Vector Dot Product Calculator (2D / 3D)
Enter two 2D (⟨x, y⟩) or 3D (⟨x, y, z⟩) vectors and the tool returns the dot product a · b, |a|, |b|, cos θ, the included angle θ in degrees, and the scalar and vector projections of A onto B. It also classifies the relationship — orthogonal, parallel, anti-parallel, acute or obtuse. This is the workhorse used in physics (W = F · d for work), computer graphics (lighting, normals), machine learning (cosine similarity), and almost every linear-algebra homework set.
Fill in every component of both vectors.
Dot product a · b
10
⟨3,4⟩ · ⟨2,1⟩ = 10
—
cos θ
0.8944
Included angle θ
26.57°
|A|
5
|B|
2.236
Scalar projection of A onto B
—
Vector projection of A onto B
—
Formula: a · b = Σ aᵢ·bᵢ = |a|·|b|·cos θ. Used in physics (work W = F · d), computer-graphics lighting, machine-learning cosine similarity, document similarity scoring and more. The URL stores d, ax, ay, az, bx, by, bz so you can bookmark a specific pair.
Formula
a · b = Σᵢ aᵢ·bᵢ (component form) = |a|·|b|·cos θ (geometric form) cos θ = a · b / (|a|·|b|) (angle between) scalar_proj(a on b) = a · b / |b| (scalar projection) vector_proj(a on b) = (a · b / |b|²) · b (vector projection)
- · The dot product is a scalar (single number), not a vector. If you need a vector result (e.g. an axis of rotation) use the cross product (3D only) instead.
- · Two non-zero vectors are orthogonal (perpendicular) ⇔ a · b = 0 — the fastest perpendicularity check, much quicker than working out slopes by hand.
- · cos θ is clamped to [−1, 1] before passing into acos() so floating-point overshoot (e.g. 1.0000000002) cannot produce NaN for nearly-parallel inputs.
- · Physics: with F and d both vectors and F constant, work W = F · d. When F is perpendicular to d (e.g. gravity acting on a horizontally sliding object), the work done is zero.
- · Machine learning: dividing the dot product by (|a|·|b|) gives cosine similarity in [−1, 1] — the standard metric for comparing documents, embeddings or recommendation candidates.
- · Geometrically, |scalar projection| is the length of the shadow A casts onto the line of B; a negative scalar projection means the shadow points opposite to B.
- · Definitions follow Stewart Calculus §12.3 and Anton & Rorres Linear Algebra §3.2.
- · The URL stores d (dimension), ax/ay/az and bx/by/bz so you can bookmark or share a specific pair.
Frequently asked
How is the dot product different from the cross product?
The dot product (a · b) returns a scalar (single number) and measures how close two vectors are to being parallel — covering cos θ, projection lengths and work W = F · d; works in 2D, 3D and any dimension. The cross product (a × b) exists only in 3D (and 7D) and returns a vector whose direction follows the right-hand rule and whose magnitude equals |a|·|b|·sin θ — it gives the normal to the plane spanned by a and b, useful for torque τ = r × F, the area of a parallelogram (= |a × b|) and 3D rotation axes. Rule of thumb: dot for projection, cross for area / normal.
Why is the angle always between 0° and 180°, never negative?
Because acos() is single-valued on [0, π] (0°–180°), where cos θ is monotonic and uniquely recovers θ. If you want a signed (e.g. counter-clockwise) angle you need atan2(y, x) instead, or to introduce a "signed angle" concept — but that only makes sense in 2D, where you can fix a positive orientation of the plane. In 3D the natural definition of "angle between two vectors" is the smaller angle between the two direction lines, which is bounded by 180° and is unsigned.
What happens if one of the inputs is the zero vector?
The dot product itself is still defined (it equals 0), but the angle θ, cos θ and both projections become undefined — the zero vector has no direction, so there is no meaningful "angle to" anything or "line to project onto". The tool shows "undefined" in those fields and flags that at least one input is the zero vector. If you need these metrics (e.g. cosine similarity), make sure neither input is zero first; an unexpectedly all-zero feature vector usually indicates a preprocessing bug (e.g. subtracting the mean then normalising left everything at zero).
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