An $\ell_p$ oblivious subspace embedding is a distribution over $r \times n$ matrices $Π$ such that for any fixed $n \times d$ matrix $A$, $$\Pr_Π[\textrm{for all }x, \ \|Ax\|_p \leq \|ΠAx\|_p \leq κ\|Ax\|_p] \geq 9/10,$$ where $r$ is the dimension of the embedding, $κ$ is the distortion of the embedding, and for an $n$-dimensional vector $y$, $\|y\|_p$ is the $\ell_p$-norm. Another important property is the sparsity of $Π$, that is, the maximum number of non-zero entries per column, as this determines the running time of computing $Π\cdot A$. While for $p = 2$ there are nearly optimal tradeoffs in terms of the dimension, distortion, and sparisty, for the important case of $1 \leq p < 2$, much less was known. In this paper we obtain nearly optimal tradeoffs for $\ell_p$ oblivious subspace embeddings for every $1 \leq p < 2$. We show for every $1 \leq p < 2$, any oblivious subspace embedding with dimension $r$ has distortion $κ= Ω\left(\frac{1}{\left(\frac{1}{d}\right)^{1 / p} \cdot \log^{2 / p}r + \left(\frac{r}{n}\right)^{1 / p - 1 / 2}}\right).$ When $r = \mathrm{poly}(d)$ in applications, this gives a $κ= Ω(d^{1/p}\log^{-2/p} d)$ lower bound, and shows the oblivious subspace embedding of Sohler and Woodruff (STOC, 2011) for $p = 1$ and the oblivious subspace embedding of Meng and Mahoney (STOC, 2013) for $1 < p < 2$ are optimal up to $\mathrm{poly}(\log(d))$ factors. We also give sparse oblivious subspace embeddings for every $1 \leq p < 2$ which are optimal in dimension and distortion, up to $\mathrm{poly}(\log d)$ factors. Oblivious subspace embeddings are crucial for distributed and streaming environments, as well as entrywise $\ell_p$ low rank approximation. Our results give improved algorithms for these applications.