Building an Endless Runner in Unity: A Developer's Technical Breakdown

Endless runners look deceptively simple from the outside. Swipe, jump, slide, collect coins, repeat. But if you've ever tried to actually implement one — procedural track generation, object pooling, collision handling, input responsiveness, and performance tuning for low-end Android devices all at once — you know the genre has more engineering depth than it lets on.

This article is written for developers, not marketers. We're going to walk through the actual systems that make an endless runner work in Unity: the architecture decisions, the common pitfalls, and the performance considerations that separate a smooth 60fps runner from a janky prototype that drops frames the moment three obstacles spawn at once.

Why Endless Runners Are a Good Engineering Exercise

Before diving into implementation, it's worth understanding why this genre is such a common starting point for Unity developers — and why so many teams choose to build on top of an existing source code rather than starting from a blank scene.

The core systems in an endless runner are reusable across dozens of other genres: object pooling, procedural spawning, state machines for player movement, and performance-conscious rendering. Once you understand how to build these systems well in a runner, you can carry that architecture into platformers, obstacle-course games, and even some puzzle formats.

A well-built reference implementation is genuinely useful here. The roundup of runner templates on the Unity Source Code blog is a good place to see how different teams have approached the same core loop — comparing tile-based level design against fully procedural generation, for example, or seeing how a chase-and-combat hybrid like Last War: Survival Battle layers combat systems on top of the standard runner architecture. Studying multiple implementations side by side is one of the fastest ways to understand which architectural decisions actually matter versus which ones are just stylistic preference.

Core System 1: Procedural Track Generation

The heart of any endless runner is the system that generates the world in front of the player indefinitely without eating memory or tanking performance. There are two common approaches:

Segment-based generation. The world is broken into fixed-length "chunks" (say, 20 units long) that are pre-authored or randomly assembled from a pool of prefab segments. As the player moves forward, new segments spawn ahead and old ones get recycled behind.

public class TrackSpawner : MonoBehaviour
{
    public GameObject[] trackSegments;
    public Transform player;
    public float segmentLength = 20f;
    public int segmentsAhead = 4;

    private Queue<GameObject> activeSegments = new Queue<GameObject>();
    private float nextSpawnZ = 0f;

    void Start()
    {
        for (int i = 0; i < segmentsAhead; i++)
        {
            SpawnSegment();
        }
    }

    void Update()
    {
        if (player.position.z > nextSpawnZ - (segmentsAhead * segmentLength))
        {
            SpawnSegment();
            RecycleOldSegment();
        }
    }

    void SpawnSegment()
    {
        GameObject prefab = trackSegments[Random.Range(0, trackSegments.Length)];
        GameObject segment = Instantiate(prefab, new Vector3(0, 0, nextSpawnZ), Quaternion.identity);
        activeSegments.Enqueue(segment);
        nextSpawnZ += segmentLength;
    }

    void RecycleOldSegment()
    {
        if (activeSegments.Count > segmentsAhead)
        {
            GameObject old = activeSegments.Dequeue();
            Destroy(old);
        }
    }
}

This approach is easier to control from a design perspective — you can hand-author interesting segment layouts and control difficulty precisely by weighting which segments are more likely to spawn as the game progresses.

Fully procedural obstacle placement. Instead of pre-built segments, obstacles and collectibles are spawned individually based on rules (minimum spacing, lane distribution, difficulty curve). This gives more variety but requires more careful tuning to avoid impossible or unfair obstacle combinations — like two full-width obstacles spawning too close together for the player to react.

Most production runners use a hybrid: segment-based track geometry (ground, ramps, turns) combined with procedural obstacle and collectible placement within each segment. This gives you the best of both — predictable level structure with unpredictable moment-to-moment gameplay.

Core System 2: Object Pooling (Non-Negotiable)

If there's one performance mistake that kills more endless runner prototypes than anything else, it's calling Instantiate() and Destroy() repeatedly during gameplay. Garbage collection spikes from constant allocation/deallocation are one of the leading causes of frame stutter in this genre, especially on mid- and low-tier Android devices.

Object pooling solves this by pre-allocating a pool of reusable objects and simply enabling/disabling them instead of creating and destroying them:

public class ObjectPool : MonoBehaviour
{
    public GameObject prefab;
    public int poolSize = 20;
    private Queue<GameObject> pool = new Queue<GameObject>();

    void Awake()
    {
        for (int i = 0; i < poolSize; i++)
        {
            GameObject obj = Instantiate(prefab);
            obj.SetActive(false);
            pool.Enqueue(obj);
        }
    }

    public GameObject GetObject()
    {
        if (pool.Count == 0)
        {
            GameObject extra = Instantiate(prefab);
            return extra;
        }
        GameObject obj = pool.Dequeue();
        obj.SetActive(true);
        return obj;
    }

    public void ReturnObject(GameObject obj)
    {
        obj.SetActive(false);
        pool.Enqueue(obj);
    }
}

Every obstacle, coin, power-up, and particle effect in your runner should go through a pooling system like this. It's the single highest-leverage optimization you can make early in development, and retrofitting it into a codebase that wasn't built with pooling in mind is significantly more painful than building it in from day one.

Core System 3: Input Handling and Responsiveness

Endless runners live or die on input responsiveness. A 100ms delay between a swipe and the character's lane change is enough to feel "off" to players, even if they can't articulate why.

A few practical guidelines:

• Detect swipes on touch-start delta, not touch-end. Waiting for a full swipe gesture to complete before registering input adds unnecessary latency. Many production runners start evaluating swipe direction as soon as the touch has moved a minimum threshold distance.
• Use a dedicated input buffer for jump/slide actions. Registering input a few frames before the player is technically able to act on it (and queuing that action) makes controls feel more forgiving without actually changing the underlying timing window.
• Decouple input polling from physics updates. Poll input in Update(), but apply movement in FixedUpdate() using cached input state, so your control responsiveness isn't tied to physics tick rate.

private Vector2 touchStartPos;
private bool swipeDetected = false;

void Update()
{
    if (Input.touchCount > 0)
    {
        Touch touch = Input.GetTouch(0);
        if (touch.phase == TouchPhase.Began)
        {
            touchStartPos = touch.position;
            swipeDetected = false;
        }
        else if (touch.phase == TouchPhase.Moved && !swipeDetected)
        {
            Vector2 delta = touch.position - touchStartPos;
            if (delta.magnitude > 50f) // minimum swipe threshold
            {
                HandleSwipe(delta);
                swipeDetected = true;
            }
        }
    }
}

Core System 4: Difficulty Scaling

A static difficulty curve is one of the fastest ways to make a runner feel stale. Most production-grade runners scale difficulty dynamically based on distance traveled or elapsed time, adjusting:

• Forward movement speed
• Obstacle spawn frequency and density
• Minimum gap between obstacles (which should never shrink below what's physically possible to react to)
• Power-up spawn rate (often inversely related to difficulty, to give players relief valves during harder sections)

A simple linear or logarithmic curve tied to distance traveled works well as a baseline:

float GetCurrentSpeed(float distanceTraveled)
{
    float baseSpeed = 8f;
    float maxSpeed = 20f;
    float rampDistance = 2000f;
    float t = Mathf.Clamp01(distanceTraveled / rampDistance);
    return Mathf.Lerp(baseSpeed, maxSpeed, t);
}

The important engineering detail here is capping the maximum speed. Uncapped scaling eventually produces obstacle configurations that are mathematically impossible to react to at the player's input speed, which is a common (and easily avoidable) bug in amateur runner implementations.

Performance Optimization for Low-End Android

This is where a lot of otherwise well-built runners fall apart in production. Endless runners are particularly punishing on frame rate because of constant camera movement, particle effects, and procedural spawning — all happening simultaneously, every frame, for as long as the player keeps the app open.

Key optimization targets:

• Draw call reduction. Batch static geometry where possible, and use texture atlases for obstacle and environment sprites to minimize material swaps.
• Particle system budgets. Cap simultaneous particle emitters and use simplified particle effects on detected low-end devices via SystemInfo.systemMemorySize or a device-tier detection library.
• LOD and culling. Even in a runner where the camera mostly faces forward, aggressive frustum culling on track segments behind the player prevents wasted rendering.
• Physics simplification. Use simple colliders (box/capsule) over mesh colliders wherever possible, and avoid running full physics simulation on objects that only need trigger-based collision detection.

Because this genre is so sensitive to frame rate consistency across the fragmented Android hardware landscape, it's worth budgeting real testing time on actual low-end devices rather than relying on editor performance or high-end test phones, which will mask problems that only show up on budget hardware in the field.

Publishing Considerations: Platform-Specific Build Configuration

Once the core systems are built and optimized, the next engineering hurdle is platform-specific build configuration — and this is a step where a lot of developers underestimate the differences between Android and iOS.

Build settings, signing certificates, IL2CPP vs Mono scripting backends, texture compression formats (ASTC vs ETC2 vs PVRTC), and store-specific review requirements around ads and permissions all differ meaningfully between the two platforms. Getting these wrong doesn't just cause build errors — it can lead to store rejections that cost a full review cycle to fix.

For a detailed technical breakdown of these platform differences — including build configuration specifics and submission requirements — see this developer-focused walkthrough of Unity Android vs iOS publishing. It's worth reading before you lock in your build pipeline, since some of these settings (like scripting backend and API compatibility level) are much easier to configure correctly up front than to retrofit after a submission gets flagged.

Testing and Iteration Workflow

A practical testing workflow for a runner in active development typically includes:

1. Editor playtesting for rapid iteration on core mechanics and feel.
2. Device farm testing across a range of Android hardware tiers to catch performance regressions early.
3. Automated frame-rate logging during build QA, comparing average and minimum FPS across representative test devices.
4. Playtester feedback sessions focused specifically on difficulty pacing — this is subjective enough that it benefits from real human input rather than pure metrics.
5. Soft launch analytics once the build is live, tracking session length, retry rate, and drop-off points along the difficulty curve.

Treating difficulty tuning as an ongoing, data-informed process rather than a one-time decision before launch tends to produce noticeably better retention outcomes over the game's lifecycle.

Closing Thoughts

Endless runners reward strong engineering fundamentals more than most casual mobile genres. The mechanics are simple to describe but genuinely demanding to implement well — procedural generation that stays fair, object pooling that eliminates GC spikes, input handling tight enough to feel instant, and difficulty scaling that stays challenging without becoming impossible.

If you're starting a runner project, it's worth studying existing production implementations before writing your own systems from scratch. Comparing different architectural approaches — like the range of templates covered in the endless runner source code roundup — can save significant development time by showing you which patterns are proven versus which are still experimental.

The genre isn't going anywhere. Its technical demands are well understood, its player expectations are well established, and its engineering challenges — performance, responsiveness, and fair difficulty scaling — are exactly the kind of problems that make you a better Unity developer once you've solved them properly.