We construct a matrix $M\in R^{m\otimes d^c}$ with just $m=O(c\,λ\,\varepsilon^{-2}\text{poly}\log1/\varepsilonδ)$ rows, which preserves the norm $\|Mx\|_2=(1\pm\varepsilon)\|x\|_2$ of all $x$ in any given $λ$ dimensional subspace of $ R^d$ with probability at least $1-δ$. This matrix can be applied to tensors $x^{(1)}\otimes\dots\otimes x^{(c)}\in R^{d^c}$ in $O(c\, m \min\{d,m\})$ time -- hence the name "Tensor Sketch". (Here $x\otimes y = \text{asvec}(xy^T) = [x_1y_1, x_1y_2,\dots,x_1y_m,x_2y_1,\dots,x_ny_m]\in R^{nm}$.) This improves upon earlier Tensor Sketch constructions by Pagh and Pham~[TOCT 2013, SIGKDD 2013] and Avron et al.~[NIPS 2014] which require $m=Ω(3^cλ^2δ^{-1})$ rows for the same guarantees. The factors of $λ$, $\varepsilon^{-2}$ and $\log1/δ$ can all be shown to be necessary making our sketch optimal up to log factors. With another construction we get $λ$ times more rows $m=\tilde O(c\,λ^2\,\varepsilon^{-2}(\log1/δ)^3)$, but the matrix can be applied to any vector $x^{(1)}\otimes\dots\otimes x^{(c)}\in R^{d^c}$ in just $\tilde O(c\, (d+m))$ time. This matches the application time of Tensor Sketch while still improving the exponential dependencies in $c$ and $\log1/δ$. Technically, we show two main lemmas: (1) For many Johnson Lindenstrauss (JL) constructions, if $Q,Q'\in R^{m\times d}$ are independent JL matrices, the element-wise product $Qx \circ Q'y$ equals $M(x\otimes y)$ for some $M\in R^{m\times d^2}$ which is itself a JL matrix. (2) If $M^{(i)}\in R^{m\times md}$ are independent JL matrices, then $M^{(1)}(x \otimes (M^{(2)}y \otimes \dots)) = M(x\otimes y\otimes \dots)$ for some $M\in R^{m\times d^c}$ which is itself a JL matrix. Combining these two results give an efficient sketch for tensors of any size.