immediately.
Contract tests can help preserve these expectations automatically. Regression tests should also cover important business behavior so a technically compatible interface does not conceal a behavioral break.
[Internal Link: “Regression Testing: How to Update an App Without Breaking Existing Features”]
When an old interface genuinely needs to disappear, removal should be the end of a process rather than the first announcement users receive.
A responsible deprecation process usually includes:
This is considerably safer than publishing a date and assuming every consumer will migrate.
Deprecation telemetry is particularly valuable. If an old endpoint still handles 18% of production requests a week before retirement, the team has evidence that removal will have consequences.
Compatibility becomes much easier to manage when usage is observable.
Some compatibility-sensitive changes benefit from being deployed before they are activated.
A feature flag can allow the new implementation to exist in production while the old behavior remains active.
Teams can then enable the change for:
If compatibility problems appear, behavior can be switched back without necessarily reverting the entire deployment.
This technique is particularly useful when the breaking risk is behavioral rather than structural.
It does not replace compatible interface design. If a deployment removes a database column that old code still needs, a feature flag cannot magically restore it.
Use flags to control behavior where both paths can genuinely coexist.
Interfaces can often be versioned.
Historical data can be harder.
Suppose version 1 stores:
{
"type": "business",
"tax_id": "..."
}
Version 2 introduces an entirely new representation.
If millions of old records remain in storage, new software still needs a strategy for reading them.
Options can include:
Protocol Buffers provides a useful illustration of designing durable formats: field numbers should not be reused because data written using an older schema may continue to exist long after the application code that produced it has changed. Protocol Buffers language guide
Stored data is another consumer of your software contract.
It cannot install an update.
Compatibility has a cost.
Supporting several old interfaces indefinitely increases:
Sometimes keeping old behavior is also unsafe.
A legacy authentication mechanism may need removal. An insecure protocol might need to be disabled. Incorrect behavior may need correction even though some consumers relied on it.
Backward compatibility should therefore be treated as a migration strategy, not an absolute prohibition against breaking change.
When a breaking change is justified:
The objective is not “never break anything.”
It is never break users accidentally.
A useful compatibility review before a meaningful software update asks:
Question
What you are protecting
Did we remove or rename an existing contract?
Existing clients
Did an optional field become required?
Older requests
Can old clients read new responses?
Client/server compatibility
Can new code read historical data?
Stored-data compatibility
Can old and new versions run together?
Rolling deployments
Did defaults or behavior change?
Behavioral compatibility
Are deprecated consumers still active?
Safe retirement
Have mixed-version combinations been tested?
Transition safety
This review does not need to make every release cumbersome.
Most updates should remain straightforward precisely because incompatible changes are identified early rather than discovered in production.
[Internal Link: “CI/CD for App Maintenance: How Automated Releases Reduce Risk”]
Software has to change.
Security fixes arrive. Product requirements evolve. APIs improve, databases change and old architectural decisions eventually need replacing.
The challenge is that the software around the change rarely moves in perfect synchrony.
Users keep older clients. Integrations upgrade later. Data outlives the code that created it. During deployment, two software versions may temporarily operate side by side.
Backward compatibility gives those moving parts room to transition.
For Levelworks, that makes it central to Compatible App Health and an important enabler of Seamlessly Updatable applications. Tested matters because compatibility is a claim that should be verified against old consumers and important behaviors rather than assumed from the new version working correctly.
The safest update is not necessarily the one that changes the least.
It is the one that clearly understands what existing users and systems depend on, preserves those contracts while the transition is happening, and removes old behavior only when consumers have a safe path forward.
That changes the release question from:
“Does the new version work?”
to the more useful one:
“Can the new version work without making the old expectations our users still rely on suddenly stop working?”
